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Sample records for automated fault extraction

  1. Automated Fault Interpretation and Extraction using Improved Supplementary Seismic Datasets

    Science.gov (United States)

    Bollmann, T. A.; Shank, R.

    2017-12-01

    During the interpretation of seismic volumes, it is necessary to interpret faults along with horizons of interest. With the improvement of technology, the interpretation of faults can be expedited with the aid of different algorithms that create supplementary seismic attributes, such as semblance and coherency. These products highlight discontinuities, but still need a large amount of human interaction to interpret faults and are plagued by noise and stratigraphic discontinuities. Hale (2013) presents a method to improve on these datasets by creating what is referred to as a Fault Likelihood volume. In general, these volumes contain less noise and do not emphasize stratigraphic features. Instead, planar features within a specified strike and dip range are highlighted. Once a satisfactory Fault Likelihood Volume is created, extraction of fault surfaces is much easier. The extracted fault surfaces are then exported to interpretation software for QC. Numerous software packages have implemented this methodology with varying results. After investigating these platforms, we developed a preferred Automated Fault Interpretation workflow.

  2. Automated fault extraction and classification using 3-D seismic data for the Ekofisk field development

    Energy Technology Data Exchange (ETDEWEB)

    Signer, C.; Nickel, M.; Randen, T.; Saeter, T.; Soenneland, H.H.

    1998-12-31

    Mapping of fractures is important for the prediction of fluid flow in many reservoir types. The fluid flow depends mainly on the efficiency of the reservoir seals. Improved spatial mapping of the open and closed fracture systems will allow a better prediction of the fluid flow pattern. The primary objectives of this paper is to present fracture characterization at the reservoir scale combined with seismic facies mapping. The complexity of the giant Ekofisk field on the Norwegian continental shelf provides an ideal framework for testing the validity and the applicability of an automated seismic fault and fracture detection and mapping tool. The mapping of the faults can be based on seismic attribute grids, which means that attribute-responses related to faults are extracted along key horizons which were interpreted in the reservoir interval. 3 refs., 3 figs.

  3. Automated fault-management in a simulated spaceflight micro-world

    Science.gov (United States)

    Lorenz, Bernd; Di Nocera, Francesco; Rottger, Stefan; Parasuraman, Raja

    2002-01-01

    BACKGROUND: As human spaceflight missions extend in duration and distance from Earth, a self-sufficient crew will bear far greater onboard responsibility and authority for mission success. This will increase the need for automated fault management (FM). Human factors issues in the use of such systems include maintenance of cognitive skill, situational awareness (SA), trust in automation, and workload. This study examine the human performance consequences of operator use of intelligent FM support in interaction with an autonomous, space-related, atmospheric control system. METHODS: An expert system representing a model-base reasoning agent supported operators at a low level of automation (LOA) by a computerized fault finding guide, at a medium LOA by an automated diagnosis and recovery advisory, and at a high LOA by automate diagnosis and recovery implementation, subject to operator approval or veto. Ten percent of the experimental trials involved complete failure of FM support. RESULTS: Benefits of automation were reflected in more accurate diagnoses, shorter fault identification time, and reduced subjective operator workload. Unexpectedly, fault identification times deteriorated more at the medium than at the high LOA during automation failure. Analyses of information sampling behavior showed that offloading operators from recovery implementation during reliable automation enabled operators at high LOA to engage in fault assessment activities CONCLUSIONS: The potential threat to SA imposed by high-level automation, in which decision advisories are automatically generated, need not inevitably be counteracted by choosing a lower LOA. Instead, freeing operator cognitive resources by automatic implementation of recover plans at a higher LOA can promote better fault comprehension, so long as the automation interface is designed to support efficient information sampling.

  4. A data-driven multiplicative fault diagnosis approach for automation processes.

    Science.gov (United States)

    Hao, Haiyang; Zhang, Kai; Ding, Steven X; Chen, Zhiwen; Lei, Yaguo

    2014-09-01

    This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented. Copyright © 2013. Published by Elsevier Ltd.

  5. Safety assessment of automated vehicle functions by simulation-based fault injection

    OpenAIRE

    Juez, Garazi; Amparan, Estibaliz; Lattarulo, Ray; Rastelli, Joshue Perez; Ruiz, Alejandra; Espinoza, Huascar

    2017-01-01

    As automated driving vehicles become more sophisticated and pervasive, it is increasingly important to assure its safety even in the presence of faults. This paper presents a simulation-based fault injection approach (Sabotage) aimed at assessing the safety of automated vehicle functions. In particular, we focus on a case study to forecast fault effects during the model-based design of a lateral control function. The goal is to determine the acceptable fault detection interval for pe...

  6. Alternative validation practice of an automated faulting measurement method.

    Science.gov (United States)

    2010-03-08

    A number of states have adopted profiler based systems to automatically measure faulting, : in jointed concrete pavements. However, little published work exists which documents the : validation process used for such automated faulting systems. This p...

  7. Operations management system advanced automation: Fault detection isolation and recovery prototyping

    Science.gov (United States)

    Hanson, Matt

    1990-01-01

    The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

  8. Automated Fault Detection for DIII-D Tokamak Experiments

    International Nuclear Information System (INIS)

    Walker, M.L.; Scoville, J.T.; Johnson, R.D.; Hyatt, A.W.; Lee, J.

    1999-01-01

    An automated fault detection software system has been developed and was used during 1999 DIII-D plasma operations. The Fault Identification and Communication System (FICS) executes automatically after every plasma discharge to check dozens of subsystems for proper operation and communicates the test results to the tokamak operator. This system is now used routinely during DIII-D operations and has led to an increase in tokamak productivity

  9. Automatic fault extraction using a modified ant-colony algorithm

    International Nuclear Information System (INIS)

    Zhao, Junsheng; Sun, Sam Zandong

    2013-01-01

    The basis of automatic fault extraction is seismic attributes, such as the coherence cube which is always used to identify a fault by the minimum value. The biggest challenge in automatic fault extraction is noise, including that of seismic data. However, a fault has a better spatial continuity in certain direction, which makes it quite different from noise. Considering this characteristic, a modified ant-colony algorithm is introduced into automatic fault identification and tracking, where the gradient direction and direction consistency are used as constraints. Numerical model test results show that this method is feasible and effective in automatic fault extraction and noise suppression. The application of field data further illustrates its validity and superiority. (paper)

  10. An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis

    Directory of Open Access Journals (Sweden)

    Jian-Hua Zhong

    2016-01-01

    Full Text Available Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM, to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

  11. Automated fault tree analysis: the GRAFTER system

    International Nuclear Information System (INIS)

    Sancaktar, S.; Sharp, D.R.

    1985-01-01

    An inherent part of probabilistic risk assessment (PRA) is the construction and analysis of detailed fault trees. For this purpose, a fault tree computer graphics code named GRAFTER has been developed. The code system centers around the GRAFTER code. This code is used interactively to construct, store, update and print fault trees of small or large sizes. The SIMON code is used to provide data for the basic event probabilities. ENCODE is used to process the GRAFTER files to prepare input for the WAMCUT code. WAMCUT is used to quantify the top event probability and to identify the cutsets. This code system has been extensively used in various PRA projects. It has resulted in reduced manpower costs, increased QA capability, ease of documentation and it has simplified sensitivity analyses. Because of its automated nature, it is also suitable for LIVING PRA Studies which require updating and modifications during the lifetime of the plant. Brief descriptions and capabilities of the GRAFTER, SIMON and ENCODE codes are provided; an application of the GRAFTER system is outlined; and conclusions and comments on the code system are given

  12. Optimization-based Method for Automated Road Network Extraction

    International Nuclear Information System (INIS)

    Xiong, D

    2001-01-01

    Automated road information extraction has significant applicability in transportation. It provides a means for creating, maintaining, and updating transportation network databases that are needed for purposes ranging from traffic management to automated vehicle navigation and guidance. This paper is to review literature on the subject of road extraction and to describe a study of an optimization-based method for automated road network extraction

  13. Component-based modeling of systems for automated fault tree generation

    International Nuclear Information System (INIS)

    Majdara, Aref; Wakabayashi, Toshio

    2009-01-01

    One of the challenges in the field of automated fault tree construction is to find an efficient modeling approach that can support modeling of different types of systems without ignoring any necessary details. In this paper, we are going to represent a new system of modeling approach for computer-aided fault tree generation. In this method, every system model is composed of some components and different types of flows propagating through them. Each component has a function table that describes its input-output relations. For the components having different operational states, there is also a state transition table. Each component can communicate with other components in the system only through its inputs and outputs. A trace-back algorithm is proposed that can be applied to the system model to generate the required fault trees. The system modeling approach and the fault tree construction algorithm are applied to a fire sprinkler system and the results are presented

  14. Automated vehicle for railway track fault detection

    Science.gov (United States)

    Bhushan, M.; Sujay, S.; Tushar, B.; Chitra, P.

    2017-11-01

    For the safety reasons, railroad tracks need to be inspected on a regular basis for detecting physical defects or design non compliances. Such track defects and non compliances, if not detected in a certain interval of time, may eventually lead to severe consequences such as train derailments. Inspection must happen twice weekly by a human inspector to maintain safety standards as there are hundreds and thousands of miles of railroad track. But in such type of manual inspection, there are many drawbacks that may result in the poor inspection of the track, due to which accidents may cause in future. So to avoid such errors and severe accidents, this automated system is designed.Such a concept would surely introduce automation in the field of inspection process of railway track and can help to avoid mishaps and severe accidents due to faults in the track.

  15. Research on Weak Fault Extraction Method for Alleviating the Mode Mixing of LMD

    Directory of Open Access Journals (Sweden)

    Lin Zhang

    2018-05-01

    Full Text Available Compared with the strong background noise, the energy entropy of early fault signals of bearings are weak under actual working conditions. Therefore, extracting the bearings’ early fault features has always been a major difficulty in fault diagnosis of rotating machinery. Based on the above problems, the masking method is introduced into the Local Mean Decomposition (LMD decomposition process, and a weak fault extraction method based on LMD and mask signal (MS is proposed. Due to the mode mixing of the product function (PF components decomposed by LMD in the noisy background, it is difficult to distinguish the authenticity of the fault frequency. Therefore, the MS method is introduced to deal with the PF components that are decomposed by the LMD and have strong correlation with the original signal, so as to suppress the modal aliasing phenomenon and extract the fault frequencies. In this paper, the actual fault signal of the rolling bearing is analyzed. By combining the MS method with the LMD method, the fault signal mixed with the noise is processed. The kurtosis value at the fault frequency is increased by eight-fold, and the signal-to-noise ratio (SNR is increased by 19.1%. The fault signal is successfully extracted by the proposed composite method.

  16. Research on vibration signal analysis and extraction method of gear local fault

    Science.gov (United States)

    Yang, X. F.; Wang, D.; Ma, J. F.; Shao, W.

    2018-02-01

    Gear is the main connection parts and power transmission parts in the mechanical equipment. If the fault occurs, it directly affects the running state of the whole machine and even endangers the personal safety. So it has important theoretical significance and practical value to study on the extraction of the gear fault signal and fault diagnosis of the gear. In this paper, the gear local fault as the research object, set up the vibration model of gear fault vibration mechanism, derive the vibration mechanism of the gear local fault and analyzes the similarities and differences of the vibration signal between the gear non fault and the gears local faults. In the MATLAB environment, the wavelet transform algorithm is used to denoise the fault signal. Hilbert transform is used to demodulate the fault vibration signal. The results show that the method can denoise the strong noise mechanical vibration signal and extract the local fault feature information from the fault vibration signal..

  17. Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

    Directory of Open Access Journals (Sweden)

    Hsueh-Hsien Chang

    2017-04-01

    Full Text Available This paper proposes statistical feature extraction methods combined with artificial intelligence (AI approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

  18. Automated Generation of Fault Management Artifacts from a Simple System Model

    Science.gov (United States)

    Kennedy, Andrew K.; Day, John C.

    2013-01-01

    Our understanding of off-nominal behavior - failure modes and fault propagation - in complex systems is often based purely on engineering intuition; specific cases are assessed in an ad hoc fashion as a (fallible) fault management engineer sees fit. This work is an attempt to provide a more rigorous approach to this understanding and assessment by automating the creation of a fault management artifact, the Failure Modes and Effects Analysis (FMEA) through querying a representation of the system in a SysML model. This work builds off the previous development of an off-nominal behavior model for the upcoming Soil Moisture Active-Passive (SMAP) mission at the Jet Propulsion Laboratory. We further developed the previous system model to more fully incorporate the ideas of State Analysis, and it was restructured in an organizational hierarchy that models the system as layers of control systems while also incorporating the concept of "design authority". We present software that was developed to traverse the elements and relationships in this model to automatically construct an FMEA spreadsheet. We further discuss extending this model to automatically generate other typical fault management artifacts, such as Fault Trees, to efficiently portray system behavior, and depend less on the intuition of fault management engineers to ensure complete examination of off-nominal behavior.

  19. Sensor fault-tolerant control for gear-shifting engaging process of automated manual transmission

    Science.gov (United States)

    Li, Liang; He, Kai; Wang, Xiangyu; Liu, Yahui

    2018-01-01

    Angular displacement sensor on the actuator of automated manual transmission (AMT) is sensitive to fault, and the sensor fault will disturb its normal control, which affects the entire gear-shifting process of AMT and results in awful riding comfort. In order to solve this problem, this paper proposes a method of fault-tolerant control for AMT gear-shifting engaging process. By using the measured current of actuator motor and angular displacement of actuator, the gear-shifting engaging load torque table is built and updated before the occurrence of the sensor fault. Meanwhile, residual between estimated and measured angular displacements is used to detect the sensor fault. Once the residual exceeds a determined fault threshold, the sensor fault is detected. Then, switch control is triggered, and the current observer and load torque table estimates an actual gear-shifting position to replace the measured one to continue controlling the gear-shifting process. Numerical and experiment tests are carried out to evaluate the reliability and feasibility of proposed methods, and the results show that the performance of estimation and control is satisfactory.

  20. CAT: a computer code for the automated construction of fault trees

    International Nuclear Information System (INIS)

    Apostolakis, G.E.; Salem, S.L.; Wu, J.S.

    1978-03-01

    A computer code, CAT (Computer Automated Tree, is presented which applies decision table methods to model the behavior of components for systematic construction of fault trees. The decision tables for some commonly encountered mechanical and electrical components are developed; two nuclear subsystems, a Containment Spray Recirculation System and a Consequence Limiting Control System, are analyzed to demonstrate the applications of CAT code

  1. [DNA Extraction from Old Bones by AutoMate Express™ System].

    Science.gov (United States)

    Li, B; Lü, Z

    2017-08-01

    To establish a method for extracting DNA from old bones by AutoMate Express™ system. Bones were grinded into powder by freeze-mill. After extraction by AutoMate Express™, DNA were amplified and genotyped by Identifiler®Plus and MinFiler™ kits. DNA were extracted from 10 old bone samples, which kept in different environments with the postmortem interval from 10 to 20 years, in 3 hours by AutoMate Express™ system. Complete STR typing results were obtained from 8 samples. AutoMate Express™ system can quickly and efficiently extract DNA from old bones, which can be applied in forensic practice. Copyright© by the Editorial Department of Journal of Forensic Medicine

  2. Path Searching Based Fault Automated Recovery Scheme for Distribution Grid with DG

    Science.gov (United States)

    Xia, Lin; Qun, Wang; Hui, Xue; Simeng, Zhu

    2016-12-01

    Applying the method of path searching based on distribution network topology in setting software has a good effect, and the path searching method containing DG power source is also applicable to the automatic generation and division of planned islands after the fault. This paper applies path searching algorithm in the automatic division of planned islands after faults: starting from the switch of fault isolation, ending in each power source, and according to the line load that the searching path traverses and the load integrated by important optimized searching path, forming optimized division scheme of planned islands that uses each DG as power source and is balanced to local important load. Finally, COBASE software and distribution network automation software applied are used to illustrate the effectiveness of the realization of such automatic restoration program.

  3. Automated extraction of DNA from clothing

    DEFF Research Database (Denmark)

    Stangegaard, Michael; Hjort, Benjamin Benn; Nøhr Hansen, Thomas

    2011-01-01

    Presence of PCR inhibitors in extracted DNA may interfere with the subsequent quantification and short tandem repeat (STR) reactions used in forensic genetic DNA typing. We have compared three automated DNA extraction methods based on magnetic beads with a manual method with the aim of reducing...

  4. Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

    OpenAIRE

    Yuehai Wang; Yongzheng Yan; Qinyong Wang

    2016-01-01

    Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In ...

  5. Multiple-Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

    Directory of Open Access Journals (Sweden)

    Min Zhang

    2018-01-01

    Full Text Available Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE and multiclass support vector machine (MSVM with particle parameter adaptive (PPA is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1 the proposed intelligent method (MFE_PPA_MSVM improves the classification recognition rate; (2 the accuracy will decline when the number of fault patterns increases; (3 prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

  6. Recent advances in automated system model extraction (SME)

    International Nuclear Information System (INIS)

    Narayanan, Nithin; Bloomsburgh, John; He Yie; Mao Jianhua; Patil, Mahesh B; Akkaraju, Sandeep

    2006-01-01

    In this paper we present two different techniques for automated extraction of system models from FEA models. We discuss two different algorithms: for (i) automated N-DOF SME for electrostatically actuated MEMS and (ii) automated N-DOF SME for MEMS inertial sensors. We will present case studies for the two different algorithms presented

  7. Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

    Directory of Open Access Journals (Sweden)

    Saleem Riaz

    2017-02-01

    Full Text Available Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective bearing fault diagnostic method using different fault features at different steps becomes more attractive. Bearings are widely used in medical applications, food processing industries, semi-conductor industries, paper making industries and aircraft components. This paper review has demonstrated that the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction. Generally literature is classified into two main groups: frequency domain, time frequency analysis. However, fault detection and diagnosis of rotating machine vibration signal processing methods to present their own limitations. In practice, most healthy ingredients faulty vibration signal from background noise and mechanical vibration signals are buried. This paper also reviews that how the advanced signal processing methods, empirical mode decomposition and interference cancellation algorithm has been investigated and developed. The condition for rotating machines based rehabilitation, prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too. Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the researchers are widely interested to make automatic

  8. A New Method for Weak Fault Feature Extraction Based on Improved MED

    Directory of Open Access Journals (Sweden)

    Junlin Li

    2018-01-01

    Full Text Available Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA, finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.

  9. PCA Fault Feature Extraction in Complex Electric Power Systems

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2010-08-01

    Full Text Available Electric power system is one of the most complex artificial systems in the world. The complexity is determined by its characteristics about constitution, configuration, operation, organization, etc. The fault in electric power system cannot be completely avoided. When electric power system operates from normal state to failure or abnormal, its electric quantities (current, voltage and angles, etc. may change significantly. Our researches indicate that the variable with the biggest coefficient in principal component usually corresponds to the fault. Therefore, utilizing real-time measurements of phasor measurement unit, based on principal components analysis technology, we have extracted successfully the distinct features of fault component. Of course, because of the complexity of different types of faults in electric power system, there still exists enormous problems need a close and intensive study.

  10. Automated extraction of faults and porous reservoir bodies. Examples from the Vallhall Field

    Energy Technology Data Exchange (ETDEWEB)

    Barkved, Olav Inge; Whitman, Doug; Kunz, Tim

    1998-12-31

    The Norwegian Vahall field is located 250 km South-West of Stavanger. The production is primarily from the highly porous and fractured chalk, the Tor formation. Fractures, evidently play a significant role in enhancing flow properties as well as production rates, are significantly higher than expected from matrix permeability alone. The fractures are primarily tectonically induced and related to faulting. Syn-depositional faulting is believed to be a controlling factor on reservoir thickness variations observed across the field. Due to the low acoustic contrast and weak appearance of the highly porous chalk, direct evidence of faulting in well bore logs is limited. The seismic data quality in the most central area of the field is very poor due to tertiary gas charging, but in the flank area of the field, the quality is excellent. 1 ref., 5 figs.

  11. Average combination difference morphological filters for fault feature extraction of bearing

    Science.gov (United States)

    Lv, Jingxiang; Yu, Jianbo

    2018-02-01

    In order to extract impulse components from vibration signals with much noise and harmonics, a new morphological filter called average combination difference morphological filter (ACDIF) is proposed in this paper. ACDIF constructs firstly several new combination difference (CDIF) operators, and then integrates the best two CDIFs as the final morphological filter. This design scheme enables ACIDF to extract positive and negative impacts existing in vibration signals to enhance accuracy of bearing fault diagnosis. The length of structure element (SE) that affects the performance of ACDIF is determined adaptively by a new indicator called Teager energy kurtosis (TEK). TEK further improves the effectiveness of ACDIF for fault feature extraction. Experimental results on the simulation and bearing vibration signals demonstrate that ACDIF can effectively suppress noise and extract periodic impulses from bearing vibration signals.

  12. Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Xiaojie Guo

    2016-12-01

    Full Text Available Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN and deep convolution neural network (DCNN, have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.

  13. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems.

    Science.gov (United States)

    Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun; Wang, Gi-Nam

    2016-01-01

    Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

  14. PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

    Directory of Open Access Journals (Sweden)

    Arup Ghosh

    2016-01-01

    Full Text Available Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

  15. Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

    Directory of Open Access Journals (Sweden)

    Sheraz Ali Khan

    2016-01-01

    Full Text Available Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A k-nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

  16. Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

    Science.gov (United States)

    Sauer, Juergen; Chavaillaz, Alain; Wastell, David

    2016-06-01

    This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

  17. Decision-table development for use with the CAT code for the automated fault-tree construction

    International Nuclear Information System (INIS)

    Wu, J.S.; Salem, S.L.; Apostolakis, G.E.

    1977-01-01

    A library of decision tables to be used in connection with the CAT computer code for the automated construction of fault trees is presented. A decision table is constructed for each component type describing the output of the component in terms of its inputs and its internal states. In addition, a modification of the CAT code that couples it with a fault tree analysis code is presented. This report represents one aspect of a study entitled, 'A General Evaluation Approach to Risk-Benefit for Large Technological Systems, and Its Application to Nuclear Power.'

  18. Automated valve fault detection based on acoustic emission parameters and support vector machine

    Directory of Open Access Journals (Sweden)

    Salah M. Ali

    2018-03-01

    Full Text Available Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor. Keywords: Condition monitoring, Faults detection, Signal analysis, Acoustic emission, Support vector machine

  19. FAULT DIAGNOSIS OF AN AIRCRAFT CONTROL SURFACES WITH AN AUTOMATED CONTROL SYSTEM

    Directory of Open Access Journals (Sweden)

    Blessing D. Ogunvoul

    2017-01-01

    Full Text Available This article is devoted to studying of fault diagnosis of an aircraft control surfaces using fault models to identify specific causes. Such failures as jamming, vibration, extreme run out and performance decrease are covered.It is proved that in case of an actuator failure or flight control structural damage, the aircraft performance decreases significantly. Commercial aircraft frequently appear in the areas of military conflicts and terrorist activity, where the risk of shooting attack is high, for example in Syria, Iraq, South Sudan etc. Accordingly, it is necessary to create and assess the fault model to identify the flight control failures.The research results demonstrate that the adequate fault model is the first step towards the managing the challenges of loss of aircraft controllability. This model is also an element of adaptive failure-resistant management model.The research considers the relationship between the parameters of an i th state of a control surface and its angular rate, also parameters classification associated with specific control surfaces in order to avoid conflict/inconsistency in the determination of a faulty control surface and its condition.The results of the method obtained in this article can be used in the design of an aircraft automated control system for timely identification of fault/failure of a specific control surface, that would contribute to an effective role aimed at increasing the survivability of an aircraft and increasing the acceptable level of safety due to loss of control.

  20. Evaluation of Four Automated Protocols for Extraction of DNA from FTA Cards

    OpenAIRE

    Stangegaard, Michael; Børsting, Claus; Ferrero-Miliani, Laura; Frank-Hansen, Rune; Poulsen, Lena; Hansen, Anders J; Morling, Niels

    2013-01-01

    Extraction of DNA using magnetic bead-based techniques on automated DNA extraction instruments provides a fast, reliable, and reproducible method for DNA extraction from various matrices. Here, we have compared the yield and quality of DNA extracted from FTA cards using four automated extraction protocols on three different instruments. The extraction processes were repeated up to six times with the same pieces of FTA cards. The sample material on the FTA cards was either blood or buccal cell...

  1. Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

    OpenAIRE

    Saleem Riaz; Hassan Elahi; Kashif Javaid; Tufail Shahzad

    2017-01-01

    Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, deve...

  2. A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

    Science.gov (United States)

    Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

    2017-11-01

    Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

  3. Fault tolerant strategies for automated operation of nuclear reactors

    International Nuclear Information System (INIS)

    Berkan, R.C.; Tsoukalas, L.

    1991-01-01

    This paper introduces an automatic control system incorporating a number of verification, validation, and command generation tasks with-in a fault-tolerant architecture. The integrated system utilizes recent methods of artificial intelligence such as neural networks and fuzzy logic control. Furthermore, advanced signal processing and nonlinear control methods are also included in the design. The primary goal is to create an on-line capability to validate signals, analyze plant performance, and verify the consistency of commands before control decisions are finalized. The application of this approach to the automated startup of the Experimental Breeder Reactor-II (EBR-II) is performed using a validated nonlinear model. The simulation results show that the advanced concepts have the potential to improve plant availability andsafety

  4. Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Lin Liang

    2015-01-01

    Full Text Available A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness. Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.

  5. The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

    Directory of Open Access Journals (Sweden)

    Te Han

    2016-01-01

    Full Text Available Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears. Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

  6. Stator and Rotor Faults Diagnosis of Squirrel Cage Motor Based on Fundamental Component Extraction Method

    Directory of Open Access Journals (Sweden)

    Guoqing An

    2017-01-01

    Full Text Available Nowadays, stator current analysis used for detecting the incipient fault in squirrel cage motor has received much attention. However, in the case of interturn short circuit in stator, the traditional symmetrical component method has lost the precondition due to the harmonics and noise; the negative sequence component (NSC is hard to be obtained accurately. For broken rotor bars, the new added fault feature blanked by fundamental component is also difficult to be discriminated in the current spectrum. To solve the above problems, a fundamental component extraction (FCE method is proposed in this paper. On one hand, via the antisynchronous speed coordinate (ASC transformation, NSC of extracted signals is transformed into the DC value. The amplitude of synthetic vector of NSC is used to evaluate the severity of stator fault. On the other hand, the extracted fundamental component can be filtered out to make the rotor fault feature emerge from the stator current spectrum. Experiment results indicate that this method is feasible and effective in both interturn short circuit and broken rotor bars fault diagnosis. Furthermore, only stator currents and voltage frequency are needed to be recorded, and this method is easy to implement.

  7. Fault tree analysis: concepts and techniques

    International Nuclear Information System (INIS)

    Fussell, J.B.

    1976-01-01

    Concepts and techniques of fault tree analysis have been developed over the past decade and now predictions from this type analysis are important considerations in the design of many systems such as aircraft, ships and their electronic systems, missiles, and nuclear reactor systems. Routine, hardware-oriented fault tree construction can be automated; however, considerable effort is needed in this area to get the methodology into production status. When this status is achieved, the entire analysis of hardware systems will be automated except for the system definition step. Automated analysis is not undesirable; to the contrary, when verified on adequately complex systems, automated analysis could well become a routine analysis. It could also provide an excellent start for a more in-depth fault tree analysis that includes environmental effects, common mode failure, and human errors. The automated analysis is extremely fast and frees the analyst from the routine hardware-oriented fault tree construction, as well as eliminates logic errors and errors of oversight in this part of the analysis. Automated analysis then affords the analyst a powerful tool to allow his prime efforts to be devoted to unearthing more subtle aspects of the modes of failure of the system

  8. Automated Feature Extraction from Hyperspectral Imagery, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed activities will result in the development of a novel hyperspectral feature-extraction toolkit that will provide a simple, automated, and accurate...

  9. What is Fault Tolerant Control

    DEFF Research Database (Denmark)

    Blanke, Mogens; Frei, C. W.; Kraus, K.

    2000-01-01

    Faults in automated processes will often cause undesired reactions and shut-down of a controlled plant, and the consequences could be damage to the plant, to personnel or the environment. Fault-tolerant control is the synonym for a set of recent techniques that were developed to increase plant...... availability and reduce the risk of safety hazards. Its aim is to prevent that simple faults develop into serious failure. Fault-tolerant control merges several disciplines to achieve this goal, including on-line fault diagnosis, automatic condition assessment and calculation of remedial actions when a fault...... is detected. The envelope of the possible remedial actions is wide. This paper introduces tools to analyze and explore structure and other fundamental properties of an automated system such that any redundancy in the process can be fully utilized to enhance safety and a availability....

  10. Wire Finishing Mill Rolling Bearing Fault Diagnosis Based on Feature Extraction and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Hong-Yu LIU

    2014-10-01

    Full Text Available Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals. Frequency domain feature extraction and wavelet transform feature extraction were made on collected signals. BP neural network fault diagnosis model was adopted. Frequency domain feature values and wavelet transform feature values were treated as neural network input values. Various typical fault models were treated as neural network output values. Corresponding relations between feature vector and fault omen were utilized. BP neural network model of typical wire plant finishing mill rolling bearing fault was constructed by training many groups sample data. After inputting sample needed to be diagnosed, wire plant finishing mill rolling bearing fault can be diagnosed. This research has important practical significance on enhancing rolling bearing fault diagnosis precision, repairing rolling bearing duly, decreasing stop time, enhancing equipment running efficiency and enhancing economic benefits.

  11. A Rolling Element Bearing Fault Diagnosis Approach Based on Multifractal Theory and Gray Relation Theory.

    Science.gov (United States)

    Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying

    2016-01-01

    Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.

  12. Applications of the Automated SMAC Modal Parameter Extraction Package

    International Nuclear Information System (INIS)

    MAYES, RANDALL L.; DORRELL, LARRY R.; KLENKE, SCOTT E.

    1999-01-01

    An algorithm known as SMAC (Synthesize Modes And Correlate), based on principles of modal filtering, has been in development for a few years. The new capabilities of the automated version are demonstrated on test data from a complex shell/payload system. Examples of extractions from impact and shaker data are shown. The automated algorithm extracts 30 to 50 modes in the bandwidth from each column of the frequency response function matrix. Examples of the synthesized Mode Indicator Functions (MIFs) compared with the actual MIFs show the accuracy of the technique. A data set for one input and 170 accelerometer outputs can typically be reduced in an hour. Application to a test with some complex modes is also demonstrated

  13. Automated extraction of pleural effusion in three-dimensional thoracic CT images

    Science.gov (United States)

    Kido, Shoji; Tsunomori, Akinori

    2009-02-01

    It is important for diagnosis of pulmonary diseases to measure volume of accumulating pleural effusion in threedimensional thoracic CT images quantitatively. However, automated extraction of pulmonary effusion correctly is difficult. Conventional extraction algorithm using a gray-level based threshold can not extract pleural effusion from thoracic wall or mediastinum correctly, because density of pleural effusion in CT images is similar to those of thoracic wall or mediastinum. So, we have developed an automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion. Our method used a template of lung obtained from a normal lung for segmentation of lungs with pleural effusions. Registration process consisted of two steps. First step was a global matching processing between normal and abnormal lungs of organs such as bronchi, bones (ribs, sternum and vertebrae) and upper surfaces of livers which were extracted using a region-growing algorithm. Second step was a local matching processing between normal and abnormal lungs which were deformed by the parameter obtained from the global matching processing. Finally, we segmented a lung with pleural effusion by use of the template which was deformed by two parameters obtained from the global matching processing and the local matching processing. We compared our method with a conventional extraction method using a gray-level based threshold and two published methods. The extraction rates of pleural effusions obtained from our method were much higher than those obtained from other methods. Automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion is promising for diagnosis of pulmonary diseases by providing quantitative volume of accumulating pleural effusion.

  14. Investigating Semi-Automated Cadastral Boundaries Extraction from Airborne Laser Scanned Data

    Directory of Open Access Journals (Sweden)

    Xianghuan Luo

    2017-09-01

    Full Text Available Many developing countries have witnessed the urgent need of accelerating cadastral surveying processes. Previous studies found that large portions of cadastral boundaries coincide with visible physical objects, namely roads, fences, and building walls. This research explores the application of airborne laser scanning (ALS techniques on cadastral surveys. A semi-automated workflow is developed to extract cadastral boundaries from an ALS point clouds. Firstly, a two-phased workflow was developed that focused on extracting digital representations of physical objects. In the automated extraction phase, after classifying points into semantic components, the outline of planar objects such as building roofs and road surfaces were generated by an α-shape algorithm, whilst the centerlines delineatiation approach was fitted into the lineate object—a fence. Afterwards, the extracted vector lines were edited and refined during the post-refinement phase. Secondly, we quantitatively evaluated the workflow performance by comparing results against an exiting cadastral map as reference. It was found that the workflow achieved promising results: around 80% completeness and 60% correctness on average, although the spatial accuracy is still modest. It is argued that the semi-automated extraction workflow could effectively speed up cadastral surveying, with both human resources and equipment costs being reduced

  15. Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

    International Nuclear Information System (INIS)

    Wang, M; Hu, N Q; Qin, G J

    2011-01-01

    In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

  16. Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Wang, M; Hu, N Q; Qin, G J, E-mail: hnq@nudt.edu.cn, E-mail: wm198063@yahoo.com.cn [School of Mechatronic Engineering and Automation, National University of Defense Technology, ChangSha, Hunan, 410073 (China)

    2011-07-19

    In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

  17. Arduino-based automation of a DNA extraction system.

    Science.gov (United States)

    Kim, Kyung-Won; Lee, Mi-So; Ryu, Mun-Ho; Kim, Jong-Won

    2015-01-01

    There have been many studies to detect infectious diseases with the molecular genetic method. This study presents an automation process for a DNA extraction system based on microfluidics and magnetic bead, which is part of a portable molecular genetic test system. This DNA extraction system consists of a cartridge with chambers, syringes, four linear stepper actuators, and a rotary stepper actuator. The actuators provide a sequence of steps in the DNA extraction process, such as transporting, mixing, and washing for the gene specimen, magnetic bead, and reagent solutions. The proposed automation system consists of a PC-based host application and an Arduino-based controller. The host application compiles a G code sequence file and interfaces with the controller to execute the compiled sequence. The controller executes stepper motor axis motion, time delay, and input-output manipulation. It drives the stepper motor with an open library, which provides a smooth linear acceleration profile. The controller also provides a homing sequence to establish the motor's reference position, and hard limit checking to prevent any over-travelling. The proposed system was implemented and its functionality was investigated, especially regarding positioning accuracy and velocity profile.

  18. Fault Features Extraction and Identification based Rolling Bearing Fault Diagnosis

    International Nuclear Information System (INIS)

    Qin, B; Sun, G D; Zhang L Y; Wang J G; HU, J

    2017-01-01

    For the fault classification model based on extreme learning machine (ELM), the diagnosis accuracy and stability of rolling bearing is greatly influenced by a critical parameter, which is the number of nodes in hidden layer of ELM. An adaptive adjustment strategy is proposed based on vibrational mode decomposition, permutation entropy, and nuclear kernel extreme learning machine to determine the tunable parameter. First, the vibration signals are measured and then decomposed into different fault feature models based on variation mode decomposition. Then, fault feature of each model is formed to a high dimensional feature vector set based on permutation entropy. Second, the ELM output function is expressed by the inner product of Gauss kernel function to adaptively determine the number of hidden layer nodes. Finally, the high dimension feature vector set is used as the input to establish the kernel ELM rolling bearing fault classification model, and the classification and identification of different fault states of rolling bearings are carried out. In comparison with the fault classification methods based on support vector machine and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability. (paper)

  19. Computer aided fault tree construction for electrical systems

    International Nuclear Information System (INIS)

    Fussell, J.B.

    1975-01-01

    A technique is presented for automated construction of the Boolean failure logic diagram, called the fault tree, for electrical systems. The method is a technique for synthesizing a fault tree from system-independent component characteristics. Terminology is defined and heuristic examples are given for all phases of the model. The computer constructed fault trees are in conventional format, use conventional symbols, and are deductively constructed from the main failure of interest to the individual component failures. The synthesis technique is generally applicable to automated fault tree construction for other types of systems

  20. Automated sample preparation using membrane microtiter extraction for bioanalytical mass spectrometry.

    Science.gov (United States)

    Janiszewski, J; Schneider, P; Hoffmaster, K; Swyden, M; Wells, D; Fouda, H

    1997-01-01

    The development and application of membrane solid phase extraction (SPE) in 96-well microtiter plate format is described for the automated analysis of drugs in biological fluids. The small bed volume of the membrane allows elution of the analyte in a very small solvent volume, permitting direct HPLC injection and negating the need for the time consuming solvent evaporation step. A programmable liquid handling station (Quadra 96) was modified to automate all SPE steps. To avoid drying of the SPE bed and to enhance the analytical precision a novel protocol for performing the condition, load and wash steps in rapid succession was utilized. A block of 96 samples can now be extracted in 10 min., about 30 times faster than manual solvent extraction or single cartridge SPE methods. This processing speed complements the high-throughput speed of contemporary high performance liquid chromatography mass spectrometry (HPLC/MS) analysis. The quantitative analysis of a test analyte (Ziprasidone) in plasma demonstrates the utility and throughput of membrane SPE in combination with HPLC/MS. The results obtained with the current automated procedure compare favorably with those obtained using solvent and traditional solid phase extraction methods. The method has been used for the analysis of numerous drug prototypes in biological fluids to support drug discovery efforts.

  1. An Analytical Model for Assessing Stability of Pre-Existing Faults in Caprock Caused by Fluid Injection and Extraction in a Reservoir

    Science.gov (United States)

    Wang, Lei; Bai, Bing; Li, Xiaochun; Liu, Mingze; Wu, Haiqing; Hu, Shaobin

    2016-07-01

    Induced seismicity and fault reactivation associated with fluid injection and depletion were reported in hydrocarbon, geothermal, and waste fluid injection fields worldwide. Here, we establish an analytical model to assess fault reactivation surrounding a reservoir during fluid injection and extraction that considers the stress concentrations at the fault tips and the effects of fault length. In this model, induced stress analysis in a full-space under the plane strain condition is implemented based on Eshelby's theory of inclusions in terms of a homogeneous, isotropic, and poroelastic medium. The stress intensity factor concept in linear elastic fracture mechanics is adopted as an instability criterion for pre-existing faults in surrounding rocks. To characterize the fault reactivation caused by fluid injection and extraction, we define a new index, the "fault reactivation factor" η, which can be interpreted as an index of fault stability in response to fluid pressure changes per unit within a reservoir resulting from injection or extraction. The critical fluid pressure change within a reservoir is also determined by the superposition principle using the in situ stress surrounding a fault. Our parameter sensitivity analyses show that the fault reactivation tendency is strongly sensitive to fault location, fault length, fault dip angle, and Poisson's ratio of the surrounding rock. Our case study demonstrates that the proposed model focuses on the mechanical behavior of the whole fault, unlike the conventional methodologies. The proposed method can be applied to engineering cases related to injection and depletion within a reservoir owing to its efficient computational codes implementation.

  2. Automated road network extraction from high spatial resolution multi-spectral imagery

    Science.gov (United States)

    Zhang, Qiaoping

    For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a

  3. The Orion GN and C Data-Driven Flight Software Architecture for Automated Sequencing and Fault Recovery

    Science.gov (United States)

    King, Ellis; Hart, Jeremy; Odegard, Ryan

    2010-01-01

    The Orion Crew Exploration Vehicle (CET) is being designed to include significantly more automation capability than either the Space Shuttle or the International Space Station (ISS). In particular, the vehicle flight software has requirements to accommodate increasingly automated missions throughout all phases of flight. A data-driven flight software architecture will provide an evolvable automation capability to sequence through Guidance, Navigation & Control (GN&C) flight software modes and configurations while maintaining the required flexibility and human control over the automation. This flexibility is a key aspect needed to address the maturation of operational concepts, to permit ground and crew operators to gain trust in the system and mitigate unpredictability in human spaceflight. To allow for mission flexibility and reconfrgurability, a data driven approach is being taken to load the mission event plan as well cis the flight software artifacts associated with the GN&C subsystem. A database of GN&C level sequencing data is presented which manages and tracks the mission specific and algorithm parameters to provide a capability to schedule GN&C events within mission segments. The flight software data schema for performing automated mission sequencing is presented with a concept of operations for interactions with ground and onboard crew members. A prototype architecture for fault identification, isolation and recovery interactions with the automation software is presented and discussed as a forward work item.

  4. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jia Uddin

    2014-01-01

    Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

  5. Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

    Science.gov (United States)

    Zhang, Shengli; Tang, Jiong

    2016-04-01

    Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

  6. Integrating angle-frequency domain synchronous averaging technique with feature extraction for gear fault diagnosis

    Science.gov (United States)

    Zhang, Shengli; Tang, J.

    2018-01-01

    Gear fault diagnosis relies heavily on the scrutiny of vibration responses measured. In reality, gear vibration signals are noisy and dominated by meshing frequencies as well as their harmonics, which oftentimes overlay the fault related components. Moreover, many gear transmission systems, e.g., those in wind turbines, constantly operate under non-stationary conditions. To reduce the influences of non-synchronous components and noise, a fault signature enhancement method that is built upon angle-frequency domain synchronous averaging is developed in this paper. Instead of being averaged in the time domain, the signals are processed in the angle-frequency domain to solve the issue of phase shifts between signal segments due to uncertainties caused by clearances, input disturbances, and sampling errors, etc. The enhanced results are then analyzed through feature extraction algorithms to identify the most distinct features for fault classification and identification. Specifically, Kernel Principal Component Analysis (KPCA) targeting at nonlinearity, Multilinear Principal Component Analysis (MPCA) targeting at high dimensionality, and Locally Linear Embedding (LLE) targeting at local similarity among the enhanced data are employed and compared to yield insights. Numerical and experimental investigations are performed, and the results reveal the effectiveness of angle-frequency domain synchronous averaging in enabling feature extraction and classification.

  7. Evaluation of four automated protocols for extraction of DNA from FTA cards.

    Science.gov (United States)

    Stangegaard, Michael; Børsting, Claus; Ferrero-Miliani, Laura; Frank-Hansen, Rune; Poulsen, Lena; Hansen, Anders J; Morling, Niels

    2013-10-01

    Extraction of DNA using magnetic bead-based techniques on automated DNA extraction instruments provides a fast, reliable, and reproducible method for DNA extraction from various matrices. Here, we have compared the yield and quality of DNA extracted from FTA cards using four automated extraction protocols on three different instruments. The extraction processes were repeated up to six times with the same pieces of FTA cards. The sample material on the FTA cards was either blood or buccal cells. With the QIAamp DNA Investigator and QIAsymphony DNA Investigator kits, it was possible to extract DNA from the FTA cards in all six rounds of extractions in sufficient amount and quality to obtain complete short tandem repeat (STR) profiles on a QIAcube and a QIAsymphony SP. With the PrepFiler Express kit, almost all the extractable DNA was extracted in the first two rounds of extractions. Furthermore, we demonstrated that it was possible to successfully extract sufficient DNA for STR profiling from previously processed FTA card pieces that had been stored at 4 °C for up to 1 year. This showed that rare or precious FTA card samples may be saved for future analyses even though some DNA was already extracted from the FTA cards.

  8. Fault Management Techniques in Human Spaceflight Operations

    Science.gov (United States)

    O'Hagan, Brian; Crocker, Alan

    2006-01-01

    This paper discusses human spaceflight fault management operations. Fault detection and response capabilities available in current US human spaceflight programs Space Shuttle and International Space Station are described while emphasizing system design impacts on operational techniques and constraints. Preflight and inflight processes along with products used to anticipate, mitigate and respond to failures are introduced. Examples of operational products used to support failure responses are presented. Possible improvements in the state of the art, as well as prioritization and success criteria for their implementation are proposed. This paper describes how the architecture of a command and control system impacts operations in areas such as the required fault response times, automated vs. manual fault responses, use of workarounds, etc. The architecture includes the use of redundancy at the system and software function level, software capabilities, use of intelligent or autonomous systems, number and severity of software defects, etc. This in turn drives which Caution and Warning (C&W) events should be annunciated, C&W event classification, operator display designs, crew training, flight control team training, and procedure development. Other factors impacting operations are the complexity of a system, skills needed to understand and operate a system, and the use of commonality vs. optimized solutions for software and responses. Fault detection, annunciation, safing responses, and recovery capabilities are explored using real examples to uncover underlying philosophies and constraints. These factors directly impact operations in that the crew and flight control team need to understand what happened, why it happened, what the system is doing, and what, if any, corrective actions they need to perform. If a fault results in multiple C&W events, or if several faults occur simultaneously, the root cause(s) of the fault(s), as well as their vehicle-wide impacts, must be

  9. Automated Extraction Of Associations Between Methylated Genes and Diseases From Biomedical Literature

    KAUST Repository

    Bin Res, Arwa A.

    2012-01-01

    . Based on this model, we developed a tool that automates extraction of associations between methylated genes and diseases from electronic text. Our study contributed an efficient method for extracting specific types of associations from free text

  10. Automatic bearing fault diagnosis of permanent magnet synchronous generators in wind turbines subjected to noise interference

    Science.gov (United States)

    Guo, Jun; Lu, Siliang; Zhai, Chao; He, Qingbo

    2018-02-01

    An automatic bearing fault diagnosis method is proposed for permanent magnet synchronous generators (PMSGs), which are widely installed in wind turbines subjected to low rotating speeds, speed fluctuations, and electrical device noise interferences. The mechanical rotating angle curve is first extracted from the phase current of a PMSG by sequentially applying a series of algorithms. The synchronous sampled vibration signal of the fault bearing is then resampled in the angular domain according to the obtained rotating phase information. Considering that the resampled vibration signal is still overwhelmed by heavy background noise, an adaptive stochastic resonance filter is applied to the resampled signal to enhance the fault indicator and facilitate bearing fault identification. Two types of fault bearings with different fault sizes in a PMSG test rig are subjected to experiments to test the effectiveness of the proposed method. The proposed method is fully automated and thus shows potential for convenient, highly efficient and in situ bearing fault diagnosis for wind turbines subjected to harsh environments.

  11. Concepts and Methods in Fault-tolerant Control

    DEFF Research Database (Denmark)

    Blanke, Mogens; Staroswiecly, M.; Wu, N.E.

    2001-01-01

    Faults in automated processes will often cause undesired reactions and shut-down of a controlled plant, and the consequences could be damage to technical parts of the plant, to personnel or the environment. Fault-tolerant control combines diagnosis with control methods to handle faults...

  12. Automated extraction of DNA from biological stains on fabric from crime cases. A comparison of a manual and three automated methods.

    Science.gov (United States)

    Stangegaard, Michael; Hjort, Benjamin B; Hansen, Thomas N; Hoflund, Anders; Mogensen, Helle S; Hansen, Anders J; Morling, Niels

    2013-05-01

    The presence of PCR inhibitors in extracted DNA may interfere with the subsequent quantification and short tandem repeat (STR) reactions used in forensic genetic DNA typing. DNA extraction from fabric for forensic genetic purposes may be challenging due to the occasional presence of PCR inhibitors that may be co-extracted with the DNA. Using 120 forensic trace evidence samples consisting of various types of fabric, we compared three automated DNA extraction methods based on magnetic beads (PrepFiler Express Forensic DNA Extraction Kit on an AutoMate Express, QIAsyphony DNA Investigator kit either with the sample pre-treatment recommended by Qiagen or an in-house optimized sample pre-treatment on a QIAsymphony SP) and one manual method (Chelex) with the aim of reducing the amount of PCR inhibitors in the DNA extracts and increasing the proportion of reportable STR-profiles. A total of 480 samples were processed. The highest DNA recovery was obtained with the PrepFiler Express kit on an AutoMate Express while the lowest DNA recovery was obtained using a QIAsymphony SP with the sample pre-treatment recommended by Qiagen. Extraction using a QIAsymphony SP with the sample pre-treatment recommended by Qiagen resulted in the lowest percentage of PCR inhibition (0%) while extraction using manual Chelex resulted in the highest percentage of PCR inhibition (51%). The largest number of reportable STR-profiles was obtained with DNA from samples extracted with the PrepFiler Express kit (75%) while the lowest number was obtained with DNA from samples extracted using a QIAsymphony SP with the sample pre-treatment recommended by Qiagen (41%). Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  13. Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

    Science.gov (United States)

    Kong, Yun; Wang, Tianyang; Li, Zheng; Chu, Fulei

    2017-09-01

    Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

  14. Automated extraction protocol for quantification of SARS-Coronavirus RNA in serum: an evaluation study

    Directory of Open Access Journals (Sweden)

    Lui Wing-bong

    2006-02-01

    Full Text Available Abstract Background We have previously developed a test for the diagnosis and prognostic assessment of the severe acute respiratory syndrome (SARS based on the detection of the SARS-coronavirus RNA in serum by real-time quantitative reverse transcriptase polymerase chain reaction (RT-PCR. In this study, we evaluated the feasibility of automating the serum RNA extraction procedure in order to increase the throughput of the assay. Methods An automated nucleic acid extraction platform using the MagNA Pure LC instrument (Roche Diagnostics was evaluated. We developed a modified protocol in compliance with the recommended biosafety guidelines from the World Health Organization based on the use of the MagNA Pure total nucleic acid large volume isolation kit for the extraction of SARS-coronavirus RNA. The modified protocol was compared with a column-based extraction kit (QIAamp viral RNA mini kit, Qiagen for quantitative performance, analytical sensitivity and precision. Results The newly developed automated protocol was shown to be free from carry-over contamination and have comparable performance with other standard protocols and kits designed for the MagNA Pure LC instrument. However, the automated method was found to be less sensitive, less precise and led to consistently lower serum SARS-coronavirus concentrations when compared with the column-based extraction method. Conclusion As the diagnostic efficiency and prognostic value of the serum SARS-CoV RNA RT-PCR test is critically associated with the analytical sensitivity and quantitative performance contributed both by the RNA extraction and RT-PCR components of the test, we recommend the use of the column-based manual RNA extraction method.

  15. Manned spacecraft automation and robotics

    Science.gov (United States)

    Erickson, Jon D.

    1987-01-01

    The Space Station holds promise of being a showcase user and driver of advanced automation and robotics technology. The author addresses the advances in automation and robotics from the Space Shuttle - with its high-reliability redundancy management and fault tolerance design and its remote manipulator system - to the projected knowledge-based systems for monitoring, control, fault diagnosis, planning, and scheduling, and the telerobotic systems of the future Space Station.

  16. Automated extraction of metastatic liver cancer regions from abdominal contrast CT images

    International Nuclear Information System (INIS)

    Yamakawa, Junki; Matsubara, Hiroaki; Kimura, Shouta; Hasegawa, Junichi; Shinozaki, Kenji; Nawano, Shigeru

    2010-01-01

    In this paper, automated extraction of metastatic liver cancer regions from abdominal contrast X-ray CT images is investigated. Because even in Japan, cases of metastatic liver cancers are increased due to recent Europeanization and/or Americanization of Japanese eating habits, development of a system for computer aided diagnosis of them is strongly expected. Our automated extraction procedure consists of following four steps; liver region extraction, density transformation for enhancement of cancer regions, segmentation for obtaining candidate cancer regions, and reduction of false positives by shape feature. Parameter values used in each step of the procedure are decided based on density and shape features of typical metastatic liver cancers. In experiments using practical 20 cases of metastatic liver tumors, it is shown that 56% of true cancers can be detected successfully from CT images by the proposed procedure. (author)

  17. An automated approach for extracting Barrier Island morphology from digital elevation models

    Science.gov (United States)

    Wernette, Phillipe; Houser, Chris; Bishop, Michael P.

    2016-06-01

    The response and recovery of a barrier island to extreme storms depends on the elevation of the dune base and crest, both of which can vary considerably alongshore and through time. Quantifying the response to and recovery from storms requires that we can first identify and differentiate the dune(s) from the beach and back-barrier, which in turn depends on accurate identification and delineation of the dune toe, crest and heel. The purpose of this paper is to introduce a multi-scale automated approach for extracting beach, dune (dune toe, dune crest and dune heel), and barrier island morphology. The automated approach introduced here extracts the shoreline and back-barrier shoreline based on elevation thresholds, and extracts the dune toe, dune crest and dune heel based on the average relative relief (RR) across multiple spatial scales of analysis. The multi-scale automated RR approach to extracting dune toe, dune crest, and dune heel based upon relative relief is more objective than traditional approaches because every pixel is analyzed across multiple computational scales and the identification of features is based on the calculated RR values. The RR approach out-performed contemporary approaches and represents a fast objective means to define important beach and dune features for predicting barrier island response to storms. The RR method also does not require that the dune toe, crest, or heel are spatially continuous, which is important because dune morphology is likely naturally variable alongshore.

  18. Establishing a novel automated magnetic bead-based method for the extraction of DNA from a variety of forensic samples.

    Science.gov (United States)

    Witt, Sebastian; Neumann, Jan; Zierdt, Holger; Gébel, Gabriella; Röscheisen, Christiane

    2012-09-01

    Automated systems have been increasingly utilized for DNA extraction by many forensic laboratories to handle growing numbers of forensic casework samples while minimizing the risk of human errors and assuring high reproducibility. The step towards automation however is not easy: The automated extraction method has to be very versatile to reliably prepare high yields of pure genomic DNA from a broad variety of sample types on different carrier materials. To prevent possible cross-contamination of samples or the loss of DNA, the components of the kit have to be designed in a way that allows for the automated handling of the samples with no manual intervention necessary. DNA extraction using paramagnetic particles coated with a DNA-binding surface is predestined for an automated approach. For this study, we tested different DNA extraction kits using DNA-binding paramagnetic particles with regard to DNA yield and handling by a Freedom EVO(®)150 extraction robot (Tecan) equipped with a Te-MagS magnetic separator. Among others, the extraction kits tested were the ChargeSwitch(®)Forensic DNA Purification Kit (Invitrogen), the PrepFiler™Automated Forensic DNA Extraction Kit (Applied Biosystems) and NucleoMag™96 Trace (Macherey-Nagel). After an extensive test phase, we established a novel magnetic bead extraction method based upon the NucleoMag™ extraction kit (Macherey-Nagel). The new method is readily automatable and produces high yields of DNA from different sample types (blood, saliva, sperm, contact stains) on various substrates (filter paper, swabs, cigarette butts) with no evidence of a loss of magnetic beads or sample cross-contamination. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  19. Diagnosis and fault-tolerant control

    CERN Document Server

    Blanke, Mogens; Lunze, Jan; Staroswiecki, Marcel

    2016-01-01

    Fault-tolerant control aims at a gradual shutdown response in automated systems when faults occur. It satisfies the industrial demand for enhanced availability and safety, in contrast to traditional reactions to faults, which bring about sudden shutdowns and loss of availability. The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process that can be used to ensure fault tolerance. It also introduces design methods suitable for diagnostic systems and fault-tolerant controllers for continuous processes that are described by analytical models of discrete-event systems represented by automata. The book is suitable for engineering students, engineers in industry and researchers who wish to get an overview of the variety of approaches to process diagnosis and fault-tolerant contro...

  20. Automated, simple, and efficient influenza RNA extraction from clinical respiratory swabs using TruTip and epMotion.

    Science.gov (United States)

    Griesemer, Sara B; Holmberg, Rebecca; Cooney, Christopher G; Thakore, Nitu; Gindlesperger, Alissa; Knickerbocker, Christopher; Chandler, Darrell P; St George, Kirsten

    2013-09-01

    Rapid, simple and efficient influenza RNA purification from clinical samples is essential for sensitive molecular detection of influenza infection. Automation of the TruTip extraction method can increase sample throughput while maintaining performance. To automate TruTip influenza RNA extraction using an Eppendorf epMotion robotic liquid handler, and to compare its performance to the bioMerieux easyMAG and Qiagen QIAcube instruments. Extraction efficacy and reproducibility of the automated TruTip/epMotion protocol was assessed from influenza-negative respiratory samples spiked with influenza A and B viruses. Clinical extraction performance from 170 influenza A and B-positive respiratory swabs was also evaluated and compared using influenza A and B real-time RT-PCR assays. TruTip/epMotion extraction efficacy was 100% in influenza virus-spiked samples with at least 745 influenza A and 370 influenza B input gene copies per extraction, and exhibited high reproducibility over four log10 concentrations of virus (extraction were also positive following TruTip extraction. Overall Ct value differences obtained between TruTip/epMotion and easyMAG/QIAcube clinical extracts ranged from 1.24 to 1.91. Pairwise comparisons of Ct values showed a high correlation of the TruTip/epMotion protocol to the other methods (R2>0.90). The automated TruTip/epMotion protocol is a simple and rapid extraction method that reproducibly purifies influenza RNA from respiratory swabs, with comparable efficacy and efficiency to both the easyMAG and QIAcube instruments. Copyright © 2013 Elsevier B.V. All rights reserved.

  1. Extraction of repetitive transients with frequency domain multipoint kurtosis for bearing fault diagnosis

    Science.gov (United States)

    Liao, Yuhe; Sun, Peng; Wang, Baoxiang; Qu, Lei

    2018-05-01

    The appearance of repetitive transients in a vibration signal is one typical feature of faulty rolling element bearings. However, accurate extraction of these fault-related characteristic components has always been a challenging task, especially when there is interference from large amplitude impulsive noises. A frequency domain multipoint kurtosis (FDMK)-based fault diagnosis method is proposed in this paper. The multipoint kurtosis is redefined in the frequency domain and the computational accuracy is improved. An envelope autocorrelation function is also presented to estimate the fault characteristic frequency, which is used to set the frequency hunting zone of the FDMK. Then, the FDMK, instead of kurtosis, is utilized to generate a fast kurtogram and only the optimal band with maximum FDMK value is selected for envelope analysis. Negative interference from both large amplitude impulsive noise and shaft rotational speed related harmonic components are therefore greatly reduced. The analysis results of simulation and experimental data verify the capability and feasibility of this FDMK-based method

  2. Natural Environment Modeling and Fault-Diagnosis for Automated Agricultural Vehicle

    DEFF Research Database (Denmark)

    Blas, Morten Rufus; Blanke, Mogens

    2008-01-01

    This paper presents results for an automatic navigation system for agricultural vehicles. The system uses stereo-vision, inertial sensors and GPS. Special emphasis has been placed on modeling the natural environment in conjunction with a fault-tolerant navigation system. The results are exemplified...... by an agricultural vehicle following cut grass (swath). It is demonstrated how faults in the system can be detected and diagnosed using state of the art techniques from fault-tolerant literature. Results in performing fault-diagnosis and fault accomodation are presented using real data....

  3. Distribution network fault section identification and fault location using artificial neural network

    DEFF Research Database (Denmark)

    Dashtdar, Masoud; Dashti, Rahman; Shaker, Hamid Reza

    2018-01-01

    In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics...... components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault...... resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters...

  4. Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme

    Directory of Open Access Journals (Sweden)

    Ghalib R. Ibrahim

    2012-01-01

    Full Text Available Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio, which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal and fault 2 (50% tooth removal under loads (0%, and 80% of the total load. The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.

  5. Extraction of CT dose information from DICOM metadata: automated Matlab-based approach.

    Science.gov (United States)

    Dave, Jaydev K; Gingold, Eric L

    2013-01-01

    The purpose of this study was to extract exposure parameters and dose-relevant indexes of CT examinations from information embedded in DICOM metadata. DICOM dose report files were identified and retrieved from a PACS. An automated software program was used to extract from these files information from the structured elements in the DICOM metadata relevant to exposure. Extracting information from DICOM metadata eliminated potential errors inherent in techniques based on optical character recognition, yielding 100% accuracy.

  6. Fault Tolerant Control System Design Using Automated Methods from Risk Analysis

    DEFF Research Database (Denmark)

    Blanke, M.

    Fault tolerant controls have the ability to be resilient to simple faults in control loop components.......Fault tolerant controls have the ability to be resilient to simple faults in control loop components....

  7. An efficient diagnostic technique for distribution systems based on under fault voltages and currents

    Energy Technology Data Exchange (ETDEWEB)

    Campoccia, A.; Di Silvestre, M.L.; Incontrera, I.; Riva Sanseverino, E. [Dipartimento di Ingegneria Elettrica elettronica e delle Telecomunicazioni, Universita degli Studi di Palermo, viale delle Scienze, 90128 Palermo (Italy); Spoto, G. [Centro per la Ricerca Elettronica in Sicilia, Monreale, Via Regione Siciliana 49, 90046 Palermo (Italy)

    2010-10-15

    Service continuity is one of the major aspects in the definition of the quality of the electrical energy, for this reason the research in the field of faults diagnostic for distribution systems is spreading ever more. Moreover the increasing interest around modern distribution systems automation for management purposes gives faults diagnostics more tools to detect outages precisely and in short times. In this paper, the applicability of an efficient fault location and characterization methodology within a centralized monitoring system is discussed. The methodology, appropriate for any kind of fault, is based on the use of the analytical model of the network lines and uses the fundamental components rms values taken from the transient measures of line currents and voltages at the MV/LV substations. The fault location and identification algorithm, proposed by the authors and suitably restated, has been implemented on a microprocessor-based device that can be installed at each MV/LV substation. The speed and precision of the algorithm have been tested against the errors deriving from the fundamental extraction within the prescribed fault clearing times and against the inherent precision of the electronic device used for computation. The tests have been carried out using Matlab Simulink for simulating the faulted system. (author)

  8. Automated DNA extraction from genetically modified maize using aminosilane-modified bacterial magnetic particles.

    Science.gov (United States)

    Ota, Hiroyuki; Lim, Tae-Kyu; Tanaka, Tsuyoshi; Yoshino, Tomoko; Harada, Manabu; Matsunaga, Tadashi

    2006-09-18

    A novel, automated system, PNE-1080, equipped with eight automated pestle units and a spectrophotometer was developed for genomic DNA extraction from maize using aminosilane-modified bacterial magnetic particles (BMPs). The use of aminosilane-modified BMPs allowed highly accurate DNA recovery. The (A(260)-A(320)):(A(280)-A(320)) ratio of the extracted DNA was 1.9+/-0.1. The DNA quality was sufficiently pure for PCR analysis. The PNE-1080 offered rapid assay completion (30 min) with high accuracy. Furthermore, the results of real-time PCR confirmed that our proposed method permitted the accurate determination of genetically modified DNA composition and correlated well with results obtained by conventional cetyltrimethylammonium bromide (CTAB)-based methods.

  9. Automated visual inspection of brake shoe wear

    Science.gov (United States)

    Lu, Shengfang; Liu, Zhen; Nan, Guo; Zhang, Guangjun

    2015-10-01

    With the rapid development of high-speed railway, the automated fault inspection is necessary to ensure train's operation safety. Visual technology is paid more attention in trouble detection and maintenance. For a linear CCD camera, Image alignment is the first step in fault detection. To increase the speed of image processing, an improved scale invariant feature transform (SIFT) method is presented. The image is divided into multiple levels of different resolution. Then, we do not stop to extract the feature from the lowest resolution to the highest level until we get sufficient SIFT key points. At that level, the image is registered and aligned quickly. In the stage of inspection, we devote our efforts to finding the trouble of brake shoe, which is one of the key components in brake system on electrical multiple units train (EMU). Its pre-warning on wear limitation is very important in fault detection. In this paper, we propose an automatic inspection approach to detect the fault of brake shoe. Firstly, we use multi-resolution pyramid template matching technology to fast locate the brake shoe. Then, we employ Hough transform to detect the circles of bolts in brake region. Due to the rigid characteristic of structure, we can identify whether the brake shoe has a fault. The experiments demonstrate that the way we propose has a good performance, and can meet the need of practical applications.

  10. Accelerated solvent extraction (ASE) - a fast and automated technique with low solvent consumption for the extraction of solid samples (T12)

    International Nuclear Information System (INIS)

    Hoefler, F.

    2002-01-01

    Full text: Accelerated solvent extraction (ASE) is a modern extraction technique that significantly streamlines sample preparation. A common organic solvent as well as water is used as extraction solvent at elevated temperature and pressure to increase extraction speed and efficiency. The entire extraction process is fully automated and performed within 15 minutes with a solvent consumption of 18 ml for a 10 g sample. For many matrices and for a variety of solutes, ASE has proven to be equivalent or superior to sonication, Soxhlet, and reflux extraction techniques while requiring less time, solvent and labor. First ASE has been applied for the extraction of environmental hazards from solid matrices. Within a very short time ASE was approved by the U.S. EPA for the extraction of BNAs, PAHs, PCBs, pesticides, herbicides, TPH, and dioxins from solid samples in method 3545. Especially for the extraction of dioxins the extraction time with ASE is reduced to 20 minutes in comparison to 18 h using Soxhlet. In food analysis ASE is used for the extraction of pesticide and mycotoxin residues from fruits and vegetables, the fat determination and extraction of vitamins. Time consuming and solvent intensive methods for the extraction of additives from polymers as well as for the extraction of marker compounds from herbal supplements can be performed with higher efficiencies using ASE. For the analysis of chemical weapons the extraction process and sample clean-up including derivatization can be automated and combined with GC-MS using an online ASE-APEC-GC system. (author)

  11. Fully Automated Electro Membrane Extraction Autosampler for LC-MS Systems Allowing Soft Extractions for High-Throughput Applications

    DEFF Research Database (Denmark)

    Fuchs, David; Pedersen-Bjergaard, Stig; Jensen, Henrik

    2016-01-01

    was optimized for soft extraction of analytes and high sample throughput. Further, it was demonstrated that by flushing the EME-syringe with acidic wash buffer and reverting the applied electric potential, carry-over between samples can be reduced to below 1%. Performance of the system was characterized (RSD......, a complete analytical workflow of purification, separation, and analysis of sample could be achieved within only 5.5 min. With the developed system large sequences of samples could be analyzed in a completely automated manner. This high degree of automation makes the developed EME-autosampler a powerful tool...

  12. Automated microfluidic devices integrating solid-phase extraction, fluorescent labeling, and microchip electrophoresis for preterm birth biomarker analysis.

    Science.gov (United States)

    Sahore, Vishal; Sonker, Mukul; Nielsen, Anna V; Knob, Radim; Kumar, Suresh; Woolley, Adam T

    2018-01-01

    We have developed multichannel integrated microfluidic devices for automated preconcentration, labeling, purification, and separation of preterm birth (PTB) biomarkers. We fabricated multilayer poly(dimethylsiloxane)-cyclic olefin copolymer (PDMS-COC) devices that perform solid-phase extraction (SPE) and microchip electrophoresis (μCE) for automated PTB biomarker analysis. The PDMS control layer had a peristaltic pump and pneumatic valves for flow control, while the PDMS fluidic layer had five input reservoirs connected to microchannels and a μCE system. The COC layers had a reversed-phase octyl methacrylate porous polymer monolith for SPE and fluorescent labeling of PTB biomarkers. We determined μCE conditions for two PTB biomarkers, ferritin (Fer) and corticotropin-releasing factor (CRF). We used these integrated microfluidic devices to preconcentrate and purify off-chip-labeled Fer and CRF in an automated fashion. Finally, we performed a fully automated on-chip analysis of unlabeled PTB biomarkers, involving SPE, labeling, and μCE separation with 1 h total analysis time. These integrated systems have strong potential to be combined with upstream immunoaffinity extraction, offering a compact sample-to-answer biomarker analysis platform. Graphical abstract Pressure-actuated integrated microfluidic devices have been developed for automated solid-phase extraction, fluorescent labeling, and microchip electrophoresis of preterm birth biomarkers.

  13. Evaluating Fault Management Operations Concepts for Next-Generation Spacecraft: What Eye Movements Tell Us

    Science.gov (United States)

    Hayashi, Miwa; Ravinder, Ujwala; McCann, Robert S.; Beutter, Brent; Spirkovska, Lily

    2009-01-01

    Performance enhancements associated with selected forms of automation were quantified in a recent human-in-the-loop evaluation of two candidate operational concepts for fault management on next-generation spacecraft. The baseline concept, called Elsie, featured a full-suite of "soft" fault management interfaces. However, operators were forced to diagnose malfunctions with minimal assistance from the standalone caution and warning system. The other concept, called Besi, incorporated a more capable C&W system with an automated fault diagnosis capability. Results from analyses of participants' eye movements indicate that the greatest empirical benefit of the automation stemmed from eliminating the need for text processing on cluttered, text-rich displays.

  14. How do normal faults grow?

    OpenAIRE

    Blækkan, Ingvild; Bell, Rebecca; Rotevatn, Atle; Jackson, Christopher; Tvedt, Anette

    2018-01-01

    Faults grow via a sympathetic increase in their displacement and length (isolated fault model), or by rapid length establishment and subsequent displacement accrual (constant-length fault model). To test the significance and applicability of these two models, we use time-series displacement (D) and length (L) data extracted for faults from nature and experiments. We document a range of fault behaviours, from sympathetic D-L fault growth (isolated growth) to sub-vertical D-L growth trajectorie...

  15. Critical Evaluation of Validation Rules Automated Extraction from Data

    Directory of Open Access Journals (Sweden)

    David Pejcoch

    2014-10-01

    Full Text Available The goal of this article is to critically evaluate a possibility of automatic extraction of such kind of rules which could be later used within a Data Quality Management process for validation of records newly incoming to Information System. For practical demonstration the 4FT-Miner procedure implemented in LISpMiner System was chosen. A motivation for this task is the potential simplification of projects focused on Data Quality Management. Initially, this article is going to critically evaluate a possibility of fully automated extraction with the aim to identify strengths and weaknesses of this approach in comparison to its alternative, when at least some a priori knowledge is available. As a result of practical implementation, this article provides design of recommended process which would be used as a guideline for future projects. Also the question of how to store and maintain extracted rules and how to integrate them with existing tools supporting Data Quality Management is discussed

  16. Development of an Automated Technique for Failure Modes and Effect Analysis

    DEFF Research Database (Denmark)

    Blanke, M.; Borch, Ole; Allasia, G.

    1999-01-01

    Advances in automation have provided integration of monitoring and control functions to enhance the operator's overview and ability to take remedy actions when faults occur. Automation in plant supervision is technically possible with integrated automation systems as platforms, but new design...... methods are needed to cope efficiently with the complexity and to ensure that the functionality of a supervisor is correct and consistent. In particular these methods are expected to significantly improve fault tolerance of the designed systems. The purpose of this work is to develop a software module...... implementing an automated technique for Failure Modes and Effects Analysis (FMEA). This technique is based on the matrix formulation of FMEA for the investigation of failure propagation through a system. As main result, this technique will provide the design engineer with decision tables for fault handling...

  17. Development of an automated technique for failure modes and effect analysis

    DEFF Research Database (Denmark)

    Blanke, Mogens; Borch, Ole; Bagnoli, F.

    1999-01-01

    Advances in automation have provided integration of monitoring and control functions to enhance the operator's overview and ability to take remedy actions when faults occur. Automation in plant supervision is technically possible with integrated automation systems as platforms, but new design...... methods are needed to cope efficiently with the complexity and to ensure that the functionality of a supervisor is correct and consistent. In particular these methods are expected to significantly improve fault tolerance of the designed systems. The purpose of this work is to develop a software module...... implementing an automated technique for Failure Modes and Effects Analysis (FMEA). This technique is based on the matrix formulation of FMEA for the investigation of failure propagation through a system. As main result, this technique will provide the design engineer with decision tables for fault handling...

  18. Automated DNA extraction platforms offer solutions to challenges of assessing microbial biofouling in oil production facilities.

    Science.gov (United States)

    Oldham, Athenia L; Drilling, Heather S; Stamps, Blake W; Stevenson, Bradley S; Duncan, Kathleen E

    2012-11-20

    The analysis of microbial assemblages in industrial, marine, and medical systems can inform decisions regarding quality control or mitigation. Modern molecular approaches to detect, characterize, and quantify microorganisms provide rapid and thorough measures unbiased by the need for cultivation. The requirement of timely extraction of high quality nucleic acids for molecular analysis is faced with specific challenges when used to study the influence of microorganisms on oil production. Production facilities are often ill equipped for nucleic acid extraction techniques, making the preservation and transportation of samples off-site a priority. As a potential solution, the possibility of extracting nucleic acids on-site using automated platforms was tested. The performance of two such platforms, the Fujifilm QuickGene-Mini80™ and Promega Maxwell®16 was compared to a widely used manual extraction kit, MOBIO PowerBiofilm™ DNA Isolation Kit, in terms of ease of operation, DNA quality, and microbial community composition. Three pipeline biofilm samples were chosen for these comparisons; two contained crude oil and corrosion products and the third transported seawater. Overall, the two more automated extraction platforms produced higher DNA yields than the manual approach. DNA quality was evaluated for amplification by quantitative PCR (qPCR) and end-point PCR to generate 454 pyrosequencing libraries for 16S rRNA microbial community analysis. Microbial community structure, as assessed by DGGE analysis and pyrosequencing, was comparable among the three extraction methods. Therefore, the use of automated extraction platforms should enhance the feasibility of rapidly evaluating microbial biofouling at remote locations or those with limited resources.

  19. Fault Tolerant Control Systems

    DEFF Research Database (Denmark)

    Bøgh, S. A.

    This thesis considered the development of fault tolerant control systems. The focus was on the category of automated processes that do not necessarily comprise a high number of identical sensors and actuators to maintain safe operation, but still have a potential for improving immunity to component...

  20. Semi-automated set-up for exhaustive micro-electromembrane extractions of basic drugs from biological fluids

    Czech Academy of Sciences Publication Activity Database

    Dvořák, Miloš; Seip, K. F.; Pedersen-Bjergaard, S.; Kubáň, Pavel

    2018-01-01

    Roč. 1005, APR (2018), s. 34-42 ISSN 0003-2670 R&D Projects: GA ČR(CZ) GA16-09135S Institutional support: RVO:68081715 Keywords : electromembrane extraction * exhaustive extraction * automation Subject RIV: CB - Analytical Chemistry, Separation OBOR OECD: Analytical chemistry Impact factor: 4.950, year: 2016

  1. A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform

    Directory of Open Access Journals (Sweden)

    Xiao Yu

    2015-11-01

    Full Text Available Because roller element bearings (REBs failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT. In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS into window spectrums, following which Rand Index (RI criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs. Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines. The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU. The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault

  2. An automatic fault management model for distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Lehtonen, M; Haenninen, S [VTT Energy, Espoo (Finland); Seppaenen, M [North-Carelian Power Co (Finland); Antila, E; Markkila, E [ABB Transmit Oy (Finland)

    1998-08-01

    An automatic computer model, called the FI/FL-model, for fault location, fault isolation and supply restoration is presented. The model works as an integrated part of the substation SCADA, the AM/FM/GIS system and the medium voltage distribution network automation systems. In the model, three different techniques are used for fault location. First, by comparing the measured fault current to the computed one, an estimate for the fault distance is obtained. This information is then combined, in order to find the actual fault point, with the data obtained from the fault indicators in the line branching points. As a third technique, in the absence of better fault location data, statistical information of line section fault frequencies can also be used. For combining the different fault location information, fuzzy logic is used. As a result, the probability weights for the fault being located in different line sections, are obtained. Once the faulty section is identified, it is automatically isolated by remote control of line switches. Then the supply is restored to the remaining parts of the network. If needed, reserve connections from other adjacent feeders can also be used. During the restoration process, the technical constraints of the network are checked. Among these are the load carrying capacity of line sections, voltage drop and the settings of relay protection. If there are several possible network topologies, the model selects the technically best alternative. The FI/IL-model has been in trial use at two substations of the North-Carelian Power Company since November 1996. This chapter lists the practical experiences during the test use period. Also the benefits of this kind of automation are assessed and future developments are outlined

  3. Doping control in Japan. An automated extraction procedure for the doping test.

    Science.gov (United States)

    Nakajima, T.; Matsumoto, T.

    1976-01-01

    Horse racing in Japan consists of two systems, the National (10 racecourses) and the Regional public racing (32 racecourses) having about 2,500 racing meetings in total per year. Urine or saliva samples for dope testing are collected by the officials from thw winner, second and third, and transported to the laboratory in a frozen state. In 1975, 76, 117 samples were analyzed by this laboratory. The laboratory provides the following four methods of analysis, which are variously combined by request. (1) Method for detection of drugs extracted by chloroform from alkalinized sample. (2) Methods for detection of camphor and its derivatives. (3) Method for detection of barbiturates. (4) Method for detection of ethanol. These methods consist of screening, mainly by thin layer chromatography and confirmatory tests using ultra violet spectrophotometry, gas chromatography and mass spectrometry combined with gas chromatography. In the screening test of doping drugs, alkalinized samples are extracted with chloroform. In order to automate the extraction procedure, the authors contrived a new automatic extractor. They also devised a means of pH adjustment of horse urine by using buffer solution and an efficient mechanism of evaporation of organic solvent. Analytical data obtained by the automatic extractor are presented in this paper. In 1972, we started research work to automate the extraction procedure in method (1) above, and the Automatic Extractor has been in use in routine work since last July. One hundred and twnety samples per hour are extracted automatically by three automatic extractors. The analytical data using this apparatus is presented below. PMID:1000163

  4. A System for Fault Management and Fault Consequences Analysis for NASA's Deep Space Habitat

    Science.gov (United States)

    Colombano, Silvano; Spirkovska, Liljana; Baskaran, Vijaykumar; Aaseng, Gordon; McCann, Robert S.; Ossenfort, John; Smith, Irene; Iverson, David L.; Schwabacher, Mark

    2013-01-01

    NASA's exploration program envisions the utilization of a Deep Space Habitat (DSH) for human exploration of the space environment in the vicinity of Mars and/or asteroids. Communication latencies with ground control of as long as 20+ minutes make it imperative that DSH operations be highly autonomous, as any telemetry-based detection of a systems problem on Earth could well occur too late to assist the crew with the problem. A DSH-based development program has been initiated to develop and test the automation technologies necessary to support highly autonomous DSH operations. One such technology is a fault management tool to support performance monitoring of vehicle systems operations and to assist with real-time decision making in connection with operational anomalies and failures. Toward that end, we are developing Advanced Caution and Warning System (ACAWS), a tool that combines dynamic and interactive graphical representations of spacecraft systems, systems modeling, automated diagnostic analysis and root cause identification, system and mission impact assessment, and mitigation procedure identification to help spacecraft operators (both flight controllers and crew) understand and respond to anomalies more effectively. In this paper, we describe four major architecture elements of ACAWS: Anomaly Detection, Fault Isolation, System Effects Analysis, and Graphic User Interface (GUI), and how these elements work in concert with each other and with other tools to provide fault management support to both the controllers and crew. We then describe recent evaluations and tests of ACAWS on the DSH testbed. The results of these tests support the feasibility and strength of our approach to failure management automation and enhanced operational autonomy

  5. Fault Tolerant Position-mooring Control for Offshore Vessels

    DEFF Research Database (Denmark)

    Blanke, Mogens; Nguyen, Trong Dong

    2018-01-01

    Fault-tolerance is crucial to maintain safety in offshore operations. The objective of this paper is to show how systematic analysis and design of fault-tolerance is conducted for a complex automation system, exemplified by thruster assisted Position-mooring. Using redundancy as required....... Functional faults that are only detectable, are rendered isolable through an active isolation approach. Once functional faults are isolated, they are handled by fault accommodation techniques to meet overall control objectives specified by class requirements. The paper illustrates the generic methodology...... by a system to handle faults in mooring lines, sensors or thrusters. Simulations and model basin experiments are carried out to validate the concept for scenarios with single or multiple faults. The results demonstrate that enhanced availability and safety are obtainable with this design approach. While...

  6. Development and Test of Methods for Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Jørgensen, R.B.

    Almost all industrial systemns are automated to ensure optimal production both in relation to energy consumtion and safety to equipment and humans. All working parts are individually subject to faults. This can lead to unacceptable economic loss or injury to people. This thesis deals with a monit......Almost all industrial systemns are automated to ensure optimal production both in relation to energy consumtion and safety to equipment and humans. All working parts are individually subject to faults. This can lead to unacceptable economic loss or injury to people. This thesis deals...

  7. Development of an automated sequential injection on-line solvent extraction-back extraction procedure as demonstrated for the determination of cadmium with detection by electrothermal atomic absorption spectrometry

    DEFF Research Database (Denmark)

    Wang, Jianhua; Hansen, Elo Harald

    2002-01-01

    An automated sequential injection (SI) on-line solvent extraction-back extraction separation/preconcentration procedure is described. Demonstrated for the assay of cadmium by electrothermal atomic absorption spectrometry (ETAAS), the analyte is initially complexed with ammonium pyrrolidinedithioc......An automated sequential injection (SI) on-line solvent extraction-back extraction separation/preconcentration procedure is described. Demonstrated for the assay of cadmium by electrothermal atomic absorption spectrometry (ETAAS), the analyte is initially complexed with ammonium....../preconcentration process of the ensuing sample. An enrichment factor of 21.4, a detection limit of 2.7 ng/l, along with a sampling frequency of 13s/h were obtained at a sample flow rate of 6.0mlmin/sup -1/. The precision (R.S.D.) at the 0.4 mug/l level was 1.8% as compared to 3.2% when quantifying the organic extractant...

  8. High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process☆

    Institute of Scientific and Technical Information of China (English)

    Ying Tian; Wenli Du; Feng Qian

    2015-01-01

    Purified terephthalic acid (PTA) is an important chemical raw material. P-xylene (PX) is transformed to terephthalic acid (TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to im-prove the product quality, as wel as to visualize the fault type clearly, a fault diagnosis method based on self-organizing map (SOM) and high dimensional feature extraction method, local tangent space alignment (LTSA), is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously, and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process in-dicate that the LTSA–SOM can wel detect and visualize the fault type.

  9. Extraction of prostatic lumina and automated recognition for prostatic calculus image using PCA-SVM.

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi.

  10. Extraction of Prostatic Lumina and Automated Recognition for Prostatic Calculus Image Using PCA-SVM

    Science.gov (United States)

    Wang, Zhuocai; Xu, Xiangmin; Ding, Xiaojun; Xiao, Hui; Huang, Yusheng; Liu, Jian; Xing, Xiaofen; Wang, Hua; Liao, D. Joshua

    2011-01-01

    Identification of prostatic calculi is an important basis for determining the tissue origin. Computation-assistant diagnosis of prostatic calculi may have promising potential but is currently still less studied. We studied the extraction of prostatic lumina and automated recognition for calculus images. Extraction of lumina from prostate histology images was based on local entropy and Otsu threshold recognition using PCA-SVM and based on the texture features of prostatic calculus. The SVM classifier showed an average time 0.1432 second, an average training accuracy of 100%, an average test accuracy of 93.12%, a sensitivity of 87.74%, and a specificity of 94.82%. We concluded that the algorithm, based on texture features and PCA-SVM, can recognize the concentric structure and visualized features easily. Therefore, this method is effective for the automated recognition of prostatic calculi. PMID:21461364

  11. Automatic location of short circuit faults

    Energy Technology Data Exchange (ETDEWEB)

    Lehtonen, M. [VTT Energy, Espoo (Finland); Hakola, T.; Antila, E. [ABB Power Oy, Helsinki (Finland); Seppaenen, M. [North-Carelian Power Company (Finland)

    1996-12-31

    In this presentation, the automatic location of short circuit faults on medium voltage distribution lines, based on the integration of computer systems of medium voltage distribution network automation is discussed. First the distribution data management systems and their interface with the substation telecontrol, or SCADA systems, is studied. Then the integration of substation telecontrol system and computerised relay protection is discussed. Finally, the implementation of the fault location system is presented and the practical experience with the system is discussed

  12. Automatic location of short circuit faults

    Energy Technology Data Exchange (ETDEWEB)

    Lehtonen, M [VTT Energy, Espoo (Finland); Hakola, T; Antila, E [ABB Power Oy (Finland); Seppaenen, M [North-Carelian Power Company (Finland)

    1998-08-01

    In this chapter, the automatic location of short circuit faults on medium voltage distribution lines, based on the integration of computer systems of medium voltage distribution network automation is discussed. First the distribution data management systems and their interface with the substation telecontrol, or SCADA systems, is studied. Then the integration of substation telecontrol system and computerized relay protection is discussed. Finally, the implementation of the fault location system is presented and the practical experience with the system is discussed

  13. Automatic location of short circuit faults

    Energy Technology Data Exchange (ETDEWEB)

    Lehtonen, M [VTT Energy, Espoo (Finland); Hakola, T; Antila, E [ABB Power Oy, Helsinki (Finland); Seppaenen, M [North-Carelian Power Company (Finland)

    1997-12-31

    In this presentation, the automatic location of short circuit faults on medium voltage distribution lines, based on the integration of computer systems of medium voltage distribution network automation is discussed. First the distribution data management systems and their interface with the substation telecontrol, or SCADA systems, is studied. Then the integration of substation telecontrol system and computerised relay protection is discussed. Finally, the implementation of the fault location system is presented and the practical experience with the system is discussed

  14. Observer-based Fault Detection and Isolation for Nonlinear Systems

    DEFF Research Database (Denmark)

    Lootsma, T.F.

    With the rise in automation the increase in fault detectionand isolation & reconfiguration is inevitable. Interest in fault detection and isolation (FDI) for nonlinear systems has grown significantly in recent years. The design of FDI is motivated by the need for knowledge about occurring faults...... in fault-tolerant control systems (FTC systems). The idea of FTC systems is to detect, isolate, and handle faults in such a way that the systems can still perform in a required manner. One prefers reduced performance after occurrence of a fault to the shut down of (sub-) systems. Hence, the idea of fault......-output decoupling is described. It is a new idea based on the solution of the input-output decoupling problem. The idea is to include FDI considerations already during the control design....

  15. Fault detection and diagnosis for complex multivariable processes using neural networks

    International Nuclear Information System (INIS)

    Weerasinghe, M.

    1998-06-01

    the complex input-output mapping performed by a network, and are in general difficult to obtain. Statistical techniques and relationships between fuzzy systems and standard radial basis function networks have been exploited to prune a trained network and to extract qualitative rules that explain the network operation for fault diagnosis. Pruning the networks improved the fault classification, while offering simple qualitative rules on process behaviour. Automation of the pruning procedure introduced flexibility and ease of application of the methods. (author)

  16. Toward Expanding Tremor Observations in the Northern San Andreas Fault System in the 1990s

    Science.gov (United States)

    Damiao, L. G.; Dreger, D. S.; Nadeau, R. M.; Taira, T.; Guilhem, A.; Luna, B.; Zhang, H.

    2015-12-01

    The connection between tremor activity and active fault processes continues to expand our understanding of deep fault zone properties and deformation, the tectonic process, and the relationship of tremor to the occurrence of larger earthquakes. Compared to tremors in subduction zones, known tremor signals in California are ~5 to ~10 smaller in amplitude and duration. These characteristics, in addition to scarce geographic coverage, lack of continuous data (e.g., before mid-2001 at Parkfield), and absence of instrumentation sensitive enough to monitor these events have stifled tremor detection. The continuous monitoring of these events over a relatively short time period in limited locations may lead to a parochial view of the tremor phenomena and its relationship to fault, tectonic, and earthquake processes. To help overcome this, we have embarked on a project to expand the geographic and temporal scope of tremor observation along the Northern SAF system using available continuous seismic recordings from a broad array of 100s of surface seismic stations from multiple seismic networks. Available data for most of these stations also extends back into the mid-1990s. Processing and analysis of tremor signal from this large and low signal-to-noise dataset requires a heavily automated, data-science type approach and specialized techniques for identifying and extracting reliable data. We report here on the automated, envelope based methodology we have developed. We finally compare our catalog results with pre-existing tremor catalogs in the Parkfield area.

  17. Automated Feature Extraction of Foredune Morphology from Terrestrial Lidar Data

    Science.gov (United States)

    Spore, N.; Brodie, K. L.; Swann, C.

    2014-12-01

    Foredune morphology is often described in storm impact prediction models using the elevation of the dune crest and dune toe and compared with maximum runup elevations to categorize the storm impact and predicted responses. However, these parameters do not account for other foredune features that may make them more or less erodible, such as alongshore variations in morphology, vegetation coverage, or compaction. The goal of this work is to identify other descriptive features that can be extracted from terrestrial lidar data that may affect the rate of dune erosion under wave attack. Daily, mobile-terrestrial lidar surveys were conducted during a 6-day nor'easter (Hs = 4 m in 6 m water depth) along 20km of coastline near Duck, North Carolina which encompassed a variety of foredune forms in close proximity to each other. This abstract will focus on the tools developed for the automated extraction of the morphological features from terrestrial lidar data, while the response of the dune will be presented by Brodie and Spore as an accompanying abstract. Raw point cloud data can be dense and is often under-utilized due to time and personnel constraints required for analysis, since many algorithms are not fully automated. In our approach, the point cloud is first projected into a local coordinate system aligned with the coastline, and then bare earth points are interpolated onto a rectilinear 0.5 m grid creating a high resolution digital elevation model. The surface is analyzed by identifying features along each cross-shore transect. Surface curvature is used to identify the position of the dune toe, and then beach and berm morphology is extracted shoreward of the dune toe, and foredune morphology is extracted landward of the dune toe. Changes in, and magnitudes of, cross-shore slope, curvature, and surface roughness are used to describe the foredune face and each cross-shore transect is then classified using its pre-storm morphology for storm-response analysis.

  18. Automated extraction of knowledge for model-based diagnostics

    Science.gov (United States)

    Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.

    1990-01-01

    The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

  19. Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-01

    Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

  20. Fault Monitoring and Re-Configurable Control for a Ship Propulsion Plant

    DEFF Research Database (Denmark)

    Blanke, M.; Izadi-Zamanabadi, Roozbeh; Lootsma, T.F.

    1998-01-01

    Minor faults in ship propulsion and their associated automation systems can cause dramatic reduction on ships' ability to propel and manoeuvre, and effective means are needed to prevent that simple faults develop into severe failure. The paper analyses the control system for a propulsion plant on...

  1. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

    Science.gov (United States)

    Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

    2015-07-06

    Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

  2. An approach to the verification of a fault-tolerant, computer-based reactor safety system: A case study using automated reasoning: Volume 1: Interim report

    International Nuclear Information System (INIS)

    Chisholm, G.H.; Kljaich, J.; Smith, B.T.; Wojcik, A.S.

    1987-01-01

    The purpose of this project is to explore the feasibility of automating the verification process for computer systems. The intent is to demonstrate that both the software and hardware that comprise the system meet specified availability and reliability criteria, that is, total design analysis. The approach to automation is based upon the use of Automated Reasoning Software developed at Argonne National Laboratory. This approach is herein referred to as formal analysis and is based on previous work on the formal verification of digital hardware designs. Formal analysis represents a rigorous evaluation which is appropriate for system acceptance in critical applications, such as a Reactor Safety System (RSS). This report describes a formal analysis technique in the context of a case study, that is, demonstrates the feasibility of applying formal analysis via application. The case study described is based on the Reactor Safety System (RSS) for the Experimental Breeder Reactor-II (EBR-II). This is a system where high reliability and availability are tantamount to safety. The conceptual design for this case study incorporates a Fault-Tolerant Processor (FTP) for the computer environment. An FTP is a computer which has the ability to produce correct results even in the presence of any single fault. This technology was selected as it provides a computer-based equivalent to the traditional analog based RSSs. This provides a more conservative design constraint than that imposed by the IEEE Standard, Criteria For Protection Systems For Nuclear Power Generating Stations (ANSI N42.7-1972)

  3. Automated extraction of chemical structure information from digital raster images

    Directory of Open Access Journals (Sweden)

    Shedden Kerby A

    2009-02-01

    Full Text Available Abstract Background To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader – a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links

  4. Automated Extraction of Substance Use Information from Clinical Texts.

    Science.gov (United States)

    Wang, Yan; Chen, Elizabeth S; Pakhomov, Serguei; Arsoniadis, Elliot; Carter, Elizabeth W; Lindemann, Elizabeth; Sarkar, Indra Neil; Melton, Genevieve B

    2015-01-01

    Within clinical discourse, social history (SH) includes important information about substance use (alcohol, drug, and nicotine use) as key risk factors for disease, disability, and mortality. In this study, we developed and evaluated a natural language processing (NLP) system for automated detection of substance use statements and extraction of substance use attributes (e.g., temporal and status) based on Stanford Typed Dependencies. The developed NLP system leveraged linguistic resources and domain knowledge from a multi-site social history study, Propbank and the MiPACQ corpus. The system attained F-scores of 89.8, 84.6 and 89.4 respectively for alcohol, drug, and nicotine use statement detection, as well as average F-scores of 82.1, 90.3, 80.8, 88.7, 96.6, and 74.5 respectively for extraction of attributes. Our results suggest that NLP systems can achieve good performance when augmented with linguistic resources and domain knowledge when applied to a wide breadth of substance use free text clinical notes.

  5. A multi-atlas based method for automated anatomical rat brain MRI segmentation and extraction of PET activity.

    Science.gov (United States)

    Lancelot, Sophie; Roche, Roxane; Slimen, Afifa; Bouillot, Caroline; Levigoureux, Elise; Langlois, Jean-Baptiste; Zimmer, Luc; Costes, Nicolas

    2014-01-01

    Preclinical in vivo imaging requires precise and reproducible delineation of brain structures. Manual segmentation is time consuming and operator dependent. Automated segmentation as usually performed via single atlas registration fails to account for anatomo-physiological variability. We present, evaluate, and make available a multi-atlas approach for automatically segmenting rat brain MRI and extracting PET activies. High-resolution 7T 2DT2 MR images of 12 Sprague-Dawley rat brains were manually segmented into 27-VOI label volumes using detailed protocols. Automated methods were developed with 7/12 atlas datasets, i.e. the MRIs and their associated label volumes. MRIs were registered to a common space, where an MRI template and a maximum probability atlas were created. Three automated methods were tested: 1/registering individual MRIs to the template, and using a single atlas (SA), 2/using the maximum probability atlas (MP), and 3/registering the MRIs from the multi-atlas dataset to an individual MRI, propagating the label volumes and fusing them in individual MRI space (propagation & fusion, PF). Evaluation was performed on the five remaining rats which additionally underwent [18F]FDG PET. Automated and manual segmentations were compared for morphometric performance (assessed by comparing volume bias and Dice overlap index) and functional performance (evaluated by comparing extracted PET measures). Only the SA method showed volume bias. Dice indices were significantly different between methods (PF>MP>SA). PET regional measures were more accurate with multi-atlas methods than with SA method. Multi-atlas methods outperform SA for automated anatomical brain segmentation and PET measure's extraction. They perform comparably to manual segmentation for FDG-PET quantification. Multi-atlas methods are suitable for rapid reproducible VOI analyses.

  6. Research on fault diagnosis for RCP rotor based on wavelet analysis

    International Nuclear Information System (INIS)

    Chen Zhihui; Xia Hong; Wang Taotao

    2008-01-01

    Wavelet analysis is with the characteristics of noise reduction and multiscale resolution, and can be used to effectively extract the fault features of the typical failures of the main pumps. Simulink is used to simulate the typical faults: Misalignment Fault, Crackle Fault of rotor, and Initial Bending Fault, then the Wavelet method is used to analyze the vibration signal. The result shows that the extracted fault feature from wavelet analysis can effectively identify the fault signals. The Wavelet analysis is a practical method for the diagnosis of main coolant pump failure, and is with certain value for application and significance. (authors)

  7. Automated diagnosis of rolling bearings using MRA and neural networks

    Science.gov (United States)

    Castejón, C.; Lara, O.; García-Prada, J. C.

    2010-01-01

    Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in productive processes are caused by bearings. They begin to deteriorate from early stages of their functional life, also called the incipient level. This manuscript develops an automated diagnosis of rolling bearings based on the analysis and classification of signature vibrations. The novelty of this work is the application of the methodology proposed for data collected from a quasi-real industrial machine, where rolling bearings support the radial and axial loads the bearings are designed for. Multiresolution analysis (MRA) is used in a first stage in order to extract the most interesting features from signals. Features will be used in a second stage as inputs of a supervised neural network (NN) for classification purposes. Experimental results carried out in a real system show the soundness of the method which detects four bearing conditions (normal, inner race fault, outer race fault and ball fault) in a very incipient stage.

  8. Application of support vector machine based on pattern spectrum entropy in fault diagnostics of rolling element bearings

    International Nuclear Information System (INIS)

    Hao, Rujiang; Chu, Fulei; Peng, Zhike; Feng, Zhipeng

    2011-01-01

    This paper presents a novel pattern classification approach for the fault diagnostics of rolling element bearings, which combines the morphological multi-scale analysis and the 'one to others' support vector machine (SVM) classifiers. The morphological pattern spectrum describes the shape characteristics of the inspected signal based on the morphological opening operation with multi-scale structuring elements. The pattern spectrum entropy and the barycenter scale location of the spectrum curve are extracted as the feature vectors presenting different faults of the bearing, which are more effective and representative than the kurtosis and the enveloping demodulation spectrum. The 'one to others' SVM algorithm is adopted to distinguish six kinds of fault signals which were measured in the experimental test rig under eight different working conditions. The recognition results of the SVM are ideal and more precise than those of the artificial neural network even though the training samples are few. The combination of the morphological pattern spectrum parameters and the 'one to others' multi-class SVM algorithm is suitable for the on-line automated fault diagnosis of the rolling element bearings. This application is promising and worth well exploiting

  9. Radar Determination of Fault Slip and Location in Partially Decorrelated Images

    Science.gov (United States)

    Parker, Jay; Glasscoe, Margaret; Donnellan, Andrea; Stough, Timothy; Pierce, Marlon; Wang, Jun

    2017-06-01

    Faced with the challenge of thousands of frames of radar interferometric images, automated feature extraction promises to spur data understanding and highlight geophysically active land regions for further study. We have developed techniques for automatically determining surface fault slip and location using deformation images from the NASA Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), which is similar to satellite-based SAR but has more mission flexibility and higher resolution (pixels are approximately 7 m). This radar interferometry provides a highly sensitive method, clearly indicating faults slipping at levels of 10 mm or less. But interferometric images are subject to decorrelation between revisit times, creating spots of bad data in the image. Our method begins with freely available data products from the UAVSAR mission, chiefly unwrapped interferograms, coherence images, and flight metadata. The computer vision techniques we use assume no data gaps or holes; so a preliminary step detects and removes spots of bad data and fills these holes by interpolation and blurring. Detected and partially validated surface fractures from earthquake main shocks, aftershocks, and aseismic-induced slip are shown for faults in California, including El Mayor-Cucapah (M7.2, 2010), the Ocotillo aftershock (M5.7, 2010), and South Napa (M6.0, 2014). Aseismic slip is detected on the San Andreas Fault from the El Mayor-Cucapah earthquake, in regions of highly patterned partial decorrelation. Validation is performed by comparing slip estimates from two interferograms with published ground truth measurements.

  10. Feature-based handling of surface faults in compact disc players

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Andersen, Palle

    2006-01-01

    In this paper a novel method called feature-based control is presented. The method is designed to improve compact disc players’ handling of surface faults on the discs. The method is based on a fault-tolerant control scheme, which uses extracted features of the surface faults to remove those from...... the detector signals used for control during the occurrence of surface faults. The extracted features are coefficients of Karhunen–Loève approximations of the surface faults. The performance of the feature-based control scheme controlling compact disc players playing discs with surface faults has been...... validated experimentally. The proposed scheme reduces the control errors due to the surface faults, and in some cases where the standard fault handling scheme fails, our scheme keeps the CD-player playing....

  11. Evaluation of automated nucleic acid extraction methods for virus detection in a multicenter comparative trial

    DEFF Research Database (Denmark)

    Rasmussen, Thomas Bruun; Uttenthal, Åse; Hakhverdyan, M.

    2009-01-01

    between the results obtained for the different automated extraction platforms. In particular, the limit of detection was identical for 9/12 and 8/12 best performing robots (using dilutions of BVDV infected-serum and cell culture material, respectively), which was similar to a manual extraction method used......Five European veterinary laboratories participated in an exercise to compare the performance of nucleic acid extraction robots. Identical sets of coded samples were prepared using serial dilutions of bovine viral diarrhoea virus (BVDV) from serum and cell culture propagated material. Each...

  12. Automating the Extraction of Metadata from Archaeological Data Using iRods Rules

    Directory of Open Access Journals (Sweden)

    David Walling

    2011-10-01

    Full Text Available The Texas Advanced Computing Center and the Institute for Classical Archaeology at the University of Texas at Austin developed a method that uses iRods rules and a Jython script to automate the extraction of metadata from digital archaeological data. The first step was to create a record-keeping system to classify the data. The record-keeping system employs file and directory hierarchy naming conventions designed specifically to maintain the relationship between the data objects and map the archaeological documentation process. The metadata implicit in the record-keeping system is automatically extracted upon ingest, combined with additional sources of metadata, and stored alongside the data in the iRods preservation environment. This method enables a more organized workflow for the researchers, helps them archive their data close to the moment of data creation, and avoids error prone manual metadata input. We describe the types of metadata extracted and provide technical details of the extraction process and storage of the data and metadata.

  13. Advanced I&C for Fault-Tolerant Supervisory Control of Small Modular Reactors

    Energy Technology Data Exchange (ETDEWEB)

    Cole, Daniel G. [Univ. of Pittsburgh, PA (United States)

    2018-01-30

    In this research, we have developed a supervisory control approach to enable automated control of SMRs. By design the supervisory control system has an hierarchical, interconnected, adaptive control architecture. A considerable advantage to this architecture is that it allows subsystems to communicate at different/finer granularity, facilitates monitoring of process at the modular and plant levels, and enables supervisory control. We have investigated the deployment of automation, monitoring, and data collection technologies to enable operation of multiple SMRs. Each unit's controller collects and transfers information from local loops and optimize that unit’s parameters. Information is passed from the each SMR unit controller to the supervisory controller, which supervises the actions of SMR units and manage plant processes. The information processed at the supervisory level will provide operators the necessary information needed for reactor, unit, and plant operation. In conjunction with the supervisory effort, we have investigated techniques for fault-tolerant networks, over which information is transmitted between local loops and the supervisory controller to maintain a safe level of operational normalcy in the presence of anomalies. The fault-tolerance of the supervisory control architecture, the network that supports it, and the impact of fault-tolerance on multi-unit SMR plant control has been a second focus of this research. To this end, we have investigated the deployment of advanced automation, monitoring, and data collection and communications technologies to enable operation of multiple SMRs. We have created a fault-tolerant multi-unit SMR supervisory controller that collects and transfers information from local loops, supervise their actions, and adaptively optimize the controller parameters. The goal of this research has been to develop the methodologies and procedures for fault-tolerant supervisory control of small modular reactors. To achieve

  14. Fault Current Characteristics of the DFIG under Asymmetrical Fault Conditions

    Directory of Open Access Journals (Sweden)

    Fan Xiao

    2015-09-01

    Full Text Available During non-severe fault conditions, crowbar protection is not activated and the rotor windings of a doubly-fed induction generator (DFIG are excited by the AC/DC/AC converter. Meanwhile, under asymmetrical fault conditions, the electrical variables oscillate at twice the grid frequency in synchronous dq frame. In the engineering practice, notch filters are usually used to extract the positive and negative sequence components. In these cases, the dynamic response of a rotor-side converter (RSC and the notch filters have a large influence on the fault current characteristics of the DFIG. In this paper, the influence of the notch filters on the proportional integral (PI parameters is discussed and the simplified calculation models of the rotor current are established. Then, the dynamic performance of the stator flux linkage under asymmetrical fault conditions is also analyzed. Based on this, the fault characteristics of the stator current under asymmetrical fault conditions are studied and the corresponding analytical expressions of the stator fault current are obtained. Finally, digital simulation results validate the analytical results. The research results are helpful to meet the requirements of a practical short-circuit calculation and the construction of a relaying protection system for the power grid with penetration of DFIGs.

  15. Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

    Directory of Open Access Journals (Sweden)

    Zhixin Yang

    2013-01-01

    Full Text Available A reliable fault diagnostic system for gas turbine generator system (GTGS, which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

  16. A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

    Science.gov (United States)

    Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

    2015-08-01

    Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

  17. Decision table development and application to the construction of fault trees

    International Nuclear Information System (INIS)

    Salem, S.L.; Wu, J.S.; Apostolakis, G.

    1979-01-01

    A systematic methodology for the construction of fault trees based on the use of decision tables has been developed. These tables are used to describe each possible output state of a component as a set of combinations of states of inputs and internal operational or T states. Two methods for modeling component behavior via decision tables have been developed, one inductive and one deductive. These methods are useful for creating decision tables that realistically model the operational and failure modes of electrical, mechanical, and hydraulic components as well as human interactions inhibit conditions and common-cause events. A computer code CAT (Computer Automated Tree) has been developed to automatically produce fault trees from decision tables. A simple electrical system was chosen to illustrate the basic features of the decision table approach and to provide an example of an actual fault tree produced by this code. This example demonstrates the potential utility of such an automated approach to fault tree construction once a basic set of general decision tables has been developed

  18. Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

    Science.gov (United States)

    Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

    2014-01-01

    In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

  19. Extraction of Citrus Hystrix D.C. (Kaffir Lime) Essential Oil Using Automated Steam Distillation Process: Analysis of Volatile Compounds

    International Nuclear Information System (INIS)

    Nurhani Kasuan; Zuraida Muhammad; Zakiah Yusoff; Mohd Hezri Fazalul Rahiman; Mohd Nasir Taib; Zaibunnisa Abdul Haiyee

    2013-01-01

    An automated steam distillation was successfully used to extract volatiles from Citrus hystrix D.C (Kaffir lime) peels. The automated steam distillation integrated with robust temperature control can commercially produce large amount of essential oil with efficient heating system. Objective of this study is to quantify the oil production rate using automated steam distillation and analyze the composition of volatiles in Kaffir lime peels oil at different controlled and uncontrolled temperature conditions. From the experimentation, oil extraction from Kaffir lime peels only took approximately less than 3 hours with amount of oil yield was 13.4 % more than uncontrolled temperature. The identified major compounds from Kaffir lime peels oil were sabinene, β-pinene, limonene, α-pinene, camphene, myrcene, terpinen-4-ol, α-terpineol, linalool, terpinolene and citronellal which are considered to have good organoleptic quality. In contrast with uncontrolled temperature, oil analysis revealed that some important volatile compounds were absent such as terpinolene, linalool, terpinen-4-ol due to thermal degradation effect from fast heating of extracted material. (author)

  20. Workflow Fault Tree Generation Through Model Checking

    DEFF Research Database (Denmark)

    Herbert, Luke Thomas; Sharp, Robin

    2014-01-01

    We present a framework for the automated generation of fault trees from models of realworld process workflows, expressed in a formalised subset of the popular Business Process Modelling and Notation (BPMN) language. To capture uncertainty and unreliability in workflows, we extend this formalism...

  1. Development and validation of an automated unit for the extraction of radiocaesium from seawater

    International Nuclear Information System (INIS)

    Bokor, Ilonka; Sdraulig, Sandra; Jenkinson, Peter; Madamperuma, Janaka; Martin, Paul

    2016-01-01

    An automated unit was developed for the in-situ extraction of radiocaesium ( 137 Cs and 134 Cs) from large volumes of seawater to achieve very low detection limits. The unit was designed for monitoring of Australian ocean and coastal waters, including at ports visited by nuclear-powered warships. The unit is housed within a robust case, and is easily transported and operated. It contains four filter cartridges connected in series. The first two cartridges are used to remove any suspended material that may be present in the seawater, while the last two cartridges are coated with potassium copper hexacyanoferrate for caesium extraction. Once the extraction is completed the coated cartridges are ashed. The ash is transferred to a small petri dish for counting of 137 Cs and 134 Cs by high resolution gamma spectrometry for a minimum of 24 h. The extraction method was validated for the following criteria: selectivity, trueness, precision, linearity, limit of detection and traceability. The validation showed the unit to be fit for purpose with the method capable of achieving low detection limits required for environmental samples. The results for the environmental measurements in Australian seawater correlate well with those reported in the Worldwide Marine Radioactivity Study (WOMARS). The cost of preparation and running the system is low and waste generation is minimal. - Highlights: • Automated unit for in-situ extraction of 137 Cs and 134 Cs from 1000 L of seawater. • Unit is robust, and easily transported and operated. • Cs extraction uses cartridges coated with potassium copper hexacyanoferrate. • Validated for selectivity, trueness, precision, linearity, LOD and traceability. • System fit for purpose for monitoring of Australian coastal and ocean waters.

  2. Data Driven Fault Tolerant Control : A Subspace Approach

    NARCIS (Netherlands)

    Dong, J.

    2009-01-01

    The main stream research on fault detection and fault tolerant control has been focused on model based methods. As far as a model is concerned, changes therein due to faults have to be extracted from measured data. Generally speaking, existing approaches process measured inputs and outputs either by

  3. A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

    Science.gov (United States)

    Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

    2016-01-15

    Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Fault tolerance and reliability in integrated ship control

    DEFF Research Database (Denmark)

    Nielsen, Jens Frederik Dalsgaard; Izadi-Zamanabadi, Roozbeh; Schiøler, Henrik

    2002-01-01

    Various strategies for achieving fault tolerance in large scale control systems are discussed. The positive and negative impacts of distribution through network communication are presented. The ATOMOS framework for standardized reliable marine automation is presented along with the corresponding...

  5. "3D_Fault_Offsets," a Matlab Code to Automatically Measure Lateral and Vertical Fault Offsets in Topographic Data: Application to San Andreas, Owens Valley, and Hope Faults

    Science.gov (United States)

    Stewart, N.; Gaudemer, Y.; Manighetti, I.; Serreau, L.; Vincendeau, A.; Dominguez, S.; Mattéo, L.; Malavieille, J.

    2018-01-01

    Measuring fault offsets preserved at the ground surface is of primary importance to recover earthquake and long-term slip distributions and understand fault mechanics. The recent explosion of high-resolution topographic data, such as Lidar and photogrammetric digital elevation models, offers an unprecedented opportunity to measure dense collections of fault offsets. We have developed a new Matlab code, 3D_Fault_Offsets, to automate these measurements. In topographic data, 3D_Fault_Offsets mathematically identifies and represents nine of the most prominent geometric characteristics of common sublinear markers along faults (especially strike slip) in 3-D, such as the streambed (minimum elevation), top, free face and base of channel banks or scarps (minimum Laplacian, maximum gradient, and maximum Laplacian), and ridges (maximum elevation). By calculating best fit lines through the nine point clouds on either side of the fault, the code computes the lateral and vertical offsets between the piercing points of these lines onto the fault plane, providing nine lateral and nine vertical offset measures per marker. Through a Monte Carlo approach, the code calculates the total uncertainty on each offset. It then provides tools to statistically analyze the dense collection of measures and to reconstruct the prefaulted marker geometry in the horizontal and vertical planes. We applied 3D_Fault_Offsets to remeasure previously published offsets across 88 markers on the San Andreas, Owens Valley, and Hope faults. We obtained 5,454 lateral and vertical offset measures. These automatic measures compare well to prior ones, field and remote, while their rich record provides new insights on the preservation of fault displacements in the morphology.

  6. Bottom-Up Technologies for Reuse: Automated Extractive Adoption of Software Product Lines

    OpenAIRE

    Martinez , Jabier ,; Ziadi , Tewfik; Bissyandé , Tegawendé; Klein , Jacques ,; Le Traon , Yves ,

    2017-01-01

    International audience; Adopting Software Product Line (SPL) engineering principles demands a high up-front investment. Bottom-Up Technologies for Reuse (BUT4Reuse) is a generic and extensible tool aimed to leverage existing similar software products in order to help in extractive SPL adoption. The envisioned users are 1) SPL adopters and 2) Integrators of techniques and algorithms to provide automation in SPL adoption activities. We present the methodology it implies for both types of users ...

  7. Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds

    Directory of Open Access Journals (Sweden)

    Y. Yu

    2014-06-01

    Full Text Available This paper presents an automated algorithm for extracting 3D trees directly from 3D mobile light detection and ranging (LiDAR data. To reduce both computational and spatial complexities, ground points are first filtered out from a raw 3D point cloud via blockbased elevation filtering. Off-ground points are then grouped into clusters representing individual objects through Euclidean distance clustering and voxel-based normalized cut segmentation. Finally, a model-driven method is proposed to achieve the extraction of 3D trees based on a pairwise 3D shape descriptor. The proposed algorithm is tested using a set of mobile LiDAR point clouds acquired by a RIEGL VMX-450 system. The results demonstrate the feasibility and effectiveness of the proposed algorithm.

  8. Automated concept and relationship extraction for the semi-automated ontology management (SEAM) system.

    Science.gov (United States)

    Doing-Harris, Kristina; Livnat, Yarden; Meystre, Stephane

    2015-01-01

    We develop medical-specialty specific ontologies that contain the settled science and common term usage. We leverage current practices in information and relationship extraction to streamline the ontology development process. Our system combines different text types with information and relationship extraction techniques in a low overhead modifiable system. Our SEmi-Automated ontology Maintenance (SEAM) system features a natural language processing pipeline for information extraction. Synonym and hierarchical groups are identified using corpus-based semantics and lexico-syntactic patterns. The semantic vectors we use are term frequency by inverse document frequency and context vectors. Clinical documents contain the terms we want in an ontology. They also contain idiosyncratic usage and are unlikely to contain the linguistic constructs associated with synonym and hierarchy identification. By including both clinical and biomedical texts, SEAM can recommend terms from those appearing in both document types. The set of recommended terms is then used to filter the synonyms and hierarchical relationships extracted from the biomedical corpus. We demonstrate the generality of the system across three use cases: ontologies for acute changes in mental status, Medically Unexplained Syndromes, and echocardiogram summary statements. Across the three uses cases, we held the number of recommended terms relatively constant by changing SEAM's parameters. Experts seem to find more than 300 recommended terms to be overwhelming. The approval rate of recommended terms increased as the number and specificity of clinical documents in the corpus increased. It was 60% when there were 199 clinical documents that were not specific to the ontology domain and 90% when there were 2879 documents very specific to the target domain. We found that fewer than 100 recommended synonym groups were also preferred. Approval rates for synonym recommendations remained low varying from 43% to 25% as the

  9. Fault diagnosis methods for district heating substations

    Energy Technology Data Exchange (ETDEWEB)

    Pakanen, J.; Hyvaerinen, J.; Kuismin, J.; Ahonen, M. [VTT Building Technology, Espoo (Finland). Building Physics, Building Services and Fire Technology

    1996-12-31

    A district heating substation is a demanding process for fault diagnosis. The process is nonlinear, load conditions of the district heating network change unpredictably and standard instrumentation is designed only for control and local monitoring purposes, not for automated diagnosis. Extra instrumentation means additional cost, which is usually not acceptable to consumers. That is why all conventional methods are not applicable in this environment. The paper presents five different approaches to fault diagnosis. While developing the methods, various kinds of pragmatic aspects and robustness had to be considered in order to achieve practical solutions. The presented methods are: classification of faults using performance indexing, static and physical modelling of process equipment, energy balance of the process, interactive fault tree reasoning and statistical tests. The methods are applied to a control valve, a heat excharger, a mud separating device and the whole process. The developed methods are verified in practice using simulation, simulation or field tests. (orig.) (25 refs.)

  10. Automated on-line liquid–liquid extraction system for temporal mass spectrometric analysis of dynamic samples

    Energy Technology Data Exchange (ETDEWEB)

    Hsieh, Kai-Ta; Liu, Pei-Han [Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd, Hsinchu, 300, Taiwan (China); Urban, Pawel L. [Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd, Hsinchu, 300, Taiwan (China); Institute of Molecular Science, National Chiao Tung University, 1001 University Rd, Hsinchu, 300, Taiwan (China)

    2015-09-24

    Most real samples cannot directly be infused to mass spectrometers because they could contaminate delicate parts of ion source and guides, or cause ion suppression. Conventional sample preparation procedures limit temporal resolution of analysis. We have developed an automated liquid–liquid extraction system that enables unsupervised repetitive treatment of dynamic samples and instantaneous analysis by mass spectrometry (MS). It incorporates inexpensive open-source microcontroller boards (Arduino and Netduino) to guide the extraction and analysis process. Duration of every extraction cycle is 17 min. The system enables monitoring of dynamic processes over many hours. The extracts are automatically transferred to the ion source incorporating a Venturi pump. Operation of the device has been characterized (repeatability, RSD = 15%, n = 20; concentration range for ibuprofen, 0.053–2.000 mM; LOD for ibuprofen, ∼0.005 mM; including extraction and detection). To exemplify its usefulness in real-world applications, we implemented this device in chemical profiling of pharmaceutical formulation dissolution process. Temporal dissolution profiles of commercial ibuprofen and acetaminophen tablets were recorded during 10 h. The extraction-MS datasets were fitted with exponential functions to characterize the rates of release of the main and auxiliary ingredients (e.g. ibuprofen, k = 0.43 ± 0.01 h{sup −1}). The electronic control unit of this system interacts with the operator via touch screen, internet, voice, and short text messages sent to the mobile phone, which is helpful when launching long-term (e.g. overnight) measurements. Due to these interactive features, the platform brings the concept of the Internet-of-Things (IoT) to the chemistry laboratory environment. - Highlights: • Mass spectrometric analysis normally requires sample preparation. • Liquid–liquid extraction can isolate analytes from complex matrices. • The proposed system automates

  11. Automated on-line liquid–liquid extraction system for temporal mass spectrometric analysis of dynamic samples

    International Nuclear Information System (INIS)

    Hsieh, Kai-Ta; Liu, Pei-Han; Urban, Pawel L.

    2015-01-01

    Most real samples cannot directly be infused to mass spectrometers because they could contaminate delicate parts of ion source and guides, or cause ion suppression. Conventional sample preparation procedures limit temporal resolution of analysis. We have developed an automated liquid–liquid extraction system that enables unsupervised repetitive treatment of dynamic samples and instantaneous analysis by mass spectrometry (MS). It incorporates inexpensive open-source microcontroller boards (Arduino and Netduino) to guide the extraction and analysis process. Duration of every extraction cycle is 17 min. The system enables monitoring of dynamic processes over many hours. The extracts are automatically transferred to the ion source incorporating a Venturi pump. Operation of the device has been characterized (repeatability, RSD = 15%, n = 20; concentration range for ibuprofen, 0.053–2.000 mM; LOD for ibuprofen, ∼0.005 mM; including extraction and detection). To exemplify its usefulness in real-world applications, we implemented this device in chemical profiling of pharmaceutical formulation dissolution process. Temporal dissolution profiles of commercial ibuprofen and acetaminophen tablets were recorded during 10 h. The extraction-MS datasets were fitted with exponential functions to characterize the rates of release of the main and auxiliary ingredients (e.g. ibuprofen, k = 0.43 ± 0.01 h"−"1). The electronic control unit of this system interacts with the operator via touch screen, internet, voice, and short text messages sent to the mobile phone, which is helpful when launching long-term (e.g. overnight) measurements. Due to these interactive features, the platform brings the concept of the Internet-of-Things (IoT) to the chemistry laboratory environment. - Highlights: • Mass spectrometric analysis normally requires sample preparation. • Liquid–liquid extraction can isolate analytes from complex matrices. • The proposed system automates the

  12. Detecting Faults By Use Of Hidden Markov Models

    Science.gov (United States)

    Smyth, Padhraic J.

    1995-01-01

    Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

  13. Distributed bearing fault diagnosis based on vibration analysis

    Science.gov (United States)

    Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

    2016-01-01

    Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

  14. The ECLSS Advanced Automation Project Evolution and Technology Assessment

    Science.gov (United States)

    Dewberry, Brandon S.; Carnes, James R.; Lukefahr, Brenda D.; Rogers, John S.; Rochowiak, Daniel M.; Mckee, James W.; Benson, Brian L.

    1990-01-01

    Viewgraphs on Environmental Control and Life Support System (ECLSS) advanced automation project evolution and technology assessment are presented. Topics covered include: the ECLSS advanced automation project; automatic fault diagnosis of ECLSS subsystems descriptions; in-line, real-time chemical and microbial fluid analysis; and object-oriented, distributed chemical and microbial modeling of regenerative environmental control systems description.

  15. A Systematic Methodology for Gearbox Health Assessment and Fault Classification

    Directory of Open Access Journals (Sweden)

    Jay Lee

    2011-01-01

    Full Text Available A systematic methodology for gearbox health assessment and fault classification is developed and evaluated for 560 data sets of gearbox vibration data provided by the Prognostics and Health Management Society for the 2009 data challenge competition. A comprehensive set of signal processing and feature extraction methods are used to extract over 200 features, including features extracted from the raw time signal, time synchronous signal, wavelet decomposition signal, frequency domain spectrum, envelope spectrum, among others. A regime segmentation approach using the tachometer signal, a spectrum similarity metric, and gear mesh frequency peak information are used to segment the data by gear type, input shaft speed, and braking torque load. A health assessment method that finds the minimum feature vector sum in each regime is used to classify and find the 80 baseline healthy data sets. A fault diagnosis method based on a distance calculation from normal along with specific features correlated to different fault signatures is used to diagnosis specific faults. The fault diagnosis method is evaluated for the diagnosis of a gear tooth breakage, input shaft imbalance, bent shaft, bearing inner race defect, and bad key, and the method could be further extended for other faults as long as a set of features can be correlated with a known fault signature. Future work looks to further refine the distance calculation algorithm for fault diagnosis, as well as further evaluate other signal processing method such as the empirical mode decomposition to see if an improved set of features can be used to improve the fault diagnosis accuracy.

  16. An Integrated Framework of Drivetrain Degradation Assessment and Fault Localization for Offshore Wind Turbines

    Directory of Open Access Journals (Sweden)

    Jay Lee

    2013-01-01

    Full Text Available As wind energy proliferates in onshore and offshore applications, it has become significantly important to predict wind turbine downtime and maintain operation uptime to ensure maximal yield. Two types of data systems have been widely adopted for monitoring turbine health condition: supervisory control and data acquisition (SCADA and condition monitoring system (CMS. Provided that research and development have focused on advancing analytical techniques based on these systems independently, an intelligent model that associates information from both systems is necessary and beneficial. In this paper, a systematic framework is designed to integrate CMS and SCADA data and assess drivetrain degradation over its lifecycle. Information reference and advanced feature extraction techniques are employed to procure heterogeneous health indicators. A pattern recognition algorithm is used to model baseline behavior and measure deviation of current behavior, where a Self-organizing Map (SOM and minimum quantization error (MQE method is selected to achieve degradation assessment. Eventually, the computation and ranking of component contribution to the detected degradation offers component-level fault localization. When validated and automated by various applications, the approach is able to incorporate diverse data resources and output actionable information to advise predictive maintenance with precise fault information. The approach is validated on a 3 MW offshore turbine, where an incipient fault is detected well before existing system shuts down the unit. A radar chart is used to illustrate the fault localization result.

  17. An artificial intelligence approach to onboard fault monitoring and diagnosis for aircraft applications

    Science.gov (United States)

    Schutte, P. C.; Abbott, K. H.

    1986-01-01

    Real-time onboard fault monitoring and diagnosis for aircraft applications, whether performed by the human pilot or by automation, presents many difficult problems. Quick response to failures may be critical, the pilot often must compensate for the failure while diagnosing it, his information about the state of the aircraft is often incomplete, and the behavior of the aircraft changes as the effect of the failure propagates through the system. A research effort was initiated to identify guidelines for automation of onboard fault monitoring and diagnosis and associated crew interfaces. The effort began by determining the flight crew's information requirements for fault monitoring and diagnosis and the various reasoning strategies they use. Based on this information, a conceptual architecture was developed for the fault monitoring and diagnosis process. This architecture represents an approach and a framework which, once incorporated with the necessary detail and knowledge, can be a fully operational fault monitoring and diagnosis system, as well as providing the basis for comparison of this approach to other fault monitoring and diagnosis concepts. The architecture encompasses all aspects of the aircraft's operation, including navigation, guidance and controls, and subsystem status. The portion of the architecture that encompasses subsystem monitoring and diagnosis was implemented for an aircraft turbofan engine to explore and demonstrate the AI concepts involved. This paper describes the architecture and the implementation for the engine subsystem.

  18. Evaluation of Sample Stability and Automated DNA Extraction for Fetal Sex Determination Using Cell-Free Fetal DNA in Maternal Plasma

    Directory of Open Access Journals (Sweden)

    Elena Ordoñez

    2013-01-01

    Full Text Available Objective. The detection of paternally inherited sequences in maternal plasma, such as the SRY gene for fetal sexing or RHD for fetal blood group genotyping, is becoming part of daily routine in diagnostic laboratories. Due to the low percentage of fetal DNA, it is crucial to ensure sample stability and the efficiency of DNA extraction. We evaluated blood stability at 4°C for at least 24 hours and automated DNA extraction, for fetal sex determination in maternal plasma. Methods. A total of 158 blood samples were collected, using EDTA-K tubes, from women in their 1st trimester of pregnancy. Samples were kept at 4°C for at least 24 hours before processing. An automated DNA extraction was evaluated, and its efficiency was compared with a standard manual procedure. The SRY marker was used to quantify cfDNA by real-time PCR. Results. Although lower cfDNA amounts were obtained by automated DNA extraction (mean 107,35 GE/mL versus 259,43 GE/mL, the SRY sequence was successfully detected in all 108 samples from pregnancies with male fetuses. Conclusion. We successfully evaluated the suitability of standard blood tubes for the collection of maternal blood and assessed samples to be suitable for analysis at least 24 hours later. This would allow shipping to a central reference laboratory almost from anywhere in Europe.

  19. Progress in automated extraction and purification of in situ {sup 14}C from quartz: Results from the Purdue in situ {sup 14}C laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Lifton, Nathaniel, E-mail: nlifton@purdue.edu [Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907 (United States); Department of Physics and Astronomy and Purdue Rare Isotope Measurement Laboratory (PRIME Lab), Purdue University, 525 Northwestern Avenue, West Lafayette, IN 47907 (United States); Goehring, Brent, E-mail: bgoehrin@tulane.edu [Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907 (United States); Wilson, Jim, E-mail: jim.wilson@aeonlaboratories.com [Aeon Laboratories, LLC, 5835 North Genematas Drive, Tucson, AZ 85704 (United States); Kubley, Thomas [Department of Physics and Astronomy and Purdue Rare Isotope Measurement Laboratory (PRIME Lab), Purdue University, 525 Northwestern Avenue, West Lafayette, IN 47907 (United States); Caffee, Marc [Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907 (United States); Department of Physics and Astronomy and Purdue Rare Isotope Measurement Laboratory (PRIME Lab), Purdue University, 525 Northwestern Avenue, West Lafayette, IN 47907 (United States)

    2015-10-15

    Current extraction methods for in situ {sup 14}C from quartz [e.g., Lifton et al., (2001), Pigati et al., (2010), Hippe et al., (2013)] are time-consuming and repetitive, making them an attractive target for automation. We report on the status of in situ {sup 14}C extraction and purification systems originally automated at the University of Arizona that have now been reconstructed and upgraded at the Purdue Rare Isotope Measurement Laboratory (PRIME Lab). The Purdue in situ {sup 14}C laboratory builds on the flow-through extraction system design of Pigati et al. (2010), automating most of the procedure by retrofitting existing valves with external servo-controlled actuators, regulating the pressure of research purity O{sub 2} inside the furnace tube via a PID-based pressure controller in concert with an inlet mass flow controller, and installing an automated liquid N{sub 2} distribution system, all driven by LabView® software. A separate system for cryogenic CO{sub 2} purification, dilution, and splitting is also fully automated, ensuring a highly repeatable process regardless of the operator. We present results from procedural blanks and an intercomparison material (CRONUS-A), as well as results of experiments to increase the amount of material used in extraction, from the standard 5 g to 10 g or above. Results thus far are quite promising with procedural blanks comparable to previous work and significant improvements in reproducibility for CRONUS-A measurements. The latter analyses also demonstrate the feasibility of quantitative extraction of in situ {sup 14}C from sample masses up to 10 g. Our lab is now analyzing unknowns routinely, but lowering overall blank levels is the focus of ongoing research.

  20. Fault diagnosis for agitator driving system in a high temperature reduction reactor

    Energy Technology Data Exchange (ETDEWEB)

    Park, Gee Young; Hong, Dong Hee; Jung, Jae Hoo; Kim, Young Hwan; Jin, Jae Hyun; Yoon, Ji Sup [KAERI, Taejon (Korea, Republic of)

    2003-10-01

    In this paper, a preliminary study for development of a fault diagnosis is presented for monitoring and diagnosing faults in the agitator driving system of a high temperature reduction reactor. In order to identify a fault occurrence and classify the fault cause, vibration signals measured by accelerometers on the outer shroud of the agitator driving system are firstly decomposed by Wavelet Transform (WT) and the features corresponding to each fault type are extracted. For the diagnosis, the fuzzy ARTMAP is employed and thereby, based on the features extracted from the WT, the robust fault classifier can be implemented with a very short training time - a single training epoch and a single learning iteration is sufficient for training the fault classifier. The test results demonstrate satisfactory classification for the faults pre-categorized from considerations of possible occurrence during experiments on a small-scale reduction reactor.

  1. Semi-automated extraction and characterization of Stromal Vascular Fraction using a new medical device.

    Science.gov (United States)

    Hanke, Alexander; Prantl, Lukas; Wenzel, Carina; Nerlich, Michael; Brockhoff, Gero; Loibl, Markus; Gehmert, Sebastian

    2016-01-01

    The stem cell rich Stromal Vascular Fraction (SVF) can be harvested by processing lipo-aspirate or fat tissue with an enzymatic digestion followed by centrifugation. To date neither a standardised extraction method for SVF nor a generally admitted protocol for cell application in patients exists. A novel commercially available semi-automated device for the extraction of SVF promises sterility, consistent results and usability in the clinical routine. The aim of this work was to compare the quantity and quality of the SVF between the new system and an established manual laboratory method. SVF was extracted from lipo-aspirate both by a prototype of the semi-automated UNiStation™ (NeoGenesis, Seoul, Korea) and by hand preparation with common laboratory equipment. Cell composition of the SVF was characterized by multi-parametric flow-cytometry (FACSCanto-II, BD Biosciences). The total cell number (quantity) of the SVF was determined as well the percentage of cells expressing the stem cell marker CD34, the leucocyte marker CD45 and the marker CD271 for highly proliferative stem cells (quality). Lipo-aspirate obtained from six patients was processed with both the novel device (d) and the hand preparation (h) which always resulted in a macroscopically visible SVF. However, there was a tendency of a fewer cell yield per gram of used lipo-aspirate with the device (d: 1.1×105±1.1×105 vs. h: 2.0×105±1.7×105; p = 0.06). Noteworthy, the percentage of CD34+ cells was significantly lower when using the device (d: 57.3% ±23.8% vs. h: 74.1% ±13.4%; p = 0.02) and CD45+ leukocyte counts tend to be higher when compared to the hand preparation (d: 20.7% ±15.8% vs. h: 9.8% ±7.1%; p = 0.07). The percentage of highly proliferative CD271+ cells was similar for both methods (d:12.9% ±9.6% vs. h: 13.4% ±11.6%; p = 0.74) and no differences were found for double positive cells of CD34+/CD45+ (d: 5.9% ±1.7% vs. h: 1.7% ±1.1%; p = 0.13), CD34+/CD271+ (d: 24

  2. A seismic fault recognition method based on ant colony optimization

    Science.gov (United States)

    Chen, Lei; Xiao, Chuangbai; Li, Xueliang; Wang, Zhenli; Huo, Shoudong

    2018-05-01

    Fault recognition is an important section in seismic interpretation and there are many methods for this technology, but no one can recognize fault exactly enough. For this problem, we proposed a new fault recognition method based on ant colony optimization which can locate fault precisely and extract fault from the seismic section. Firstly, seismic horizons are extracted by the connected component labeling algorithm; secondly, the fault location are decided according to the horizontal endpoints of each horizon; thirdly, the whole seismic section is divided into several rectangular blocks and the top and bottom endpoints of each rectangular block are considered as the nest and food respectively for the ant colony optimization algorithm. Besides that, the positive section is taken as an actual three dimensional terrain by using the seismic amplitude as a height. After that, the optimal route from nest to food calculated by the ant colony in each block is judged as a fault. Finally, extensive comparative tests were performed on the real seismic data. Availability and advancement of the proposed method were validated by the experimental results.

  3. TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction

    OpenAIRE

    Rahnama, Mehdi; Gloaguen, Richard

    2014-01-01

    Geological structures, such as faults and fractures, appear as image discontinuities or lineaments in remote sensing data. Geologic lineament mapping is a very important issue in geo-engineering, especially for construction site selection, seismic, and risk assessment, mineral exploration and hydrogeological research. Classical methods of lineaments extraction are based on semi-automated (or visual) interpretation of optical data and digital elevation models. We developed a freely available M...

  4. A novel automated device for rapid nucleic acid extraction utilizing a zigzag motion of magnetic silica beads

    International Nuclear Information System (INIS)

    Yamaguchi, Akemi; Matsuda, Kazuyuki; Uehara, Masayuki; Honda, Takayuki; Saito, Yasunori

    2016-01-01

    We report a novel automated device for nucleic acid extraction, which consists of a mechanical control system and a disposable cassette. The cassette is composed of a bottle, a capillary tube, and a chamber. After sample injection in the bottle, the sample is lysed, and nucleic acids are adsorbed on the surface of magnetic silica beads. These magnetic beads are transported and are vibrated through the washing reagents in the capillary tube under the control of the mechanical control system, and thus, the nucleic acid is purified without centrifugation. The purified nucleic acid is automatically extracted in 3 min for the polymerase chain reaction (PCR). The nucleic acid extraction is dependent on the transport speed and the vibration frequency of the magnetic beads, and optimizing these two parameters provided better PCR efficiency than the conventional manual procedure. There was no difference between the detection limits of our novel device and that of the conventional manual procedure. We have already developed the droplet-PCR machine, which can amplify and detect specific nucleic acids rapidly and automatically. Connecting the droplet-PCR machine to our novel automated extraction device enables PCR analysis within 15 min, and this system can be made available as a point-of-care testing in clinics as well as general hospitals. - Highlights: • Automatic nucleic acid extraction is performed in 3 min. • Zigzag motion of magnetic silica beads yields rapid and efficient extraction. • The present our device provides better performance than the conventional procedure.

  5. Collection and analysis of existing information on applicability of investigation methods for estimation of beginning age of faulting in present faulting pattern

    International Nuclear Information System (INIS)

    Doke, Ryosuke; Yasue, Ken-ichi; Tanikawa, Shin-ichi; Nakayasu, Akio; Niizato, Tadafumi; Tanaka, Takenobu; Aoki, Michinori; Sekiya, Ayako

    2011-12-01

    In the field of R and D programs of a geological disposal of high level radioactive waste, it is great importance to develop a set of investigation and analysis techniques for the assessment of long-term geosphere stability over a geological time, which means that any changes of geological environment will not significantly impact on the long-term safety of a geological disposal system. In Japanese archipelago, crustal movements are so active that uplift and subsidence are remarkable in recent several hundreds of thousands of years. Therefore, it is necessary to assess the long-term geosphere stability taking into account a topographic change caused by crustal movements. One of the factors for the topographic change is the movement of an active fault, which is a geological process to release a strain accumulated by plate motion. A beginning age of the faulting in the present faulting pattern suggests the beginning age of neotectonic activities around the active fault, and also provides basic information to identifying the stage of a geomorphic development of mountains. Therefore, the age of faulting in the present faulting pattern is important information to estimate a topographic change in the future on the mountain regions of Japan. In this study, existing information related to methods for the estimation of the beginning age of the faulting in the present faulting pattern on the active fault were collected and reviewed. A principle of method, noticing points and technical know-hows in the application of the methods, data uncertainty, and so on were extracted from the existing information. Based on these extracted information, task-flows indicating working process on the estimation of the beginning age for the faulting of the active fault were illustrated on each method. Additionally, the distribution map of the beginning age with accuracy of faulting in the present faulting pattern on the active fault was illustrated. (author)

  6. AUTOMATION OF CONVEYOR BELT TRANSPORT

    Directory of Open Access Journals (Sweden)

    Nenad Marinović

    1990-12-01

    Full Text Available Belt conveyor transport, although one of the most economical mining transport system, introduce many problems to mantain the continuity of the operation. Every stop causes economical loses. Optimal operation require correct tension of the belt, correct belt position and velocity and faultless rolls, which are together input conditions for automation. Detection and position selection of the faults are essential for safety to eliminate fire hazard and for efficient maintenance. Detection and location of idler roll faults are still open problem and up to now not solved successfully (the paper is published in Croatian.

  7. Automated CO2 extraction from air for clumped isotope analysis in the atmo- and biosphere

    Science.gov (United States)

    Hofmann, Magdalena; Ziegler, Martin; Pons, Thijs; Lourens, Lucas; Röckmann, Thomas

    2015-04-01

    The conventional stable isotope ratios 13C/12C and 18O/16O in atmospheric CO2 are a powerful tool for unraveling the global carbon cycle. In recent years, it has been suggested that the abundance of the very rare isotopologue 13C18O16O on m/z 47 might be a promising tracer to complement conventional stable isotope analysis of atmospheric CO2 [Affek and Eiler, 2006; Affek et al. 2007; Eiler and Schauble, 2004; Yeung et al., 2009]. Here we present an automated analytical system that is designed for clumped isotope analysis of atmo- and biospheric CO2. The carbon dioxide gas is quantitatively extracted from about 1.5L of air (ATP). The automated stainless steel extraction and purification line consists of three main components: (i) a drying unit (a magnesium perchlorate unit and a cryogenic water trap), (ii) two CO2 traps cooled with liquid nitrogen [Werner et al., 2001] and (iii) a GC column packed with Porapak Q that can be cooled with liquid nitrogen to -30°C during purification and heated up to 230°C in-between two extraction runs. After CO2 extraction and purification, the CO2 is automatically transferred to the mass spectrometer. Mass spectrometric analysis of the 13C18O16O abundance is carried out in dual inlet mode on a MAT 253 mass spectrometer. Each analysis generally consists of 80 change-over-cycles. Three additional Faraday cups were added to the mass spectrometer for simultaneous analysis of the mass-to-charge ratios 44, 45, 46, 47, 48 and 49. The reproducibility for δ13C, δ18O and Δ47 for repeated CO2 extractions from air is in the range of 0.11o (SD), 0.18o (SD) and 0.02 (SD)o respectively. This automated CO2 extraction and purification system will be used to analyse the clumped isotopic signature in atmospheric CO2 (tall tower, Cabauw, Netherlands) and to study the clumped isotopic fractionation during photosynthesis (leaf chamber experiments) and soil respiration. References Affek, H. P., Xu, X. & Eiler, J. M., Geochim. Cosmochim. Acta 71, 5033

  8. Automated extraction of DNA and PCR setup using a Tecan Freedom EVO® liquid handler

    DEFF Research Database (Denmark)

    Frøslev, Tobias Guldberg; Hansen, Anders Johannes; Stangegaard, Michael

    2009-01-01

    We have implemented and validated automated protocols for DNA extraction and PCR setup using a Tecan Freedom EVO® liquid handler mounted with the TeMagS magnetic separation device. The methods were validated for accredited, forensic genetic work according to ISO 17025 using the Qiagen Mag...... genetic DNA typing can be implemented on a simple robot leading to the reduction of manual work as well as increased quality and throughput....

  9. Automated Contingency Management for Advanced Propulsion Systems, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Automated Contingency Management (ACM), or the ability to confidently and autonomously adapt to fault conditions with the goal of still achieving mission objectives,...

  10. Automated Extraction Of Associations Between Methylated Genes and Diseases From Biomedical Literature

    KAUST Repository

    Bin Res, Arwa A.

    2012-12-01

    Associations between methylated genes and diseases have been investigated in several studies, and it is critical to have such information available for better understanding of diseases and clinical decisions. However, such information is scattered in a large number of electronic publications and it is difficult to manually search for it. Therefore, the goal of the project is to develop a machine learning model that can efficiently extract such information. Twelve machine learning algorithms were applied and compared in application to this problem based on three approaches that involve: document-term frequency matrices, position weight matrices, and a hybrid approach that uses the combination of the previous two. The best results we obtained by the hybrid approach with a random forest model that, in a 10-fold cross-validation, achieved F-score and accuracy of nearly 85% and 84%, respectively. On a completely separate testing set, F-score and accuracy of 89% and 88%, respectively, were obtained. Based on this model, we developed a tool that automates extraction of associations between methylated genes and diseases from electronic text. Our study contributed an efficient method for extracting specific types of associations from free text and the methodology developed here can be extended to other similar association extraction problems.

  11. Comparison of QIAsymphony Automated and QIAamp Manual DNA Extraction Systems for Measuring Epstein-Barr Virus DNA Load in Whole Blood Using Real-Time PCR

    OpenAIRE

    Laus, Stella; Kingsley, Lawrence A.; Green, Michael; Wadowsky, Robert M.

    2011-01-01

    Automated and manual extraction systems have been used with real-time PCR for quantification of Epstein-Barr virus [human herpesvirus 4 (HHV-4)] DNA in whole blood, but few studies have evaluated relative performances. In the present study, the automated QIAsymphony and manual QIAamp extraction systems (Qiagen, Valencia, CA) were assessed using paired aliquots derived from clinical whole-blood specimens and an in-house, real-time PCR assay. The detection limits using the QIAsymphony and QIAam...

  12. Automated extraction of genomic DNA from medically important yeast species and filamentous fungi by using the MagNA Pure LC system.

    Science.gov (United States)

    Loeffler, Juergen; Schmidt, Kathrin; Hebart, Holger; Schumacher, Ulrike; Einsele, Hermann

    2002-06-01

    A fully automated assay was established for the extraction of DNA from clinically important fungi by using the MagNA Pure LC instrument. The test was evaluated by DNA isolation from 23 species of yeast and filamentous fungi and by extractions (n = 28) of serially diluted Aspergillus fumigatus conidia (10(5) to 0 CFU/ml). Additionally, DNA from 67 clinical specimens was extracted and compared to the manual protocol. The detection limit of the MagNA Pure LC assay of 10 CFU corresponded to the sensitivity when DNA was extracted manually; in 9 of 28 runs, we could achieve a higher sensitivity of 1 CFU/ml blood, which was found to be significant (p DNA from all fungal species analyzed could be extracted and amplified by real-time PCR. Negative controls from all MagNA Pure isolations remained negative. Sixty-three clinical samples showed identical results by both methods, whereas in 4 of 67 samples, discordant results were obtained. Thus, the MagNA Pure LC technique offers a fast protocol for automated DNA isolation from numerous fungi, revealing high sensitivity and purity.

  13. Fault Locating, Prediction and Protection (FLPPS)

    Energy Technology Data Exchange (ETDEWEB)

    Yinger, Robert, J.; Venkata, S., S.; Centeno, Virgilio

    2010-09-30

    One of the main objectives of this DOE-sponsored project was to reduce customer outage time. Fault location, prediction, and protection are the most important aspects of fault management for the reduction of outage time. In the past most of the research and development on power system faults in these areas has focused on transmission systems, and it is not until recently with deregulation and competition that research on power system faults has begun to focus on the unique aspects of distribution systems. This project was planned with three Phases, approximately one year per phase. The first phase of the project involved an assessment of the state-of-the-art in fault location, prediction, and detection as well as the design, lab testing, and field installation of the advanced protection system on the SCE Circuit of the Future located north of San Bernardino, CA. The new feeder automation scheme, with vacuum fault interrupters, will limit the number of customers affected by the fault. Depending on the fault location, the substation breaker might not even trip. Through the use of fast communications (fiber) the fault locations can be determined and the proper fault interrupting switches opened automatically. With knowledge of circuit loadings at the time of the fault, ties to other circuits can be closed automatically to restore all customers except the faulted section. This new automation scheme limits outage time and increases reliability for customers. The second phase of the project involved the selection, modeling, testing and installation of a fault current limiter on the Circuit of the Future. While this project did not pay for the installation and testing of the fault current limiter, it did perform the evaluation of the fault current limiter and its impacts on the protection system of the Circuit of the Future. After investigation of several fault current limiters, the Zenergy superconducting, saturable core fault current limiter was selected for

  14. Automated prostate cancer localization without the need for peripheral zone extraction using multiparametric MRI.

    Science.gov (United States)

    Liu, Xin; Yetik, Imam Samil

    2011-06-01

    Multiparametric magnetic resonance imaging (MRI) has been shown to have higher localization accuracy than transrectal ultrasound (TRUS) for prostate cancer. Therefore, automated cancer segmentation using multiparametric MRI is receiving a growing interest, since MRI can provide both morphological and functional images for tissue of interest. However, all automated methods to this date are applicable to a single zone of the prostate, and the peripheral zone (PZ) of the prostate needs to be extracted manually, which is a tedious and time-consuming job. In this paper, our goal is to remove the need of PZ extraction by incorporating the spatial and geometric information of prostate tumors with multiparametric MRI derived from T2-weighted MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI). In order to remove the need of PZ extraction, the authors propose a new method to incorporate the spatial information of the cancer. This is done by introducing a new feature called location map. This new feature is constructed by applying a nonlinear transformation to the spatial position coordinates of each pixel, so that the location map implicitly represents the geometric position of each pixel with respect to the prostate region. Then, this new feature is combined with multiparametric MR images to perform tumor localization. The proposed algorithm is applied to multiparametric prostate MRI data obtained from 20 patients with biopsy-confirmed prostate cancer. The proposed method which does not need the masks of PZ was found to have prostate cancer detection specificity of 0.84, sensitivity of 0.80 and dice coefficient value of 0.42. The authors have found that fusing the spatial information allows us to obtain tumor outline without the need of PZ extraction with a considerable success (better or similar performance to methods that require manual PZ extraction). Our experimental results quantitatively demonstrate the effectiveness of the proposed

  15. Research on the Fault Coefficient in Complex Electrical Engineering

    Directory of Open Access Journals (Sweden)

    Yi Sun

    2015-08-01

    Full Text Available Fault detection and isolation in a complex system are research hotspots and frontier problems in the reliability engineering field. Fault identification can be regarded as a procedure of excavating key characteristics from massive failure data, then classifying and identifying fault samples. In this paper, based on the fundamental of feature extraction about the fault coefficient, we will discuss the fault coefficient feature in complex electrical engineering in detail. For general fault types in a complex power system, even if there is a strong white Gaussian stochastic interference, the fault coefficient feature is still accurate and reliable. The results about comparative analysis of noise influence will also demonstrate the strong anti-interference ability and great redundancy of the fault coefficient feature in complex electrical engineering.

  16. Improving the software fault localization process through testability information

    NARCIS (Netherlands)

    Gonzalez-Sanchez, A.; Abreu, R.; Gross, H.; Van Gemund, A.

    2010-01-01

    When failures occur during software testing, automated software fault localization helps to diagnose their root causes and identify the defective components of a program to support debugging. Diagnosis is carried out by selecting test cases in such way that their pass or fail information will narrow

  17. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture.

    Science.gov (United States)

    Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

    2017-01-14

    In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

  18. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT for Aquaculture

    Directory of Open Access Journals (Sweden)

    Yingyi Chen

    2017-01-01

    Full Text Available In the Internet of Things (IoT equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

  19. Fault Diagnosis of Batch Reactor Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Sujatha Subramanian

    2014-01-01

    Full Text Available Fault diagnosis of a batch reactor gives the early detection of fault and minimizes the risk of thermal runaway. It provides superior performance and helps to improve safety and consistency. It has become more vital in this technical era. In this paper, support vector machine (SVM is used to estimate the heat release (Qr of the batch reactor both normal and faulty conditions. The signature of the residual, which is obtained from the difference between nominal and estimated faulty Qr values, characterizes the different natures of faults occurring in the batch reactor. Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA techniques. artificial neural network (ANN classifiers like multilayer perceptron (MLP, radial basis function (RBF, and Bayes net are used to classify the different types of faults from the reduced features. It is observed from the result of the comparative study that the proposed method for fault diagnosis with limited number of features extracted from only one estimated parameter (Qr shows that it is more efficient and fast for diagnosing the typical faults.

  20. Automated Freedom from Interference Analysis for Automotive Software

    OpenAIRE

    Leitner-Fischer , Florian; Leue , Stefan; Liu , Sirui

    2016-01-01

    International audience; Freedom from Interference for automotive software systems developed according to the ISO 26262 standard means that a fault in a less safety critical software component will not lead to a fault in a more safety critical component. It is an important concern in the realm of functional safety for automotive systems. We present an automated method for the analysis of concurrency-related interferences based on the QuantUM approach and tool that we have previously developed....

  1. Development of a morphological convolution operator for bearing fault detection

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

    2018-05-01

    This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

  2. FADES: A tool for automated fault analysis of complex systems

    International Nuclear Information System (INIS)

    Wood, C.

    1990-01-01

    FADES is an Expert System for performing fault analyses on complex connected systems. By using a graphical editor to draw components and link them together the FADES system allows the analyst to describe a given system. The knowledge base created is used to qualitatively simulate the system behaviour. By inducing all possible component failures in the system and determining their effects, a set of facts is built up. These facts are then used to create Fault Trees, or FMEA tables. The facts may also be used for explanation effects and to generate diagnostic rules allowing system instrumentation to be optimised. The prototype system has been built and tested and is preently undergoing testing by users. All comments from these trials will be used to tailor the system to the requirements of the user so that the end product performs the exact task required

  3. Restructuring of workflows to minimise errors via stochastic model checking: An automated evolutionary approach

    DEFF Research Database (Denmark)

    Herbert, Luke Thomas; Hansen, Zaza Nadja Lee

    2016-01-01

    This article presents a framework for the automated restructuring of stochastic workflows to reduce the impact of faults. The framework allows for the modelling of workflows by means of a formalised subset of the BPMN workflow language. We extend this modelling formalism to describe faults...

  4. Deep SOMs for automated feature extraction and classification from big data streaming

    Science.gov (United States)

    Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

    2017-03-01

    In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

  5. Repetitive transient extraction for machinery fault diagnosis using multiscale fractional order entropy infogram

    Science.gov (United States)

    Xu, Xuefang; Qiao, Zijian; Lei, Yaguo

    2018-03-01

    The presence of repetitive transients in vibration signals is a typical symptom of local faults of rotating machinery. Infogram was developed to extract the repetitive transients from vibration signals based on Shannon entropy. Unfortunately, the Shannon entropy is maximized for random processes and unable to quantify the repetitive transients buried in heavy random noise. In addition, the vibration signals always contain multiple intrinsic oscillatory modes due to interaction and coupling effects between machine components. Under this circumstance, high values of Shannon entropy appear in several frequency bands or high value of Shannon entropy doesn't appear in the optimal frequency band, and the infogram becomes difficult to interpret. Thus, it also becomes difficult to select the optimal frequency band for extracting the repetitive transients from the whole frequency bands. To solve these problems, multiscale fractional order entropy (MSFE) infogram is proposed in this paper. With the help of MSFE infogram, the complexity and nonlinear signatures of the vibration signals can be evaluated by quantifying spectral entropy over a range of scales in fractional domain. Moreover, the similarity tolerance of MSFE infogram is helpful for assessing the regularity of signals. A simulation and two experiments concerning a locomotive bearing and a wind turbine gear are used to validate the MSFE infogram. The results demonstrate that the MSFE infogram is more robust to the heavy noise than infogram and the high value is able to only appear in the optimal frequency band for the repetitive transient extraction.

  6. Tolerance Towards Sensor Faults: An Application to a Flexible Arm Manipulator

    Directory of Open Access Journals (Sweden)

    Chee Pin Tan

    2006-12-01

    Full Text Available As more engineering operations become automatic, the need for robustness towards faults increases. Hence, a fault tolerant control (FTC scheme is a valuable asset. This paper presents a robust sensor fault FTC scheme implemented on a flexible arm manipulator, which has many applications in automation. Sensor faults affect the system's performance in the closed loop when the faulty sensor readings are used to generate the control input. In this paper, the non-faulty sensors are used to reconstruct the faults on the potentially faulty sensors. The reconstruction is subtracted from the faulty sensors to form a compensated ‘virtual sensor’ and this signal (instead of the normally used faulty sensor output is then used to generate the control input. A design method is also presented in which the FTC scheme is made insensitive to any system uncertainties. Two fault conditions are tested; total failure and incipient faults. Then the scheme robustness is tested by implementing the flexible joint's FTC scheme on a flexible link, which has different parameters. Excellent results have been obtained for both cases (joint and link; the FTC scheme caused the system performance is almost identical to the fault-free scenario, whilst providing an indication that a fault is present, even for simultaneous faults.

  7. Simultaneous Sensor and Process Fault Diagnostics for Propellant Feed System

    Science.gov (United States)

    Cao, J.; Kwan, C.; Figueroa, F.; Xu, R.

    2006-01-01

    The main objective of this research is to extract fault features from sensor faults and process faults by using advanced fault detection and isolation (FDI) algorithms. A tank system that has some common characteristics to a NASA testbed at Stennis Space Center was used to verify our proposed algorithms. First, a generic tank system was modeled. Second, a mathematical model suitable for FDI has been derived for the tank system. Third, a new and general FDI procedure has been designed to distinguish process faults and sensor faults. Extensive simulations clearly demonstrated the advantages of the new design.

  8. Fault Length Vs Fault Displacement Evaluation In The Case Of Cerro Prieto Pull-Apart Basin (Baja California, Mexico) Subsidence

    Science.gov (United States)

    Glowacka, E.; Sarychikhina, O.; Nava Pichardo, F. A.; Farfan, F.; Garcia Arthur, M. A.; Orozco, L.; Brassea, J.

    2013-05-01

    The Cerro Prieto pull-apart basin is located in the southern part of San Andreas Fault system, and is characterized by high seismicity, recent volcanism, tectonic deformation and hydrothermal activity (Lomnitz et al, 1970; Elders et al., 1984; Suárez-Vidal et al., 2008). Since the Cerro Prieto geothermal field production started, in 1973, significant subsidence increase was observed (Glowacka and Nava, 1996, Glowacka et al., 1999), and a relation between fluid extraction rate and subsidence rate has been suggested (op. cit.). Analysis of existing deformation data (Glowacka et al., 1999, 2005, Sarychikhina 2011) points to the fact that, although the extraction changes influence the subsidence rate, the tectonic faults control the spatial extent of the observed subsidence. Tectonic faults act as water barriers in the direction perpendicular to the fault, and/or separate regions with different compaction, and as effect the significant part of the subsidence is released as vertical displacement on the ground surface along fault rupture. These faults ruptures cause damages to roads and irrigation canals and water leakage. Since 1996, a network of geotechnical instruments has operated in the Mexicali Valley, for continuous recording of deformation phenomena. To date, the network (REDECVAM: Mexicali Valley Crustal Strain Measurement Array) includes two crackmeters and eight tiltmeters installed on, or very close to, the main faults; all instruments have sampling intervals in the 1 to 20 minutes range. Additionally, there are benchmarks for measuring vertical fault displacements for which readings are recorded every 3 months. Since the crackmeter measures vertical displacement on the fault at one place only, the question appears: can we use the crackmeter data to evaluate how long is the lenth of the fractured fault, and how quickly it grows, so we can know where we can expect fractures in the canals or roads? We used the Wells and Coppersmith (1994) relations between

  9. FTREX Testing Report (Fault Tree Reliability Evaluation eXpert) Version 1.5

    International Nuclear Information System (INIS)

    Jung, Woo Sik

    2009-07-01

    In order to verify FTREX functions and to confirm the correctness of FTREX 1.5, various tests were performed 1.fault trees with negates 2.fault trees with house events 3.fault trees with multiple tops 4.fault trees with logical loops 5.fault trees with initiators, house events, negates, logical loops, and flag events By using the automated cutest propagation test, the FTREX 1.5 functions are verified. FTREX version 1.3 and later versions have capability to perform bottom-up cutset-propagation test in order check cutest status. FTREX 1.5 always generates the proper minimal cut sets. All the output cutsets of the tested problems are MCSs (Minimal Cut Sets) and have no non-minimal cutsets and improper cutsets. The improper cutsets are those that have no effect to top, have multiple initiators, or have disjoint events A * -A

  10. Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification

    Science.gov (United States)

    Liu, Yongbin; He, Bing; Liu, Fang; Lu, Siliang; Zhao, Yilei

    2016-12-01

    Fault pattern identification is a crucial step for the intelligent fault diagnosis of real-time health conditions in monitoring a mechanical system. However, many challenges exist in extracting the effective feature from vibration signals for fault recognition. A new feature fusion method is proposed in this study to extract new features using kernel joint approximate diagonalization of eigen-matrices (KJADE). In the method, the input space that is composed of original features is mapped into a high-dimensional feature space by nonlinear mapping. Then, the new features can be estimated through the eigen-decomposition of the fourth-order cumulative kernel matrix obtained from the feature space. Therefore, the proposed method could be used to reduce data redundancy because it extracts the inherent pattern structure of different fault classes as it is nonlinear by nature. The integration evaluation factor of between-class and within-class scatters (SS) is employed to depict the clustering performance quantitatively, and the new feature subset extracted by the proposed method is fed into a multi-class support vector machine for fault pattern identification. Finally, the effectiveness of the proposed method is verified by experimental vibration signals with different bearing fault types and severities. Results of several cases show that the KJADE algorithm is efficient in feature fusion for bearing fault identification.

  11. FTMP (Fault Tolerant Multiprocessor) programmer's manual

    Science.gov (United States)

    Feather, F. E.; Liceaga, C. A.; Padilla, P. A.

    1986-01-01

    The Fault Tolerant Multiprocessor (FTMP) computer system was constructed using the Rockwell/Collins CAPS-6 processor. It is installed in the Avionics Integration Research Laboratory (AIRLAB) of NASA Langley Research Center. It is hosted by AIRLAB's System 10, a VAX 11/750, for the loading of programs and experimentation. The FTMP support software includes a cross compiler for a high level language called Automated Engineering Design (AED) System, an assembler for the CAPS-6 processor assembly language, and a linker. Access to this support software is through an automated remote access facility on the VAX which relieves the user of the burden of learning how to use the IBM 4381. This manual is a compilation of information about the FTMP support environment. It explains the FTMP software and support environment along many of the finer points of running programs on FTMP. This will be helpful to the researcher trying to run an experiment on FTMP and even to the person probing FTMP with fault injections. Much of the information in this manual can be found in other sources; we are only attempting to bring together the basic points in a single source. If the reader should need points clarified, there is a list of support documentation in the back of this manual.

  12. Evaluation of three automated nucleic acid extraction systems for identification of respiratory viruses in clinical specimens by multiplex real-time PCR.

    Science.gov (United States)

    Kim, Yoonjung; Han, Mi-Soon; Kim, Juwon; Kwon, Aerin; Lee, Kyung-A

    2014-01-01

    A total of 84 nasopharyngeal swab specimens were collected from 84 patients. Viral nucleic acid was extracted by three automated extraction systems: QIAcube (Qiagen, Germany), EZ1 Advanced XL (Qiagen), and MICROLAB Nimbus IVD (Hamilton, USA). Fourteen RNA viruses and two DNA viruses were detected using the Anyplex II RV16 Detection kit (Seegene, Republic of Korea). The EZ1 Advanced XL system demonstrated the best analytical sensitivity for all the three viral strains. The nucleic acids extracted by EZ1 Advanced XL showed higher positive rates for virus detection than the others. Meanwhile, the MICROLAB Nimbus IVD system was comprised of fully automated steps from nucleic extraction to PCR setup function that could reduce human errors. For the nucleic acids recovered from nasopharyngeal swab specimens, the QIAcube system showed the fewest false negative results and the best concordance rate, and it may be more suitable for detecting various viruses including RNA and DNA virus strains. Each system showed different sensitivity and specificity for detection of certain viral pathogens and demonstrated different characteristics such as turnaround time and sample capacity. Therefore, these factors should be considered when new nucleic acid extraction systems are introduced to the laboratory.

  13. CHANNEL MORPHOLOGY TOOL (CMT): A GIS-BASED AUTOMATED EXTRACTION MODEL FOR CHANNEL GEOMETRY

    Energy Technology Data Exchange (ETDEWEB)

    JUDI, DAVID [Los Alamos National Laboratory; KALYANAPU, ALFRED [Los Alamos National Laboratory; MCPHERSON, TIMOTHY [Los Alamos National Laboratory; BERSCHEID, ALAN [Los Alamos National Laboratory

    2007-01-17

    This paper describes an automated Channel Morphology Tool (CMT) developed in ArcGIS 9.1 environment. The CMT creates cross-sections along a stream centerline and uses a digital elevation model (DEM) to create station points with elevations along each of the cross-sections. The generated cross-sections may then be exported into a hydraulic model. Along with the rapid cross-section generation the CMT also eliminates any cross-section overlaps that might occur due to the sinuosity of the channels using the Cross-section Overlap Correction Algorithm (COCoA). The CMT was tested by extracting cross-sections from a 5-m DEM for a 50-km channel length in Houston, Texas. The extracted cross-sections were compared directly with surveyed cross-sections in terms of the cross-section area. Results indicated that the CMT-generated cross-sections satisfactorily matched the surveyed data.

  14. Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion

    Science.gov (United States)

    Lin, Jinshan; Chen, Qian

    2013-07-01

    Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing vibration data and proposes a novel method for fault diagnosis of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing fault data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing fault data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing fault data with different fault diameters is significantly or slightly different depending on different types of bearing faults. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing faults. Subsequently, five characteristic parameters sensitive to changes of bearing fault conditions were extracted from the multifractal spectrum and utilized to construct fault features of bearing fault data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing fault data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked

  15. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    Science.gov (United States)

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2018-03-01

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  16. Reverse fault growth and fault interaction with frictional interfaces: insights from analogue models

    Science.gov (United States)

    Bonanno, Emanuele; Bonini, Lorenzo; Basili, Roberto; Toscani, Giovanni; Seno, Silvio

    2017-04-01

    The association of faulting and folding is a common feature in mountain chains, fold-and-thrust belts, and accretionary wedges. Kinematic models are developed and widely used to explain a range of relationships between faulting and folding. However, these models may result not to be completely appropriate to explain shortening in mechanically heterogeneous rock bodies. Weak layers, bedding surfaces, or pre-existing faults placed ahead of a propagating fault tip may influence the fault propagation rate itself and the associated fold shape. In this work, we employed clay analogue models to investigate how mechanical discontinuities affect the propagation rate and the associated fold shape during the growth of reverse master faults. The simulated master faults dip at 30° and 45°, recalling the range of the most frequent dip angles for active reverse faults that occurs in nature. The mechanical discontinuities are simulated by pre-cutting the clay pack. For both experimental setups (30° and 45° dipping faults) we analyzed three different configurations: 1) isotropic, i.e. without precuts; 2) with one precut in the middle of the clay pack; and 3) with two evenly-spaced precuts. To test the repeatability of the processes and to have a statistically valid dataset we replicate each configuration three times. The experiments were monitored by collecting successive snapshots with a high-resolution camera pointing at the side of the model. The pictures were then processed using the Digital Image Correlation method (D.I.C.), in order to extract the displacement and shear-rate fields. These two quantities effectively show both the on-fault and off-fault deformation, indicating the activity along the newly-formed faults and whether and at what stage the discontinuities (precuts) are reactivated. To study the fault propagation and fold shape variability we marked the position of the fault tips and the fold profiles for every successive step of deformation. Then we compared

  17. Modeling and Performance Considerations for Automated Fault Isolation in Complex Systems

    Science.gov (United States)

    Ferrell, Bob; Oostdyk, Rebecca

    2010-01-01

    The purpose of this paper is to document the modeling considerations and performance metrics that were examined in the development of a large-scale Fault Detection, Isolation and Recovery (FDIR) system. The FDIR system is envisioned to perform health management functions for both a launch vehicle and the ground systems that support the vehicle during checkout and launch countdown by using suite of complimentary software tools that alert operators to anomalies and failures in real-time. The FDIR team members developed a set of operational requirements for the models that would be used for fault isolation and worked closely with the vendor of the software tools selected for fault isolation to ensure that the software was able to meet the requirements. Once the requirements were established, example models of sufficient complexity were used to test the performance of the software. The results of the performance testing demonstrated the need for enhancements to the software in order to meet the demands of the full-scale ground and vehicle FDIR system. The paper highlights the importance of the development of operational requirements and preliminary performance testing as a strategy for identifying deficiencies in highly scalable systems and rectifying those deficiencies before they imperil the success of the project

  18. Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

    Science.gov (United States)

    Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

    2016-01-01

    This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. PMID:26848665

  19. Computer-oriented approach to fault-tree construction

    International Nuclear Information System (INIS)

    Salem, S.L.; Apostolakis, G.E.; Okrent, D.

    1976-11-01

    A methodology for systematically constructing fault trees for general complex systems is developed and applied, via the Computer Automated Tree (CAT) program, to several systems. A means of representing component behavior by decision tables is presented. The method developed allows the modeling of components with various combinations of electrical, fluid and mechanical inputs and outputs. Each component can have multiple internal failure mechanisms which combine with the states of the inputs to produce the appropriate output states. The generality of this approach allows not only the modeling of hardware, but human actions and interactions as well. A procedure for constructing and editing fault trees, either manually or by computer, is described. The techniques employed result in a complete fault tree, in standard form, suitable for analysis by current computer codes. Methods of describing the system, defining boundary conditions and specifying complex TOP events are developed in order to set up the initial configuration for which the fault tree is to be constructed. The approach used allows rapid modifications of the decision tables and systems to facilitate the analysis and comparison of various refinements and changes in the system configuration and component modeling

  20. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    Directory of Open Access Journals (Sweden)

    Chen Lu

    Full Text Available Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for

  1. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    Science.gov (United States)

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

  2. Transformer fault diagnosis using continuous sparse autoencoder.

    Science.gov (United States)

    Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou

    2016-01-01

    This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.

  3. Drilling Automation Demonstrations in Subsurface Exploration for Astrobiology

    Science.gov (United States)

    Glass, Brian; Cannon, H.; Lee, P.; Hanagud, S.; Davis, K.

    2006-01-01

    This project proposes to study subsurface permafrost microbial habitats at a relevant Arctic Mars-analog site (Haughton Crater, Devon Island, Canada) while developing and maturing the subsurface drilling and drilling automation technologies that will be required by post-2010 missions. It builds on earlier drilling technology projects to add permafrost and ice-drilling capabilities to 5m with a lightweight drill that will be automatically monitored and controlled in-situ. Frozen cores obtained with this drill under sterilized protocols will be used in testing three hypotheses pertaining to near-surface physical geology and ground H2O ice distribution, viewed as a habitat for microbial life in subsurface ice and ice-consolidated sediments. Automation technologies employed will demonstrate hands-off diagnostics and drill control, using novel vibrational dynamical analysis methods and model-based reasoning to monitor and identify drilling fault states before and during faults. Three field deployments, to a Mars-analog site with frozen impact crater fallback breccia, will support science goals, provide a rigorous test of drilling automation and lightweight permafrost drilling, and leverage past experience with the field site s particular logistics.

  4. Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter

    International Nuclear Information System (INIS)

    Meng, Lingjie; Xiang, Jiawei; Zhong, Yongteng; Song, Wenlei

    2015-01-01

    Defective rolling bearing response is often characterized by the presence of periodic impulses. However, the in-situ sampled vibration signal is ordinarily mixed with ambient noises and easy to be interfered even submerged. The hybrid approach combining the second generation wavelet denoising with morphological filter is presented. The raw signal is purified using the second generation wavelet. The difference between the closing and opening operator is employed as the morphology filter to extract the periodicity impulsive features from the purified signal and the defect information is easily to be extracted from the corresponding frequency spectrum. The proposed approach is evaluated by simulations and vibration signals from defective bearings with inner race fault, outer race fault, rolling element fault and compound faults, espectively. Results show that the ambient noises can be fully restrained and the defect information of the above defective bearings is well extracted, which demonstrates that the approach is feasible and effective for the fault detection of rolling bearing.

  5. Two sides of a fault: Grain-scale analysis of pore pressure control on fault slip.

    Science.gov (United States)

    Yang, Zhibing; Juanes, Ruben

    2018-02-01

    Pore fluid pressure in a fault zone can be altered by natural processes (e.g., mineral dehydration and thermal pressurization) and industrial operations involving subsurface fluid injection and extraction for the development of energy and water resources. However, the effect of pore pressure change on the stability and slip motion of a preexisting geologic fault remains poorly understood; yet, it is critical for the assessment of seismic hazard. Here, we develop a micromechanical model to investigate the effect of pore pressure on fault slip behavior. The model couples fluid flow on the network of pores with mechanical deformation of the skeleton of solid grains. Pore fluid exerts pressure force onto the grains, the motion of which is solved using the discrete element method. We conceptualize the fault zone as a gouge layer sandwiched between two blocks. We study fault stability in the presence of a pressure discontinuity across the gouge layer and compare it with the case of continuous (homogeneous) pore pressure. We focus on the onset of shear failure in the gouge layer and reproduce conditions where the failure plane is parallel to the fault. We show that when the pressure is discontinuous across the fault, the onset of slip occurs on the side with the higher pore pressure, and that this onset is controlled by the maximum pressure on both sides of the fault. The results shed new light on the use of the effective stress principle and the Coulomb failure criterion in evaluating the stability of a complex fault zone.

  6. Two sides of a fault: Grain-scale analysis of pore pressure control on fault slip

    Science.gov (United States)

    Yang, Zhibing; Juanes, Ruben

    2018-02-01

    Pore fluid pressure in a fault zone can be altered by natural processes (e.g., mineral dehydration and thermal pressurization) and industrial operations involving subsurface fluid injection and extraction for the development of energy and water resources. However, the effect of pore pressure change on the stability and slip motion of a preexisting geologic fault remains poorly understood; yet, it is critical for the assessment of seismic hazard. Here, we develop a micromechanical model to investigate the effect of pore pressure on fault slip behavior. The model couples fluid flow on the network of pores with mechanical deformation of the skeleton of solid grains. Pore fluid exerts pressure force onto the grains, the motion of which is solved using the discrete element method. We conceptualize the fault zone as a gouge layer sandwiched between two blocks. We study fault stability in the presence of a pressure discontinuity across the gouge layer and compare it with the case of continuous (homogeneous) pore pressure. We focus on the onset of shear failure in the gouge layer and reproduce conditions where the failure plane is parallel to the fault. We show that when the pressure is discontinuous across the fault, the onset of slip occurs on the side with the higher pore pressure, and that this onset is controlled by the maximum pressure on both sides of the fault. The results shed new light on the use of the effective stress principle and the Coulomb failure criterion in evaluating the stability of a complex fault zone.

  7. Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters

    Science.gov (United States)

    Lu, Yunsong; Wang, Fuli; Jia, Mingxing; Qi, Yuanchen

    2016-12-01

    This paper concerns fault diagnosis of centrifugal compressor based on thermal parameters. An improved qualitative simulation (QSIM) based fault diagnosis method is proposed to diagnose the faults of centrifugal compressor in a gas-steam combined-cycle power plant (CCPP). The qualitative models under normal and two faulty conditions have been built through the analysis of the principle of centrifugal compressor. To solve the problem of qualitative description of the observations of system variables, a qualitative trend extraction algorithm is applied to extract the trends of the observations. For qualitative states matching, a sliding window based matching strategy which consists of variables operating ranges constraints and qualitative constraints is proposed. The matching results are used to determine which QSIM model is more consistent with the running state of system. The correct diagnosis of two typical faults: seal leakage and valve stuck in the centrifugal compressor has validated the targeted performance of the proposed method, showing the advantages of fault roots containing in thermal parameters.

  8. Medication errors: prescribing faults and prescription errors.

    Science.gov (United States)

    Velo, Giampaolo P; Minuz, Pietro

    2009-06-01

    1. Medication errors are common in general practice and in hospitals. Both errors in the act of writing (prescription errors) and prescribing faults due to erroneous medical decisions can result in harm to patients. 2. Any step in the prescribing process can generate errors. Slips, lapses, or mistakes are sources of errors, as in unintended omissions in the transcription of drugs. Faults in dose selection, omitted transcription, and poor handwriting are common. 3. Inadequate knowledge or competence and incomplete information about clinical characteristics and previous treatment of individual patients can result in prescribing faults, including the use of potentially inappropriate medications. 4. An unsafe working environment, complex or undefined procedures, and inadequate communication among health-care personnel, particularly between doctors and nurses, have been identified as important underlying factors that contribute to prescription errors and prescribing faults. 5. Active interventions aimed at reducing prescription errors and prescribing faults are strongly recommended. These should be focused on the education and training of prescribers and the use of on-line aids. The complexity of the prescribing procedure should be reduced by introducing automated systems or uniform prescribing charts, in order to avoid transcription and omission errors. Feedback control systems and immediate review of prescriptions, which can be performed with the assistance of a hospital pharmacist, are also helpful. Audits should be performed periodically.

  9. All Roads Lead to Fault Diagnosis : Model-Based Reasoning with LYDIA

    NARCIS (Netherlands)

    Feldman, A.B.; Pietersma, J.; Van Gemund, A.J.C.

    2006-01-01

    Model-Based Reasoning (MBR) over qualitative models of complex, real-world systems has proven succesful for automated fault diagnosis, control, and repair. Expressing a system under diagnosis in a formal model and infering a diagnosis given observations are both challenging problems. In this paper

  10. Space Station Initial Operational Concept (IOC) operations and safety view - Automation and robotics for Space Station

    Science.gov (United States)

    Bates, William V., Jr.

    1989-01-01

    The automation and robotics requirements for the Space Station Initial Operational Concept (IOC) are discussed. The amount of tasks to be performed by an eight-person crew, the need for an automated or directed fault analysis capability, and ground support requirements are considered. Issues important in determining the role of automation for the IOC are listed.

  11. Novel methods for earth fault management in medium voltage distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Nikander, A; Jaerventausta, P [Tampere Univ. of Technology (Finland)

    1998-08-01

    Customers have become less and less tolerable against even short interruptions of supply. Rapid autoreclosures are especially harmful for those commercial and private customers who have equipment which will be disturbed by these under half second interruptions. Mainly due to increasing use of distribution automation (eg. remote controlled switching devices, fault detectors, computational fault location) the average interruption period per customer has been reduced. Simultaneously the amount of equipment sensitive to short voltage break or dip has increased. Therefore reducing the number of the interruptions has become a more essential target

  12. Programmable automation systems in PSA

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1997-06-01

    The Finnish safety authority (STUK) requires plant specific PSAs, and quantitative safety goals are set on different levels. The reliability analysis is more problematic when critical safety functions are realized by applying programmable automation systems. Conventional modeling techniques do not necessarily apply to the analysis of these systems, and the quantification seems to be impossible. However, it is important to analyze contribution of programmable automation systems to the plant safety and PSA is the only method with system analytical view over the safety. This report discusses the applicability of PSA methodology (fault tree analyses, failure modes and effects analyses) in the analysis of programmable automation systems. The problem of how to decompose programmable automation systems for reliability modeling purposes is discussed. In addition to the qualitative analysis and structural reliability modeling issues, the possibility to evaluate failure probabilities of programmable automation systems is considered. One solution to the quantification issue is the use of expert judgements, and the principles to apply expert judgements is discussed in the paper. A framework to apply expert judgements is outlined. Further, the impacts of subjective estimates on the interpretation of PSA results are discussed. (orig.) (13 refs.)

  13. Comparative Study of Parametric and Non-parametric Approaches in Fault Detection and Isolation

    DEFF Research Database (Denmark)

    Katebi, S.D.; Blanke, M.; Katebi, M.R.

    This report describes a comparative study between two approaches to fault detection and isolation in dynamic systems. The first approach uses a parametric model of the system. The main components of such techniques are residual and signature generation for processing and analyzing. The second...... approach is non-parametric in the sense that the signature analysis is only dependent on the frequency or time domain information extracted directly from the input-output signals. Based on these approaches, two different fault monitoring schemes are developed where the feature extraction and fault decision...

  14. Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

    Directory of Open Access Journals (Sweden)

    Liang Hua

    2015-01-01

    Full Text Available Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

  15. Arsenic fractionation in agricultural soil using an automated three-step sequential extraction method coupled to hydride generation-atomic fluorescence spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Rosas-Castor, J.M. [Universidad Autónoma de Nuevo León, UANL, Facultad de Ciencias Químicas, Cd. Universitaria, San Nicolás de los Garza, Nuevo León, C.P. 66451 Nuevo León (Mexico); Group of Analytical Chemistry, Automation and Environment, University of Balearic Islands, Cra. Valldemossa km 7.5, 07122 Palma de Mallorca (Spain); Portugal, L.; Ferrer, L. [Group of Analytical Chemistry, Automation and Environment, University of Balearic Islands, Cra. Valldemossa km 7.5, 07122 Palma de Mallorca (Spain); Guzmán-Mar, J.L.; Hernández-Ramírez, A. [Universidad Autónoma de Nuevo León, UANL, Facultad de Ciencias Químicas, Cd. Universitaria, San Nicolás de los Garza, Nuevo León, C.P. 66451 Nuevo León (Mexico); Cerdà, V. [Group of Analytical Chemistry, Automation and Environment, University of Balearic Islands, Cra. Valldemossa km 7.5, 07122 Palma de Mallorca (Spain); Hinojosa-Reyes, L., E-mail: laura.hinojosary@uanl.edu.mx [Universidad Autónoma de Nuevo León, UANL, Facultad de Ciencias Químicas, Cd. Universitaria, San Nicolás de los Garza, Nuevo León, C.P. 66451 Nuevo León (Mexico)

    2015-05-18

    Highlights: • A fully automated flow-based modified-BCR extraction method was developed to evaluate the extractable As of soil. • The MSFIA–HG-AFS system included an UV photo-oxidation step for organic species degradation. • The accuracy and precision of the proposed method were found satisfactory. • The time analysis can be reduced up to eight times by using the proposed flow-based BCR method. • The labile As (F1 + F2) was <50% of total As in soil samples from As-contaminated-mining zones. - Abstract: A fully automated modified three-step BCR flow-through sequential extraction method was developed for the fractionation of the arsenic (As) content from agricultural soil based on a multi-syringe flow injection analysis (MSFIA) system coupled to hydride generation-atomic fluorescence spectrometry (HG-AFS). Critical parameters that affect the performance of the automated system were optimized by exploiting a multivariate approach using a Doehlert design. The validation of the flow-based modified-BCR method was carried out by comparison with the conventional BCR method. Thus, the total As content was determined in the following three fractions: fraction 1 (F1), the acid-soluble or interchangeable fraction; fraction 2 (F2), the reducible fraction; and fraction 3 (F3), the oxidizable fraction. The limits of detection (LOD) were 4.0, 3.4, and 23.6 μg L{sup −1} for F1, F2, and F3, respectively. A wide working concentration range was obtained for the analysis of each fraction, i.e., 0.013–0.800, 0.011–0.900 and 0.079–1.400 mg L{sup −1} for F1, F2, and F3, respectively. The precision of the automated MSFIA–HG-AFS system, expressed as the relative standard deviation (RSD), was evaluated for a 200 μg L{sup −1} As standard solution, and RSD values between 5 and 8% were achieved for the three BCR fractions. The new modified three-step BCR flow-based sequential extraction method was satisfactorily applied for arsenic fractionation in real agricultural

  16. On-line early fault detection and diagnosis of municipal solid waste incinerators

    International Nuclear Information System (INIS)

    Zhao Jinsong; Huang Jianchao; Sun Wei

    2008-01-01

    A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI

  17. Automated Extraction of Genomic DNA from Medically Important Yeast Species and Filamentous Fungi by Using the MagNA Pure LC System

    OpenAIRE

    Loeffler, Juergen; Schmidt, Kathrin; Hebart, Holger; Schumacher, Ulrike; Einsele, Hermann

    2002-01-01

    A fully automated assay was established for the extraction of DNA from clinically important fungi by using the MagNA Pure LC instrument. The test was evaluated by DNA isolation from 23 species of yeast and filamentous fungi and by extractions (n = 28) of serially diluted Aspergillus fumigatus conidia (105 to 0 CFU/ml). Additionally, DNA from 67 clinical specimens was extracted and compared to the manual protocol. The detection limit of the MagNA Pure LC assay of 10 CFU corresponded to the sen...

  18. A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

    Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

  19. A morphogram with the optimal selection of parameters used in morphological analysis for enhancing the ability in bearing fault diagnosis

    International Nuclear Information System (INIS)

    Wang, Dong; Tse, Peter W; Tse, Yiu L

    2012-01-01

    Morphological analysis is a signal processing method that extracts the local morphological features of a signal by intersecting it with a structuring element (SE). When a bearing suffers from a localized fault, an impulse-type cyclic signal is generated. The amplitude and the cyclic time interval of impacts could reflect the health status of the inspected bearing and the cause of defects, respectively. In this paper, an enhanced morphological analysis called ‘morphogram’ is presented for extracting the cyclic impacts caused by a certain bearing fault. Based on the theory of morphology, the morphogram is realized by simple mathematical operators, including Minkowski addition and subtraction. The morphogram is able to detect all possible fault intervals. The most likely fault-interval-based construction index (CI) is maximized to establish the optimal range of the flat SE for the extraction of bearing fault cyclic features so that the type and cause of bearing faults can be easily determined in a time domain. The morphogram has been validated by simulated bearing fault signals, real bearing faulty signals collected from a laboratorial rotary machine and an industrial bearing fault signal. The results show that the morphogram is able to detect all possible bearing fault intervals. Based on the most likely bearing fault interval shown on the morphogram, the CI is effective in determining the optimal parameters of the flat SE for the extraction of bearing fault cyclic features for bearing fault diagnosis. (paper)

  20. Fault diagnosis of rotating machine by isometric feature mapping

    International Nuclear Information System (INIS)

    Zhang, Yun; Li, Benwei; Wang, Lin; Wang, Wen; Wang, Zibin

    2013-01-01

    Principal component analysis (PCA) and linear discriminate analysis (LDA) are well-known linear dimensionality reductions for fault classification. However, since they are linear methods, they perform not well for high-dimensional data that has the nonlinear geometric structure. As kernel extension of PCA, Kernel PCA is used for nonlinear fault classification. However, the performance of Kernel PCA largely depends on its kernel function which can only be empirically selected from finite candidates. Thus, a novel rotating machine fault diagnosis approach based on geometrically motivated nonlinear dimensionality reduction named isometric feature mapping (Isomap) is proposed. The approach can effectively extract the intrinsic nonlinear manifold features embedded in high-dimensional fault data sets. Experimental results with rotor and rolling bearing data show that the proposed approach overcomes the flaw of conventional fault pattern recognition approaches and obviously improves the fault classification performance.

  1. A Study on Integrated Control Network for Multiple Automation Services-1st year report

    Energy Technology Data Exchange (ETDEWEB)

    Hyun, D.H.; Park, B.S.; Kim, M.S.; Lim, Y.H.; Ahn, S.K. [Korea Electric Power Research Institute, Taejon (Korea)

    2002-07-01

    This report describes the development of Integrated and Intelligent Gateway which is under developed. The network operating technique in this report can identifies the causes of the communication faults and can avoid communication network faults in advance. Utility companies spend large financial investment and time for supplying the stabilized power. Since this is deeply related to the reliability of Automation Systems, it is natural to employ Fault-Tolerant communication network for Automation Systems. Use of the network system developed in this report is not limited in DAS. It can be expandable to the many kinds of data services for customer. Thus this report suggests the direction of the communication network development. This 1st year report is composed of following contents, 1) The introduction and problems of DAS. 2) The configuration and functions of IIG. 3) The protocols. (author). 27 refs., 73 figs., 6 tabs.

  2. Automated touch sensing in the mouse tapered beam test using Raspberry Pi.

    Science.gov (United States)

    Ardesch, Dirk Jan; Balbi, Matilde; Murphy, Timothy H

    2017-11-01

    Rodent models of neurological disease such as stroke are often characterized by motor deficits. One of the tests that are used to assess these motor deficits is the tapered beam test, which provides a sensitive measure of bilateral motor function based on foot faults (slips) made by a rodent traversing a gradually narrowing beam. However, manual frame-by-frame scoring of video recordings is necessary to obtain test results, which is time-consuming and prone to human rater bias. We present a cost-effective method for automated touch sensing in the tapered beam test. Capacitive touch sensors detect foot faults onto the beam through a layer of conductive paint, and results are processed and stored on a Raspberry Pi computer. Automated touch sensing using this method achieved high sensitivity (96.2%) as compared to 'gold standard' manual video scoring. Furthermore, it provided a reliable measure of lateralized motor deficits in mice with unilateral photothrombotic stroke: results indicated an increased number of contralesional foot faults for up to 6days after ischemia. The automated adaptation of the tapered beam test produces results immediately after each trial, without the need for labor-intensive post-hoc video scoring. It also increases objectivity of the data as it requires less experimenter involvement during analysis. Automated touch sensing may provide a useful adaptation to the existing tapered beam test in mice, while the simplicity of the hardware lends itself to potential further adaptations to related behavioral tests. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Fully automated synthesis of 11C-acetate as tumor PET tracer by simple modified solid-phase extraction purification

    International Nuclear Information System (INIS)

    Tang, Xiaolan; Tang, Ganghua; Nie, Dahong

    2013-01-01

    Introduction: Automated synthesis of 11 C-acetate ( 11 C-AC) as the most commonly used radioactive fatty acid tracer is performed by a simple, rapid, and modified solid-phase extraction (SPE) purification. Methods: Automated synthesis of 11 C-AC was implemented by carboxylation reaction of MeMgBr on a polyethylene Teflon loop ring with 11 C-CO 2 , followed by acidic hydrolysis with acid and SCX cartridge, and purification on SCX, AG11A8 and C18 SPE cartridges using a commercially available 11 C-tracer synthesizer. Quality control test and animals positron emission tomography (PET) imaging were also carried out. Results: A high and reproducible decay-uncorrected radiochemical yield of (41.0±4.6)% (n=10) was obtained from 11 C-CO 2 within the whole synthesis time about 8 min. The radiochemical purity of 11 C-AC was over 95% by high-performance liquid chromatography (HPLC) analysis. Quality control test and PET imaging showed that 11 C-AC injection produced by the simple SPE procedure was safe and efficient, and was in agreement with the current Chinese radiopharmaceutical quality control guidelines. Conclusion: The novel, simple, and rapid method is readily adapted to the fully automated synthesis of 11 C-AC on several existing commercial synthesis module. The method can be used routinely to produce 11 C-AC for preclinical and clinical studies with PET imaging. - Highlights: • A fully automated synthesis of 11 C-acetate by simple modified solid-phase extraction purification has been developed. • Typical non-decay-corrected yields were (41.0±4.6)% (n=10) • Radiochemical purity was determined by radio-HPLC analysis on a C18 column using the gradient program, instead of expensive organic acid column or anion column. • QC testing (RCP>99%)

  4. Fault Ride Through Capability Enhancement of a Large-Scale PMSG Wind System with Bridge Type Fault Current Limiters

    Directory of Open Access Journals (Sweden)

    ALAM, M. S.

    2018-02-01

    Full Text Available In this paper, bridge type fault current limiter (BFCL is proposed as a potential solution to the fault problems of permanent magnet synchronous generator (PMSG based large-scale wind energy system. As PMSG wind system is more vulnerable to disturbances, it is essential to guarantee the stability during severe disturbances by enhancing the fault ride through capability. BFCL controller has been designed to insert resistance and inductance during the inception of system disturbances in order to limit fault current. Constant capacitor voltage has been maintained by the grid voltage source converter (GVSC controller while current extraction or injection has been achieved by machine VSC (MVSC controller. Symmetrical and unsymmetrical faults have been applied in the system to show the effectiveness of the proposed BFCL solution. PMSG wind system, BFCL and their controllers have been implemented by real time hardware in loop (RTHIL setup with real time digital simulator (RTDS and dSPACE. Another significant feature of this work is that the performance of the proposed BFCL is compared with that of series dynamic braking resistor (SDBR. Comparative RTHIL implementation results show that the proposed BFCL is very efficient in improving system fault ride through capability by limiting the fault current and outperforms SDBR.

  5. A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction.

    Science.gov (United States)

    Ballanger, Bénédicte; Tremblay, Léon; Sgambato-Faure, Véronique; Beaudoin-Gobert, Maude; Lavenne, Franck; Le Bars, Didier; Costes, Nicolas

    2013-08-15

    MRI templates and digital atlases are needed for automated and reproducible quantitative analysis of non-human primate PET studies. Segmenting brain images via multiple atlases outperforms single-atlas labelling in humans. We present a set of atlases manually delineated on brain MRI scans of the monkey Macaca fascicularis. We use this multi-atlas dataset to evaluate two automated methods in terms of accuracy, robustness and reliability in segmenting brain structures on MRI and extracting regional PET measures. Twelve individual Macaca fascicularis high-resolution 3DT1 MR images were acquired. Four individual atlases were created by manually drawing 42 anatomical structures, including cortical and sub-cortical structures, white matter regions, and ventricles. To create the MRI template, we first chose one MRI to define a reference space, and then performed a two-step iterative procedure: affine registration of individual MRIs to the reference MRI, followed by averaging of the twelve resampled MRIs. Automated segmentation in native space was obtained in two ways: 1) Maximum probability atlases were created by decision fusion of two to four individual atlases in the reference space, and transformation back into the individual native space (MAXPROB)(.) 2) One to four individual atlases were registered directly to the individual native space, and combined by decision fusion (PROPAG). Accuracy was evaluated by computing the Dice similarity index and the volume difference. The robustness and reproducibility of PET regional measurements obtained via automated segmentation was evaluated on four co-registered MRI/PET datasets, which included test-retest data. Dice indices were always over 0.7 and reached maximal values of 0.9 for PROPAG with all four individual atlases. There was no significant mean volume bias. The standard deviation of the bias decreased significantly when increasing the number of individual atlases. MAXPROB performed better when increasing the number of

  6. A rule-based fault detection method for air handling units

    Energy Technology Data Exchange (ETDEWEB)

    Schein, J.; Bushby, S. T.; Castro, N. S. [National Institute of Standards and Technology, Gaithersburg, MD (United States); House, J. M. [Iowa Energy Center, Ankeny, IA (United States)

    2006-07-01

    Air handling unit performance assessment rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units (AHUs). Control signals are used to determine the mode of operation of the AHU. A subset of the expert rules which correspond to that mode of operation are then evaluated to determine whether a fault exists. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon the sensor data and control signals that are commonly available in these systems. APAR was tested using data sets collected from a 'hardware-in-the-loop' emulator and from several field sites. APAR was also embedded in commercial AHU controllers and tested in the emulator. (author)

  7. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  8. Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell

    Directory of Open Access Journals (Sweden)

    Minhwan Seo

    2018-06-01

    Full Text Available Early detection of internal short circuit which is main cause of thermal runaway in a lithium-ion battery is necessary to ensure battery safety for users. As a promising fault index, internal short circuit resistance can directly represent degree of the fault because it describes self-discharge phenomenon caused by the internal short circuit clearly. However, when voltages of individual cells in a lithium-ion battery pack are not provided, the effect of internal short circuit in the battery pack is not readily observed in whole terminal voltage of the pack, leading to difficulty in estimating accurate internal short circuit resistance. In this paper, estimating the resistance with the whole terminal voltages and the load currents of the pack, a detection method for the soft internal short circuit in the pack is proposed. Open circuit voltage of a faulted cell in the pack is extracted to reflect the self-discharge phenomenon obviously; this process yields accurate estimates of the resistance. The proposed method is verified with various soft short conditions in both simulations and experiments. The error of estimated resistance does not exceed 31.2% in the experiment, thereby enabling the battery management system to detect the internal short circuit early.

  9. FAULT DETECTION AND LOCALIZATION IN MOTORCYCLES BASED ON THE CHAIN CODE OF PSEUDOSPECTRA AND ACOUSTIC SIGNALS

    Directory of Open Access Journals (Sweden)

    B. S. Anami

    2013-06-01

    Full Text Available Vehicles produce sound signals with varying temporal and spectral properties under different working conditions. These sounds are indicative of the condition of the engine. Fault diagnosis is a significantly difficult task in geographically remote places where expertise is scarce. Automated fault diagnosis can assist riders to assess the health condition of their vehicles. This paper presents a method for fault detection and location in motorcycles based on the chain code of the pseudospectra and Mel-frequency cepstral coefficient (MFCC features of acoustic signals. The work comprises two stages: fault detection and fault location. The fault detection stage uses the chain code of the pseudospectrum as a feature vector. If the motorcycle is identified as faulty, the MFCCs of the same sample are computed and used as features for fault location. Both stages employ dynamic time warping for the classification of faults. Five types of faults in motorcycles are considered in this work. Observed classification rates are over 90% for the fault detection stage and over 94% for the fault location stage. The work identifies other interesting applications in the development of acoustic fingerprints for fault diagnosis of machinery, tuning of musical instruments, medical diagnosis, etc.

  10. Automated mini-column solid-phase extraction cleanup for high-throughput analysis of chemical contaminants in foods by low-pressure gas chromatography – tandem mass spectrometry

    Science.gov (United States)

    This study demonstrated the application of an automated high-throughput mini-cartridge solid-phase extraction (mini-SPE) cleanup for the rapid low-pressure gas chromatography – tandem mass spectrometry (LPGC-MS/MS) analysis of pesticides and environmental contaminants in QuEChERS extracts of foods. ...

  11. Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact fault diagnosis

    Science.gov (United States)

    Yang, Honggang; Lin, Huibin; Ding, Kang

    2018-05-01

    The performance of sparse features extraction by commonly used K-Singular Value Decomposition (K-SVD) method depends largely on the signal segment selected in rolling bearing diagnosis, furthermore, the calculating speed is relatively slow and the dictionary becomes so redundant when the fault signal is relatively long. A new sliding window denoising K-SVD (SWD-KSVD) method is proposed, which uses only one small segment of time domain signal containing impacts to perform sliding window dictionary learning and select an optimal pattern with oscillating information of the rolling bearing fault according to a maximum variance principle. An inner product operation between the optimal pattern and the whole fault signal is performed to enhance the characteristic of the impacts' occurrence moments. Lastly, the signal is reconstructed at peak points of the inner product to realize the extraction of the rolling bearing fault features. Both simulation and experiments verify that the method could extract the fault features effectively.

  12. Planning and Resource Management in an Intelligent Automated Power Management System

    Science.gov (United States)

    Morris, Robert A.

    1991-01-01

    Power system management is a process of guiding a power system towards the objective of continuous supply of electrical power to a set of loads. Spacecraft power system management requires planning and scheduling, since electrical power is a scarce resource in space. The automation of power system management for future spacecraft has been recognized as an important R&D goal. Several automation technologies have emerged including the use of expert systems for automating human problem solving capabilities such as rule based expert system for fault diagnosis and load scheduling. It is questionable whether current generation expert system technology is applicable for power system management in space. The objective of the ADEPTS (ADvanced Electrical Power management Techniques for Space systems) is to study new techniques for power management automation. These techniques involve integrating current expert system technology with that of parallel and distributed computing, as well as a distributed, object-oriented approach to software design. The focus of the current study is the integration of new procedures for automatically planning and scheduling loads with procedures for performing fault diagnosis and control. The objective is the concurrent execution of both sets of tasks on separate transputer processors, thus adding parallelism to the overall management process.

  13. Distribution automation at BC Hydro : a case study

    Energy Technology Data Exchange (ETDEWEB)

    Siew, C. [BC Hydro, Vancouver, BC (Canada). Smart Grid Development Program

    2009-07-01

    This presentation discussed a distribution automation study conducted by BC Hydro to determine methods of improving grid performance by supporting intelligent transmission and distribution systems. The utility's smart grid program includes a number of utility-side and customer-side applications, including enabled demand response, microgrid, and operational efficiency applications. The smart grid program will improve reliability and power quality by 40 per cent, improve conservation and energy efficiency throughout the province, and provide enhanced customer service. Programs and initiatives currently underway at the utility include distribution management, smart metering, distribution automation, and substation automation programs. The utility's automation functionality will include fault interruption and locating, restoration capability, and restoration success. A decision support system has also been established to assist control room and field operating personnel with monitoring and control of the electric distribution system. Protection, control and monitoring (PCM) and volt VAR optimization upgrades are also planned. Reclosers are also being automated, and an automation guide has been developed for switches. tabs., figs.

  14. Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Chi-Man Vong

    2013-01-01

    Full Text Available Engine ignition patterns can be analyzed to identify the engine fault according to both the specific prior domain knowledge and the shape features of the patterns. One of the challenges in ignition system diagnosis is that more than one fault may appear at a time. This kind of problem refers to simultaneous-fault diagnosis. Another challenge is the acquisition of a large amount of costly simultaneous-fault ignition patterns for constructing the diagnostic system because the number of the training patterns depends on the combination of different single faults. The above problems could be resolved by the proposed framework combining feature extraction, probabilistic classification, and decision threshold optimization. With the proposed framework, the features of the single faults in a simultaneous-fault pattern are extracted and then detected using a new probabilistic classifier, namely, pairwise coupling relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is not necessary. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnoses and is superior to the existing approach.

  15. Automated analysis of autoradiographic imagery

    International Nuclear Information System (INIS)

    Bisignani, W.T.; Greenhouse, S.C.

    1975-01-01

    A research programme is described which has as its objective the automated characterization of neurological tissue regions from autoradiographs by utilizing hybrid-resolution image processing techniques. An experimental system is discussed which includes raw imagery, scanning an digitizing equipments, feature-extraction algorithms, and regional characterization techniques. The parameters extracted by these algorithms are presented as well as the regional characteristics which are obtained by operating on the parameters with statistical sampling techniques. An approach is presented for validating the techniques and initial experimental results are obtained from an anlysis of an autoradiograph of a region of the hypothalamus. An extension of these automated techniques to other biomedical research areas is discussed as well as the implications of applying automated techniques to biomedical research problems. (author)

  16. Autonomy, Automation, and Systems

    Science.gov (United States)

    Turner, Philip R.

    1987-02-01

    Aerospace industry interest in autonomy and automation, given fresh impetus by the national goal of establishing a Space Station, is becoming a major item of research and technology development. The promise of new technology arising from research in Artificial Intelligence (AI) has focused much attention on its potential in autonomy and automation. These technologies can improve performance in autonomous control functions that involve planning, scheduling, and fault diagnosis of complex systems. There are, however, many aspects of system and subsystem design in an autonomous system that impact AI applications, but do not directly involve AI technology. Development of a system control architecture, establishment of an operating system within the design, providing command and sensory data collection features appropriate to automated operation, and the use of design analysis tools to support system engineering are specific examples of major design issues. Aspects such as these must also receive attention and technology development support if we are to implement complex autonomous systems within the realistic limitations of mass, power, cost, and available flight-qualified technology that are all-important to a flight project.

  17. Automated solid-phase extraction of herbicides from water for gas chromatographic-mass spectrometric analysis

    Science.gov (United States)

    Meyer, M.T.; Mills, M.S.; Thurman, E.M.

    1993-01-01

    An automated solid-phase extraction (SPE) method was developed for the pre-concentration of chloroacetanilide and triazine herbicides, and two triazine metabolites from 100-ml water samples. Breakthrough experiments for the C18 SPE cartridge show that the two triazine metabolites are not fully retained and that increasing flow-rate decreases their retention. Standard curve r2 values of 0.998-1.000 for each compound were consistently obtained and a quantitation level of 0.05 ??g/l was achieved for each compound tested. More than 10,000 surface and ground water samples have been analyzed by this method.

  18. Diversity requirements for safety critical software-based automation systems

    International Nuclear Information System (INIS)

    Korhonen, J.; Pulkkinen, U.; Haapanen, P.

    1998-03-01

    System vendors nowadays propose software-based systems even for the most critical safety functions in nuclear power plants. Due to the nature and mechanisms of influence of software faults new methods are needed for the safety and reliability evaluation of these systems. In the research project 'Programmable automation systems in nuclear power plants (OHA)' various safety assessment methods and tools for software based systems are developed and evaluated. This report first discusses the (common cause) failure mechanisms in software-based systems, then defines fault-tolerant system architectures to avoid common cause failures, then studies the various alternatives to apply diversity and their influence on system reliability. Finally, a method for the assessment of diversity is described. Other recently published reports in OHA-report series handles the statistical reliability assessment of software based (STUK-YTO-TR 119), usage models in reliability assessment of software-based systems (STUK-YTO-TR 128) and handling of programmable automation in plant PSA-studies (STUK-YTO-TR 129)

  19. Automated solid-phase extraction of phenolic acids using layered double hydroxide-alumina-polymer disks.

    Science.gov (United States)

    Ghani, Milad; Palomino Cabello, Carlos; Saraji, Mohammad; Manuel Estela, Jose; Cerdà, Víctor; Turnes Palomino, Gemma; Maya, Fernando

    2018-01-26

    The application of layered double hydroxide-Al 2 O 3 -polymer mixed-matrix disks for solid-phase extraction is reported for the first time. Al 2 O 3 is embedded in a polymer matrix followed by an in situ metal-exchange process to obtain a layered double hydroxide-Al 2 O 3 -polymer mixed-matrix disk with excellent flow-through properties. The extraction performance of the prepared disks is evaluated as a proof of concept for the automated extraction using sequential injection analysis of organic acids (p-hydroxybenzoic acid, 3,4-dihydroxybenzoic acid, gallic acid) following an anion-exchange mechanism. After the solid-phase extraction, phenolic acids were quantified by reversed-phase high-performance liquid chromatography with diode-array detection using a core-shell silica-C18 stationary phase and isocratic elution (acetonitrile/0.5% acetic acid in pure water, 5:95, v/v). High sensitivity and reproducibility were obtained with limits of detection in the range of 0.12-0.25 μg/L (sample volume, 4 mL), and relative standard deviations between 2.9 and 3.4% (10 μg/L, n = 6). Enrichment factors of 34-39 were obtained. Layered double hydroxide-Al 2 O 3 -polymer mixed-matrix disks had an average lifetime of 50 extractions. Analyte recoveries ranged from 93 to 96% for grape juice and nonalcoholic beer samples. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. Fault size classification of rotating machinery using support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Y. S.; Lee, D. H.; Park, S. K. [Korea Hydro and Nuclear Power Co. Ltd., Daejeon (Korea, Republic of)

    2012-03-15

    Studies on fault diagnosis of rotating machinery have been carried out to obtain a machinery condition in two ways. First is a classical approach based on signal processing and analysis using vibration and acoustic signals. Second is to use artificial intelligence techniques to classify machinery conditions into normal or one of the pre-determined fault conditions. Support Vector Machine (SVM) is well known as intelligent classifier with robust generalization ability. In this study, a two-step approach is proposed to predict fault types and fault sizes of rotating machinery in nuclear power plants using multi-class SVM technique. The model firstly classifies normal and 12 fault types and then identifies their sizes in case of predicting any faults. The time and frequency domain features are extracted from the measured vibration signals and used as input to SVM. A test rig is used to simulate normal and the well-know 12 artificial fault conditions with three to six fault sizes of rotating machinery. The application results to the test data show that the present method can estimate fault types as well as fault sizes with high accuracy for bearing an shaft-related faults and misalignment. Further research, however, is required to identify fault size in case of unbalance, rubbing, looseness, and coupling-related faults.

  1. Fault size classification of rotating machinery using support vector machine

    International Nuclear Information System (INIS)

    Kim, Y. S.; Lee, D. H.; Park, S. K.

    2012-01-01

    Studies on fault diagnosis of rotating machinery have been carried out to obtain a machinery condition in two ways. First is a classical approach based on signal processing and analysis using vibration and acoustic signals. Second is to use artificial intelligence techniques to classify machinery conditions into normal or one of the pre-determined fault conditions. Support Vector Machine (SVM) is well known as intelligent classifier with robust generalization ability. In this study, a two-step approach is proposed to predict fault types and fault sizes of rotating machinery in nuclear power plants using multi-class SVM technique. The model firstly classifies normal and 12 fault types and then identifies their sizes in case of predicting any faults. The time and frequency domain features are extracted from the measured vibration signals and used as input to SVM. A test rig is used to simulate normal and the well-know 12 artificial fault conditions with three to six fault sizes of rotating machinery. The application results to the test data show that the present method can estimate fault types as well as fault sizes with high accuracy for bearing an shaft-related faults and misalignment. Further research, however, is required to identify fault size in case of unbalance, rubbing, looseness, and coupling-related faults

  2. Automated Controller Synthesis for non-Deterministic Piecewise-Affine Hybrid Systems

    DEFF Research Database (Denmark)

    Grunnet, Jacob Deleuran

    formations. This thesis uses a hybrid systems model of a satellite formation with possible actuator faults as a motivating example for developing an automated control synthesis method for non-deterministic piecewise-affine hybrid systems (PAHS). The method does not only open an avenue for further research...... in fault tolerant satellite formation control, but can be used to synthesise controllers for a wide range of systems where external events can alter the system dynamics. The synthesis method relies on abstracting the hybrid system into a discrete game, finding a winning strategy for the game meeting...... game and linear optimisation solvers for controller refinement. To illustrate the efficacy of the method a reoccurring satellite formation example including actuator faults has been used. The end result is the application of PAHSCTRL on the example showing synthesis and simulation of a fault tolerant...

  3. An Integrated Approach of Model checking and Temporal Fault Tree for System Safety Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Koh, Kwang Yong; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)

    2009-10-15

    Digitalization of instruments and control systems in nuclear power plants offers the potential to improve plant safety and reliability through features such as increased hardware reliability and stability, and improved failure detection capability. It however makes the systems and their safety analysis more complex. Originally, safety analysis was applied to hardware system components and formal methods mainly to software. For software-controlled or digitalized systems, it is necessary to integrate both. Fault tree analysis (FTA) which has been one of the most widely used safety analysis technique in nuclear industry suffers from several drawbacks as described in. In this work, to resolve the problems, FTA and model checking are integrated to provide formal, automated and qualitative assistance to informal and/or quantitative safety analysis. Our approach proposes to build a formal model of the system together with fault trees. We introduce several temporal gates based on timed computational tree logic (TCTL) to capture absolute time behaviors of the system and to give concrete semantics to fault tree gates to reduce errors during the analysis, and use model checking technique to automate the reasoning process of FTA.

  4. Ten kilometer vertical Moho offset and shallow velocity contrast along the Denali fault zone from double-difference tomography, receiver functions, and fault zone head waves

    Science.gov (United States)

    Allam, A. A.; Schulte-Pelkum, V.; Ben-Zion, Y.; Tape, C.; Ruppert, N.; Ross, Z. E.

    2017-11-01

    We examine the structure of the Denali fault system in the crust and upper mantle using double-difference tomography, P-wave receiver functions, and analysis (spatial distribution and moveout) of fault zone head waves. The three methods have complementary sensitivity; tomography is sensitive to 3D seismic velocity structure but smooths sharp boundaries, receiver functions are sensitive to (quasi) horizontal interfaces, and fault zone head waves are sensitive to (quasi) vertical interfaces. The results indicate that the Mohorovičić discontinuity is vertically offset by 10 to 15 km along the central 600 km of the Denali fault in the imaged region, with the northern side having shallower Moho depths around 30 km. An automated phase picker algorithm is used to identify 1400 events that generate fault zone head waves only at near-fault stations. At shorter hypocentral distances head waves are observed at stations on the northern side of the fault, while longer propagation distances and deeper events produce head waves on the southern side. These results suggest a reversal of the velocity contrast polarity with depth, which we confirm by computing average 1D velocity models separately north and south of the fault. Using teleseismic events with M ≥ 5.1, we obtain 31,400 P receiver functions and apply common-conversion-point stacking. The results are migrated to depth using the derived 3D tomography model. The imaged interfaces agree with the tomography model, showing a Moho offset along the central Denali fault and also the sub-parallel Hines Creek fault, a suture zone boundary 30 km to the north. To the east, this offset follows the Totschunda fault, which ruptured during the M7.9 2002 earthquake, rather than the Denali fault itself. The combined results suggest that the Denali fault zone separates two distinct crustal blocks, and that the Totschunda and Hines Creeks segments are important components of the fault and Cretaceous-aged suture zone structure.

  5. AUTOMATED ANALYSIS OF AQUEOUS SAMPLES CONTAINING PESTICIDES, ACIDIC/BASIC/NEUTRAL SEMIVOLATILES AND VOLATILE ORGANIC COMPOUNDS BY SOLID PHASE EXTRACTION COUPLED IN-LINE TO LARGE VOLUME INJECTION GC/MS

    Science.gov (United States)

    Data is presented on the development of a new automated system combining solid phase extraction (SPE) with GC/MS spectrometry for the single-run analysis of water samples containing a broad range of organic compounds. The system uses commercially available automated in-line 10-m...

  6. Advanced features of the fault tree solver FTREX

    International Nuclear Information System (INIS)

    Jung, Woo Sik; Han, Sang Hoon; Ha, Jae Joo

    2005-01-01

    This paper presents advanced features of a fault tree solver FTREX (Fault Tree Reliability Evaluation eXpert). Fault tree analysis is one of the most commonly used methods for the safety analysis of industrial systems especially for the probabilistic safety analysis (PSA) of nuclear power plants. Fault trees are solved by the classical Boolean algebra, conventional Binary Decision Diagram (BDD) algorithm, coherent BDD algorithm, and Bayesian networks. FTREX could optionally solve fault trees by the conventional BDD algorithm or the coherent BDD algorithm and could convert the fault trees into the form of the Bayesian networks. The algorithm based on the classical Boolean algebra solves a fault tree and generates MCSs. The conventional BDD algorithm generates a BDD structure of the top event and calculates the exact top event probability. The BDD structure is a factorized form of the prime implicants. The MCSs of the top event could be extracted by reducing the prime implicants in the BDD structure. The coherent BDD algorithm is developed to overcome the shortcomings of the conventional BDD algorithm such as the huge memory requirements and a long run time

  7. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

    Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

  8. Automated Solar Flare Detection and Feature Extraction in High-Resolution and Full-Disk Hα Images

    Science.gov (United States)

    Yang, Meng; Tian, Yu; Liu, Yangyi; Rao, Changhui

    2018-05-01

    In this article, an automated solar flare detection method applied to both full-disk and local high-resolution Hα images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of Hα solar flares.

  9. Automatic identification of otologic drilling faults: a preliminary report.

    Science.gov (United States)

    Shen, Peng; Feng, Guodong; Cao, Tianyang; Gao, Zhiqiang; Li, Xisheng

    2009-09-01

    A preliminary study was carried out to identify parameters to characterize drilling faults when using an otologic drill under various operating conditions. An otologic drill was modified by the addition of four sensors. Under consistent conditions, the drill was used to simulate three important types of drilling faults and the captured data were analysed to extract characteristic signals. A multisensor information fusion system was designed to fuse the signals and automatically identify the faults. When identifying drilling faults, there was a high degree of repeatability and regularity, with an average recognition rate of >70%. This study shows that the variables measured change in a fashion that allows the identification of particular drilling faults, and that it is feasible to use these data to provide rapid feedback for a control system. Further experiments are being undertaken to implement such a system.

  10. Extraction of the number of peroxisomes in yeast cells by automated image analysis.

    Science.gov (United States)

    Niemistö, Antti; Selinummi, Jyrki; Saleem, Ramsey; Shmulevich, Ilya; Aitchison, John; Yli-Harja, Olli

    2006-01-01

    An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results.

  11. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi

    2017-07-06

    Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.

  12. Fault Tolerance in ZigBee Wireless Sensor Networks

    Science.gov (United States)

    Alena, Richard; Gilstrap, Ray; Baldwin, Jarren; Stone, Thom; Wilson, Pete

    2011-01-01

    Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 PRO Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. This technology is supported by System-on-a-Chip solutions, resulting in extremely small and low-power nodes. The Wireless Connections in Space Project addresses the aerospace flight domain for both flight-critical and non-critical avionics. WSNs provide the inherent fault tolerance required for aerospace applications utilizing such technology. The team from Ames Research Center has developed techniques for assessing the fault tolerance of ZigBee WSNs challenged by radio frequency (RF) interference or WSN node failure.

  13. Study on multi-fractal fault diagnosis based on EMD fusion in hydraulic engineering

    International Nuclear Information System (INIS)

    Lu, Shibao; Wang, Jianhua; Xue, Yangang

    2016-01-01

    Highlights: • The measured shafting vibration data signal of the hydroelectric generating set is acquired through EMD. • The vibration signal waveform is identified and purified with EMD to obtain approximation coefficient of various fault signals. • The multi-fractal spectrum provides the distributed geometrical or probabilistic information of point. • EMD provides the real information for the next subsequent analysis and recognition. - Abstract: The vibration signal analysis of the hydraulic turbine unit aims at extracting the characteristic information of the unit vibration. The effective signal processing and information extraction are the key to state monitoring and fault diagnosis of the hydraulic turbine unit. In this paper, the vibration fault diagnosis model is established, which combines EMD, multi-fractal spectrum and modified BP neural network; the vibration signal waveform is identified and purified with EMD to obtain approximation coefficient of various fault signals; the characteristic vector of the vibration fault is acquired with the multi-fractal spectrum algorithm, which is classified and identified as input vector of BP neural network. The signal characteristics are extracted through the waveform, the diagnosis and identification are carried out in combination of the multi-fractal spectrum to provide a new method for fault diagnosis of the hydraulic turbine unit. After the application test, the results show that the method can improve the intelligence and humanization of diagnosis, enhance the man–machine interaction, and produce satisfactory identification result.

  14. A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom.

    Science.gov (United States)

    Chavez, Sofia; Viviano, Joseph; Zamyadi, Mojdeh; Kingsley, Peter B; Kochunov, Peter; Strother, Stephen; Voineskos, Aristotle

    2018-02-01

    To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available. A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs. Copyright © 2017 Elsevier

  15. UBO Detector - A cluster-based, fully automated pipeline for extracting white matter hyperintensities.

    Science.gov (United States)

    Jiang, Jiyang; Liu, Tao; Zhu, Wanlin; Koncz, Rebecca; Liu, Hao; Lee, Teresa; Sachdev, Perminder S; Wen, Wei

    2018-07-01

    We present 'UBO Detector', a cluster-based, fully automated pipeline for extracting and calculating variables for regions of white matter hyperintensities (WMH) (available for download at https://cheba.unsw.edu.au/group/neuroimaging-pipeline). It takes T1-weighted and fluid attenuated inversion recovery (FLAIR) scans as input, and SPM12 and FSL functions are utilised for pre-processing. The candidate clusters are then generated by FMRIB's Automated Segmentation Tool (FAST). A supervised machine learning algorithm, k-nearest neighbor (k-NN), is applied to determine whether the candidate clusters are WMH or non-WMH. UBO Detector generates both image and text (volumes and the number of WMH clusters) outputs for whole brain, periventricular, deep, and lobar WMH, as well as WMH in arterial territories. The computation time for each brain is approximately 15 min. We validated the performance of UBO Detector by showing a) high segmentation (similarity index (SI) = 0.848) and volumetric (intraclass correlation coefficient (ICC) = 0.985) agreement between the UBO Detector-derived and manually traced WMH; b) highly correlated (r 2  > 0.9) and a steady increase of WMH volumes over time; and c) significant associations of periventricular (t = 22.591, p deep (t = 14.523, p < 0.001) WMH volumes generated by UBO Detector with Fazekas rating scores. With parallel computing enabled in UBO Detector, the processing can take advantage of multi-core CPU's that are commonly available on workstations. In conclusion, UBO Detector is a reliable, efficient and fully automated WMH segmentation pipeline. Copyright © 2018 Elsevier Inc. All rights reserved.

  16. Study on fault diagnosis and load feedback control system of combine harvester

    Science.gov (United States)

    Li, Ying; Wang, Kun

    2017-01-01

    In order to timely gain working status parameters of operating parts in combine harvester and improve its operating efficiency, fault diagnosis and load feedback control system is designed. In the system, rotation speed sensors were used to gather these signals of forward speed and rotation speeds of intermediate shaft, conveying trough, tangential and longitudinal flow threshing rotors, grain conveying auger. Using C8051 single chip microcomputer (SCM) as processor for main control unit, faults diagnosis and forward speed control were carried through by rotation speed ratio analysis of each channel rotation speed and intermediate shaft rotation speed by use of multi-sensor fused fuzzy control algorithm, and these processing results would be sent to touch screen and display work status of combine harvester. Field trials manifest that fault monitoring and load feedback control system has good man-machine interaction and the fault diagnosis method based on rotation speed ratios has low false alarm rate, and the system can realize automation control of forward speed for combine harvester.

  17. Bevel Gearbox Fault Diagnosis using Vibration Measurements

    Directory of Open Access Journals (Sweden)

    Hartono Dennis

    2016-01-01

    Full Text Available The use of vibration measurementanalysis has been proven to be effective for gearbox fault diagnosis. However, the complexity of vibration signals observed from a gearbox makes it difficult to accurately detectfaults in the gearbox. This work is based on a comparative studyof several time-frequency signal processing methods that can be used to extract information from transient vibration signals containing useful diagnostic information. Experiments were performed on a bevel gearbox test rig using vibration measurements obtained from accelerometers. Initially, thediscrete wavelet transform was implementedfor vibration signal analysis to extract the frequency content of signal from the relevant frequency region. Several time-frequency signal processing methods werethen incorporated to extract the fault features of vibration signals and their diagnostic performances were compared. It was shown thatthe Short Time Fourier Transform (STFT could not offer a good time resolution to detect the periodicity of the faulty gear tooth due the difficulty in choosing an appropriate window length to capture the impulse signal. The Continuous Wavelet Transform (CWT, on the other hand, was suitable to detection of vibration transients generated by localized fault from a gearbox due to its multi-scale property. However, both methods still require a thorough visual inspection. In contrast, it was shown from the experiments that the diagnostic method using the Cepstrumanalysis could provide a direct indication of the faulty tooth without the need of a thorough visual inspection as required by CWT and STFT.

  18. Partly Duffing Oscillator Stochastic Resonance Method and Its Application on Mechanical Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jian Dang

    2016-01-01

    Full Text Available Due to the fact that the slight fault signals in early failure of mechanical system are usually submerged in heavy background noise, it is unfeasible to extract the weak fault feature via the traditional vibration analysis. Stochastic resonance (SR, as a method of utilizing noise to amplify weak signals in nonlinear dynamical systems, can detect weak signals overwhelmed in the noise. However, based on the analysis of the impact of noise intensity on SR effect, it is concluded that the detection results are dramatically limited by the noise intensity of measured signals, especially for incipient fault feature of mechanical system with poor working environment. Therefore, this paper proposes a partly Duffing oscillator SR method to extract the fault feature of mechanical system. In this method, to locate the appearance of weak fault feature and decrease noise intensity, the permutation entropy index is constructed to select the measured signals for the input of Duffing oscillator system. Then, according to the regulation of system parameters, a reasonable match between the selected signals and Duffing oscillator model is achieved to produce a SR phenomenon and realize the fault diagnosis of mechanical system. Experiment results demonstrate that the proposed method achieves a better effect on the fault diagnosis of mechanical system.

  19. Automated Sampling and Extraction of Krypton from Small Air Samples for Kr-85 Measurement Using Atom Trap Trace Analysis

    International Nuclear Information System (INIS)

    Hebel, S.; Hands, J.; Goering, F.; Kirchner, G.; Purtschert, R.

    2015-01-01

    Atom-Trap-Trace-Analysis (ATTA) provides the capability of measuring the Krypton-85 concentration in microlitre amounts of krypton extracted from air samples of about 1 litre. This sample size is sufficiently small to allow for a range of applications, including on-site spot sampling and continuous sampling over periods of several hours. All samples can be easily handled and transported to an off-site laboratory for ATTA measurement, or stored and analyzed on demand. Bayesian sampling methodologies can be applied by blending samples for bulk measurement and performing in-depth analysis as required. Prerequisite for measurement is the extraction of a pure krypton fraction from the sample. This paper introduces an extraction unit able to isolate the krypton in small ambient air samples with high speed, high efficiency and in a fully automated manner using a combination of cryogenic distillation and gas chromatography. Air samples are collected using an automated smart sampler developed in-house to achieve a constant sampling rate over adjustable time periods ranging from 5 minutes to 3 hours per sample. The smart sampler can be deployed in the field and operate on battery for one week to take up to 60 air samples. This high flexibility of sampling and the fast, robust sample preparation are a valuable tool for research and the application of Kr-85 measurements to novel Safeguards procedures. (author)

  20. In-flight Fault Detection and Isolation in Aircraft Flight Control Systems

    Science.gov (United States)

    Azam, Mohammad; Pattipati, Krishna; Allanach, Jeffrey; Poll, Scott; Patterson-Hine, Ann

    2005-01-01

    In this paper we consider the problem of test design for real-time fault detection and isolation (FDI) in the flight control system of fixed-wing aircraft. We focus on the faults that are manifested in the control surface elements (e.g., aileron, elevator, rudder and stabilizer) of an aircraft. For demonstration purposes, we restrict our focus on the faults belonging to nine basic fault classes. The diagnostic tests are performed on the features extracted from fifty monitored system parameters. The proposed tests are able to uniquely isolate each of the faults at almost all severity levels. A neural network-based flight control simulator, FLTZ(Registered TradeMark), is used for the simulation of various faults in fixed-wing aircraft flight control systems for the purpose of FDI.

  1. Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Chin-Tsung Hsieh

    2014-01-01

    Full Text Available The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

  2. Vehicle Lateral Control Under Fault in Front and/or Rear Sensors: Final Report

    OpenAIRE

    Lu, Guang; Huang, Jihua; Tomizuka, Masayoshi

    2004-01-01

    This report presents the research results of Task Order 4204(TO4204), "Vehicle Lateral Control under Fault in Front and/or Rear Sensors". This project is a continuing effort of the Partners for Advanced Transit and Highways (PATH) on the research of passenger vehicles for Automated Highway Systems (AHS).

  3. A fault diagnosis and operation advising cooperative expert system based on multi-agent technology

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, W.; Bai, X.; Ding, J.; Fang, Z.; Li, Z. [China Electric Power Research Inst., Haidian District, Beijing (China)

    2006-07-01

    Power systems are becoming more and more complex. In addition, the amount of real-time alarm messages from the supervisory control and data acquisition, energy management systems and wide area measurement systems about switchgear and protection are also increasing to a point far beyond the operator's capacity to digest the information. Research and development of a fault diagnosis system is necessary for the timely identification of fault or malfunctioning devices and for realizing the automation functions of dynamic supervisory control system. The prevailing fault diagnosis approaches in power systems include the expert system, artificial neural network, and fault diagnosis based on optimal theory. This paper discussed the advantages and disadvantages of each of these approaches for diagnosing faults. The paper also proposed a new fault diagnosis and operational processing approach based on a cooperative expert system combined with a multi-agent architecture. For solving complex and correlative faults, the cooperative expert system can overcome the deficiency of a single expert system. It can be used not only for diagnosing complex faults in real time but also in providing timely operational advice. The proposed system has been used successfully in a district power grid in China's Shangdong province for a year. 9 refs., 4 figs.

  4. Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis

    Science.gov (United States)

    Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng

    2018-01-01

    Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.

  5. Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis

    Science.gov (United States)

    Girondin, Victor; Pekpe, Komi Midzodzi; Morel, Herve; Cassar, Jean-Philippe

    2013-07-01

    The objective of this paper is to propose a vibration-based automated framework dealing with local faults occurring on bearings in the transmission of a helicopter. The knowledge of the shaft speed and kinematic computation provide theoretical frequencies that reveal deteriorations on the inner and outer races, on the rolling elements or on the cage. In practice, the theoretical frequencies of bearing faults may be shifted. They may also be masked by parasitical frequencies because the numerous noisy vibrations and the complexity of the transmission mechanics make the signal spectrum very profuse. Consequently, detection methods based on the monitoring of the theoretical frequencies may lead to wrong decisions. In order to deal with this drawback, we propose to readjust the fault frequencies from the theoretical frequencies using the redundancy introduced by the harmonics. The proposed method provides the confidence index of the readjusted frequency. Minor variations in shaft speed may induce random jitters. The change of the contact surface or of the transmission path brings also a random component in amplitude and phase. These random components in the signal destroy spectral localization of frequencies and thus hide the fault occurrence in the spectrum. Under the hypothesis that these random signals can be modeled as cyclostationary signals, the envelope spectrum can reveal that hidden patterns. In order to provide an indicator estimating fault severity, statistics are proposed. They make the hypothesis that the harmonics at the readjusted frequency are corrupted with an additive normally distributed noise. In this case, the statistics computed from the spectra are chi-square distributed and a signal-to-noise indicator is proposed. The algorithms are then tested with data from two test benches and from flight conditions. The bearing type and the radial load are the main differences between the experiences on the benches. The fault is mainly visible in the

  6. Feature Extraction Method for High Impedance Ground Fault Localization in Radial Power Distribution Networks

    DEFF Research Database (Denmark)

    Jensen, Kåre Jean; Munk, Steen M.; Sørensen, John Aasted

    1998-01-01

    A new approach to the localization of high impedance ground faults in compensated radial power distribution networks is presented. The total size of such networks is often very large and a major part of the monitoring of these is carried out manually. The increasing complexity of industrial...... of three phase voltages and currents. The method consists of a feature extractor, based on a grid description of the feeder by impulse responses, and a neural network for ground fault localization. The emphasis of this paper is the feature extractor, and the detection of the time instance of a ground fault...... processes and communication systems lead to demands for improved monitoring of power distribution networks so that the quality of power delivery can be kept at a controlled level. The ground fault localization method for each feeder in a network is based on the centralized frequency broadband measurement...

  7. Human-centered automation and AI - Ideas, insights, and issues from the Intelligent Cockpit Aids research effort

    Science.gov (United States)

    Abbott, Kathy H.; Schutte, Paul C.

    1989-01-01

    A development status evaluation is presented for the NASA-Langley Intelligent Cockpit Aids research program, which encompasses AI, human/machine interfaces, and conventional automation. Attention is being given to decision-aiding concepts for human-centered automation, with emphasis on inflight subsystem fault management, inflight mission replanning, and communications management. The cockpit envisioned is for advanced commercial transport aircraft.

  8. Research of Control System and Fault Diagnosis of the Sound-absorbing Board Production Line

    Directory of Open Access Journals (Sweden)

    Yanjun Xiao

    2014-08-01

    Full Text Available Programmable Logic Controller is the core of the control system of the sound- absorbing board production line and the design of fault diagnosis is an essential modules in the sound- absorbing board production line. The article discourses the application of PLC in the control system of the production line, and designs the methods of grading treatment and prevention of troubles, which makes use of PLC’S logic functions. The method has good expansibility, and has good guidance to the fault diagnosis in other automation equipments.

  9. Fault morphology of the lyo Fault, the Median Tectonic Line Active Fault System

    OpenAIRE

    後藤, 秀昭

    1996-01-01

    In this paper, we investigated the various fault features of the lyo fault and depicted fault lines or detailed topographic map. The results of this paper are summarized as follows; 1) Distinct evidence of the right-lateral movement is continuously discernible along the lyo fault. 2) Active fault traces are remarkably linear suggesting that the angle of fault plane is high. 3) The lyo fault can be divided into four segments by jogs between left-stepping traces. 4) The mean slip rate is 1.3 ~ ...

  10. Mode automata and their compilation into fault trees

    International Nuclear Information System (INIS)

    Rauzy, Antoine

    2002-01-01

    In this article, we advocate the use of mode automata as a high level representation language for reliability studies. Mode automata are states/transitions based representations with the additional notion of flow. They can be seen as a generalization of both finite capacity Petri nets and block diagrams. They can be assembled into hierarchies by means of composition operations. The contribution of this article is twofold. First, we introduce mode automata and we discuss their relationship with other formalisms. Second, we propose an algorithm to compile mode automata into Boolean equations (fault trees). Such a compilation is of interest for two reasons. First, assessment tools for Boolean models are much more efficient than those for states/transitions models. Second, the automated generation of fault trees from higher level representations makes easier their maintenance through the life cycle of systems under study

  11. A Transient Fault Recognition Method for an AC-DC Hybrid Transmission System Based on MMC Information Fusion

    Directory of Open Access Journals (Sweden)

    Jikai Chen

    2016-12-01

    Full Text Available At present, the research is still in the primary stage in the process of fault disturbance energy transfer in the multilevel modular converter based high voltage direct current (HVDC-MMC. An urgent problem is how to extract and analyze the fault features hidden in MMC electrical information in further studies on the HVDC system. Aiming at the above, this article analyzes the influence of AC transient disturbance on electrical signals of MMC. At the same time, it is found that the energy distribution of electrical signals in MMC is different for different arms in the same frequency bands after the discrete wavelet packet transformation (DWPT. Renyi wavelet packet energy entropy (RWPEE and Renyi wavelet packet time entropy (RWPTE are proposed and applied to AC transient fault feature extraction from electrical signals in MMC. Using the feature extraction results of Renyi wavelet packet entropy (RWPE, a novel recognition method is put forward to recognize AC transient faults using the information fusion technology. Theoretical analysis and experimental results show that the proposed method is available to recognize transient AC faults.

  12. Automatic fault tracing of active faults in the Sutlej valley (NW-Himalayas, India)

    Science.gov (United States)

    Janda, C.; Faber, R.; Hager, C.; Grasemann, B.

    2003-04-01

    In the Sutlej Valley the Lesser Himalayan Crystalline Sequence (LHCS) is actively extruding between the Munsiari Thrust (MT) at the base, and the Karcham Normal Fault (KNF) at the top. The clear evidences for ongoing deformation are brittle faults in Holocene lake deposits, hot springs activity near the faults and dramatically younger cooling ages within the LHCS (Vannay and Grasemann, 2001). Because these brittle fault zones obviously influence the morphology in the field we developed a new method for automatically tracing the intersections of planar fault geometries with digital elevation models (Faber, 2002). Traditional mapping techniques use structure contours (i.e. lines or curves connecting points of equal elevation on a geological structure) in order to construct intersections of geological structures with topographic maps. However, even if the geological structure is approximated by a plane and therefore structure contours are equally spaced lines, this technique is rather time consuming and inaccurate, because errors are cumulative. Drawing structure contours by hand makes it also impossible to slightly change the azimuth and dip direction of the favoured plane without redrawing everything from the beginning on. However, small variations of the fault position which are easily possible by either inaccuracies of measurement in the field or small local variations in the trend and/or dip of the fault planes can have big effects on the intersection with topography. The developed method allows to interactively view intersections in a 2D and 3D mode. Unlimited numbers of planes can be moved separately in 3 dimensions (translation and rotation) and intersections with the topography probably following morphological features can be mapped. Besides the increase of efficiency this method underlines the shortcoming of classical lineament extraction ignoring the dip of planar structures. Using this method, areas of active faulting influencing the morphology, can be

  13. The SSM/PMAD automated test bed project

    Science.gov (United States)

    Lollar, Louis F.

    1991-01-01

    The Space Station Module/Power Management and Distribution (SSM/PMAD) autonomous subsystem project was initiated in 1984. The project's goal has been to design and develop an autonomous, user-supportive PMAD test bed simulating the SSF Hab/Lab module(s). An eighteen kilowatt SSM/PMAD test bed model with a high degree of automated operation has been developed. This advanced automation test bed contains three expert/knowledge based systems that interact with one another and with other more conventional software residing in up to eight distributed 386-based microcomputers to perform the necessary tasks of real-time and near real-time load scheduling, dynamic load prioritizing, and fault detection, isolation, and recovery (FDIR).

  14. Managing expectations: assessment of chemistry databases generated by automated extraction of chemical structures from patents.

    Science.gov (United States)

    Senger, Stefan; Bartek, Luca; Papadatos, George; Gaulton, Anna

    2015-12-01

    First public disclosure of new chemical entities often takes place in patents, which makes them an important source of information. However, with an ever increasing number of patent applications, manual processing and curation on such a large scale becomes even more challenging. An alternative approach better suited for this large corpus of documents is the automated extraction of chemical structures. A number of patent chemistry databases generated by using the latter approach are now available but little is known that can help to manage expectations when using them. This study aims to address this by comparing two such freely available sources, SureChEMBL and IBM SIIP (IBM Strategic Intellectual Property Insight Platform), with manually curated commercial databases. When looking at the percentage of chemical structures successfully extracted from a set of patents, using SciFinder as our reference, 59 and 51 % were also found in our comparison in SureChEMBL and IBM SIIP, respectively. When performing this comparison with compounds as starting point, i.e. establishing if for a list of compounds the databases provide the links between chemical structures and patents they appear in, we obtained similar results. SureChEMBL and IBM SIIP found 62 and 59 %, respectively, of the compound-patent pairs obtained from Reaxys. In our comparison of automatically generated vs. manually curated patent chemistry databases, the former successfully provided approximately 60 % of links between chemical structure and patents. It needs to be stressed that only a very limited number of patents and compound-patent pairs were used for our comparison. Nevertheless, our results will hopefully help to manage expectations of users of patent chemistry databases of this type and provide a useful framework for more studies like ours as well as guide future developments of the workflows used for the automated extraction of chemical structures from patents. The challenges we have encountered

  15. The evolution of automation and robotics in manned spaceflight

    Science.gov (United States)

    Moser, T. L.; Erickson, J. D.

    1986-01-01

    The evolution of automation on all manned spacecraft including the Space Shuttle is reviewed, and a concept for increasing automation and robotics from the current Shuttle Remote Manipulator System (RMS) to an autonomous system is presented. The requirements for robotic elements are identified for various functions on the Space Station, including extravehicular functions and functions within laboratory and habitation modules which expand man's capacity in space and allow selected teleoperation from the ground. The initial Space Station will employ a telerobot and necessary knowledge based systems as an advisory to the crew on monitoring, fault diagnosis, and short term planning and scheduling.

  16. Early detection of incipient faults in power plants using accelerated neural network learning

    International Nuclear Information System (INIS)

    Parlos, A.G.; Jayakumar, M.; Atiya, A.

    1992-01-01

    An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels

  17. Operating procedure automation to enhance safety of nuclear power plants

    International Nuclear Information System (INIS)

    Husseiny, A.A.; Sabri, Z.A.; Adams, S.K.; Rodriguez, R.J.; Packer, D.; Holmes, J.W.

    1989-01-01

    Use of logic statements and computer assist are explored as means for automation and improvement on design of operating procedures including those employed in abnormal and emergency situations. Operating procedures for downpower and loss of forced circulation are used for demonstration. Human-factors analysis is performed on generic emergency operating procedures for three strategies of control; manual, semi-automatic and automatic, using standard emergency operating procedures. Such preliminary analysis shows that automation of procedures is feasible provided that fault-tolerant software and hardware become available for design of the controllers. Recommendations are provided for tests to substantiate the promise of enhancement of plant safety. Adequate design of operating procedures through automation may alleviate several major operational problems of nuclear power plants. Also, automation of procedures is necessary for partial or overall automatic control of plants. Fully automatic operations are needed for space applications while supervised automation of land-based and offshore plants may become the thrust of new generation of nulcear power plants. (orig.)

  18. Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

    Science.gov (United States)

    Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

    2016-09-01

    For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

  19. Automatic fault tree generation in the EPR PSA project

    International Nuclear Information System (INIS)

    Villatte, N; Nonclercq, P.; Taupy, S.

    2012-01-01

    Tools (KB3 and Atelier EPS) have been developed at EDF to assist the analysts in building fault trees for PSA (Probabilistic Safety Assessment) and importing them into RiskSpectrum (RiskSpectrum is a Swedish code used at EDF for PSA). System modelling is performed using KB3 software with a knowledge base describing generic classes of components with their behaviour and failure modes. Using these classes of components, the analyst can describe (using a graphical system editor): a simplified system diagram from the mechanical system drawings and functional descriptions, the missions of the studied system (in a form of high level fault trees) and its different configurations for the missions. He can also add specific knowledge about the system. Then, the analyst chooses missions and configurations to specify and launch fault trees generations. From the system description, KB3 produces by backward-chaining on rules, detailed system fault trees. These fault trees are finally imported into RiskSpectrum (they are converted by Atelier EPS into a format readable by RiskSpectrum). KB3 and Atelier EPS have been used to create the majority of the fault trees for the EDF EPR Probabilistic Safety Analysis conducted from November 2009 to March 2010. 25 systems were modelled, and 127 fault trees were automatically generated in a rather short time by different analysts with the help of these tools. A feedback shows a lot of advantages to use KB3 and Atelier EPS: homogeneity and consistency between the different generated fault trees, traceability of modelling, control of modelling and last but not least: the automation of detailed fault tree creation relieves the human analyst of this tedious task so that he can focus his attention on more important tasks: modelling the failure of a function. This industrial application has also helped us gather an interesting feedback from the analysts that should help us improve the handling of the tools. We propose in this paper indeed some

  20. Fault Tolerant Autonomous Lateral Control for Heavy Vehicles

    OpenAIRE

    Talbot, Craig Matthew; Papadimitriou, Iakovos; Tomizuka, Masayoshi

    2004-01-01

    This report summarizes the research results of TO4233, "Fault Tolerant Autonomous Lateral Control for Heavy Vehicles". This project represents a continuing effort of PATH's research on Automated Highway Systems (AHS) and more specifically in the area of heavy vehicles. Research on the lateral control of heavy vehicles for AHS has been going on at PATH since 1993. MOU129, "Steering and Braking Control of Heavy Duty Vehicles" was the first project and it was followed by MOU242, "Lateral Control...

  1. Concept of a computer network architecture for complete automation of nuclear power plants

    International Nuclear Information System (INIS)

    Edwards, R.M.; Ray, A.

    1990-01-01

    The state of the art in automation of nuclear power plants has been largely limited to computerized data acquisition, monitoring, display, and recording of process signals. Complete automation of nuclear power plants, which would include plant operations, control, and management, fault diagnosis, and system reconfiguration with efficient and reliable man/machine interactions, has been projected as a realistic goal. This paper presents the concept of a computer network architecture that would use a high-speed optical data highway to integrate diverse, interacting, and spatially distributed functions that are essential for a fully automated nuclear power plant

  2. Automated Water Extraction Index

    DEFF Research Database (Denmark)

    Feyisa, Gudina Legese; Meilby, Henrik; Fensholt, Rasmus

    2014-01-01

    Classifying surface cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic classification tasks is to distinguish water bodies from dry land surfaces. Landsat imagery is among the most widely used sources of data in remote sensing of water...... resources; and although several techniques of surface water extraction using Landsat data are described in the literature, their application is constrained by low accuracy in various situations. Besides, with the use of techniques such as single band thresholding and two-band indices, identifying...... an appropriate threshold yielding the highest possible accuracy is a challenging and time consuming task, as threshold values vary with location and time of image acquisition. The purpose of this study was therefore to devise an index that consistently improves water extraction accuracy in the presence...

  3. Hypothetical Scenario Generator for Fault-Tolerant Diagnosis

    Science.gov (United States)

    James, Mark

    2007-01-01

    The Hypothetical Scenario Generator for Fault-tolerant Diagnostics (HSG) is an algorithm being developed in conjunction with other components of artificial- intelligence systems for automated diagnosis and prognosis of faults in spacecraft, aircraft, and other complex engineering systems. By incorporating prognostic capabilities along with advanced diagnostic capabilities, these developments hold promise to increase the safety and affordability of the affected engineering systems by making it possible to obtain timely and accurate information on the statuses of the systems and predicting impending failures well in advance. The HSG is a specific instance of a hypothetical- scenario generator that implements an innovative approach for performing diagnostic reasoning when data are missing. The special purpose served by the HSG is to (1) look for all possible ways in which the present state of the engineering system can be mapped with respect to a given model and (2) generate a prioritized set of future possible states and the scenarios of which they are parts.

  4. Design of neuro fuzzy fault tolerant control using an adaptive observer

    International Nuclear Information System (INIS)

    Anita, R.; Umamaheswari, B.; Viswanathan, B.

    2001-01-01

    New methodologies and concepts are developed in the control theory to meet the ever-increasing demands in industrial applications. Fault detection and diagnosis of technical processes have become important in the course of progressive automation in the operation of groups of electric drives. When a group of electric drives is under operation, fault tolerant control becomes complicated. For multiple motors in operation, fault detection and diagnosis might prove to be difficult. Estimation of all states and parameters of all drives is necessary to analyze the actuator and sensor faults. To maintain system reliability, detection and isolation of failures should be performed quickly and accurately, and hardware should be properly integrated. Luenberger full order observer can be used for estimation of the entire states in the system for the detection of actuator and sensor failures. Due to the insensitivity of the Luenberger observer to the system parameter variations, state estimation becomes inaccurate under the varying parameter conditions of the drives. Consequently, the estimation performance deteriorates, resulting in ordinary state observers unsuitable for fault detection technique. Therefore an adaptive observe, which can estimate the system states and parameter and detect the faults simultaneously, is designed in our paper. For a Group of D C drives, there may be parameter variations for some of the drives, and for other drives, there may not be parameter variations depending on load torque, friction, etc. So, estimation of all states and parameters of all drives is carried out using an adaptive observer. If there is any deviation with the estimated values, it is understood that fault has occurred and the nature of the fault, whether sensor fault or actuator fault, is determined by neural fuzzy network, and fault tolerant control is reconfigured. Experimental results with neuro fuzzy system using adaptive observer-based fault tolerant control are good, so as

  5. Automated data extraction from general practice records in an Australian setting: trends in influenza-like illness in sentinel general practices and emergency departments.

    Science.gov (United States)

    Liljeqvist, Gösta T H; Staff, Michael; Puech, Michele; Blom, Hans; Torvaldsen, Siranda

    2011-06-06

    Influenza intelligence in New South Wales (NSW), Australia is derived mainly from emergency department (ED) presentations and hospital and intensive care admissions, which represent only a portion of influenza-like illness (ILI) in the population. A substantial amount of the remaining data lies hidden in general practice (GP) records. Previous attempts in Australia to gather ILI data from GPs have given them extra work. We explored the possibility of applying automated data extraction from GP records in sentinel surveillance in an Australian setting.The two research questions asked in designing the study were: Can syndromic ILI data be extracted automatically from routine GP data? How do ILI trends in sentinel general practice compare with ILI trends in EDs? We adapted a software program already capable of automated data extraction to identify records of patients with ILI in routine electronic GP records in two of the most commonly used commercial programs. This tool was applied in sentinel sites to gather retrospective data for May-October 2007-2009 and in real-time for the same interval in 2010. The data were compared with that provided by the Public Health Real-time Emergency Department Surveillance System (PHREDSS) and with ED data for the same periods. The GP surveillance tool identified seasonal trends in ILI both retrospectively and in near real-time. The curve of seasonal ILI was more responsive and less volatile than that of PHREDSS on a local area level. The number of weekly ILI presentations ranged from 8 to 128 at GP sites and from 0 to 18 in EDs in non-pandemic years. Automated data extraction from routine GP records offers a means to gather data without introducing any additional work for the practitioner. Adding this method to current surveillance programs will enhance their ability to monitor ILI and to detect early warning signals of new ILI events.

  6. Automated diagnostics scoping study. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Quadrel, R.W.; Lash, T.A.

    1994-06-01

    The objective of the Automated Diagnostics Scoping Study was to investigate the needs for diagnostics in building operation and to examine some of the current technologies in automated diagnostics that can address these needs. The study was conducted in two parts. In the needs analysis, the authors interviewed facility managers and engineers at five building sites. In the technology survey, they collected published information on automated diagnostic technologies in commercial and military applications as well as on technologies currently under research. The following describe key areas that the authors identify for the research, development, and deployment of automated diagnostic technologies: tools and techniques to aid diagnosis during building commissioning, especially those that address issues arising from integrating building systems and diagnosing multiple simultaneous faults; technologies to aid diagnosis for systems and components that are unmonitored or unalarmed; automated capabilities to assist cause-and-effect exploration during diagnosis; inexpensive, reliable sensors, especially those that expand the current range of sensory input; technologies that aid predictive diagnosis through trend analysis; integration of simulation and optimization tools with building automation systems to optimize control strategies and energy performance; integration of diagnostic, control, and preventive maintenance technologies. By relating existing technologies to perceived and actual needs, the authors reached some conclusions about the opportunities for automated diagnostics in building operation. Some of a building operator`s needs can be satisfied by off-the-shelf hardware and software. Other needs are not so easily satisfied, suggesting directions for future research. Their conclusions and suggestions are offered in the final section of this study.

  7. Multiscale singular value manifold for rotating machinery fault diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Yi; Lu, BaoChun; Zhang, Deng Feng [School of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing (United States)

    2017-01-15

    Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis.

  8. Validation of an automated solid-phase extraction method for the analysis of 23 opioids, cocaine, and metabolites in urine with ultra-performance liquid chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Ramírez Fernández, María del Mar; Van Durme, Filip; Wille, Sarah M R; di Fazio, Vincent; Kummer, Natalie; Samyn, Nele

    2014-06-01

    The aim of this work was to automate a sample preparation procedure extracting morphine, hydromorphone, oxymorphone, norcodeine, codeine, dihydrocodeine, oxycodone, 6-monoacetyl-morphine, hydrocodone, ethylmorphine, benzoylecgonine, cocaine, cocaethylene, tramadol, meperidine, pentazocine, fentanyl, norfentanyl, buprenorphine, norbuprenorphine, propoxyphene, methadone and 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine from urine samples. Samples were extracted by solid-phase extraction (SPE) with cation exchange cartridges using a TECAN Freedom Evo 100 base robotic system, including a hydrolysis step previous extraction when required. Block modules were carefully selected in order to use the same consumable material as in manual procedures to reduce cost and/or manual sample transfers. Moreover, the present configuration included pressure monitoring pipetting increasing pipetting accuracy and detecting sampling errors. The compounds were then separated in a chromatographic run of 9 min using a BEH Phenyl analytical column on a ultra-performance liquid chromatography-tandem mass spectrometry system. Optimization of the SPE was performed with different wash conditions and elution solvents. Intra- and inter-day relative standard deviations (RSDs) were within ±15% and bias was within ±15% for most of the compounds. Recovery was >69% (RSD automated SPE system was observed. The extracted samples were stable for 72 h in the autosampler (4°C). This method was applied to authentic samples (from forensic and toxicology cases) and to proficiency testing schemes containing cocaine, heroin, buprenorphine and methadone, offering fast and reliable results. Automation resulted in improved precision and accuracy, and a minimum operator intervention, leading to safer sample handling and less time-consuming procedures.

  9. Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

    Directory of Open Access Journals (Sweden)

    Guoyang Yan

    2014-01-01

    Full Text Available Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA and fisher discriminate analysis (FDA.

  10. Automated misfire diagnosis in engines using torsional vibration and block rotation

    International Nuclear Information System (INIS)

    Chen, J; Randall, R B; Peeters, B; Auweraer, H Van der; Desmet, W

    2012-01-01

    Even though a lot of research has gone into diagnosing misfire in IC engines, most approaches use torsional vibration of the crankshaft, and only a few use the rocking motion (roll) of the engine block. Additionally, misfire diagnosis normally requires an expert to interpret the analysis results from measured vibration signals. Artificial Neural Networks (ANNs) are potential tools for the automated misfire diagnosis of IC engines, as they can learn the patterns corresponding to various faults. This paper proposes an ANN-based automated diagnostic system which combines torsional vibration and rotation of the block for more robust misfire diagnosis. A critical issue with ANN applications is the network training, and it is improbable and/or uneconomical to expect to experience a sufficient number of different faults, or generate them in seeded tests, to obtain sufficient experimental results for the network training. Therefore, new simulation models, which can simulate combustion faults in engines, were developed. The simulation models are based on the thermodynamic and mechanical principles of IC engines and therefore the proposed misfire diagnostic system can in principle be adapted for any engine. During the building process of the models, based on a particular engine, some mechanical and physical parameters, for example the inertial properties of the engine parts and parameters of engine mounts, were first measured and calculated. A series of experiments were then carried out to capture the vibration signals for both normal condition and with a range of faults. The simulation models were updated and evaluated by the experimental results. Following the signal processing of the experimental and simulation signals, the best features were selected as the inputs to ANN networks. The automated diagnostic system comprises three stages: misfire detection, misfire localization and severity identification. Multi-layer Perceptron (MLP) and Probabilistic Neural Networks were

  11. High-frequency maximum observable shaking map of Italy from fault sources

    KAUST Repository

    Zonno, Gaetano

    2012-03-17

    We present a strategy for obtaining fault-based maximum observable shaking (MOS) maps, which represent an innovative concept for assessing deterministic seismic ground motion at a regional scale. Our approach uses the fault sources supplied for Italy by the Database of Individual Seismogenic Sources, and particularly by its composite seismogenic sources (CSS), a spatially continuous simplified 3-D representation of a fault system. For each CSS, we consider the associated Typical Fault, i. e., the portion of the corresponding CSS that can generate the maximum credible earthquake. We then compute the high-frequency (1-50 Hz) ground shaking for a rupture model derived from its associated maximum credible earthquake. As the Typical Fault floats within its CSS to occupy all possible positions of the rupture, the high-frequency shaking is updated in the area surrounding the fault, and the maximum from that scenario is extracted and displayed on a map. The final high-frequency MOS map of Italy is then obtained by merging 8,859 individual scenario-simulations, from which the ground shaking parameters have been extracted. To explore the internal consistency of our calculations and validate the results of the procedure we compare our results (1) with predictions based on the Next Generation Attenuation ground-motion equations for an earthquake of M w 7.1, (2) with the predictions of the official Italian seismic hazard map, and (3) with macroseismic intensities included in the DBMI04 Italian database. We then examine the uncertainties and analyse the variability of ground motion for different fault geometries and slip distributions. © 2012 Springer Science+Business Media B.V.

  12. High-frequency maximum observable shaking map of Italy from fault sources

    KAUST Repository

    Zonno, Gaetano; Basili, Roberto; Meroni, Fabrizio; Musacchio, Gemma; Mai, Paul Martin; Valensise, Gianluca

    2012-01-01

    We present a strategy for obtaining fault-based maximum observable shaking (MOS) maps, which represent an innovative concept for assessing deterministic seismic ground motion at a regional scale. Our approach uses the fault sources supplied for Italy by the Database of Individual Seismogenic Sources, and particularly by its composite seismogenic sources (CSS), a spatially continuous simplified 3-D representation of a fault system. For each CSS, we consider the associated Typical Fault, i. e., the portion of the corresponding CSS that can generate the maximum credible earthquake. We then compute the high-frequency (1-50 Hz) ground shaking for a rupture model derived from its associated maximum credible earthquake. As the Typical Fault floats within its CSS to occupy all possible positions of the rupture, the high-frequency shaking is updated in the area surrounding the fault, and the maximum from that scenario is extracted and displayed on a map. The final high-frequency MOS map of Italy is then obtained by merging 8,859 individual scenario-simulations, from which the ground shaking parameters have been extracted. To explore the internal consistency of our calculations and validate the results of the procedure we compare our results (1) with predictions based on the Next Generation Attenuation ground-motion equations for an earthquake of M w 7.1, (2) with the predictions of the official Italian seismic hazard map, and (3) with macroseismic intensities included in the DBMI04 Italian database. We then examine the uncertainties and analyse the variability of ground motion for different fault geometries and slip distributions. © 2012 Springer Science+Business Media B.V.

  13. Multiwavelet packet entropy and its application in transmission line fault recognition and classification.

    Science.gov (United States)

    Liu, Zhigang; Han, Zhiwei; Zhang, Yang; Zhang, Qiaoge

    2014-11-01

    Multiwavelets possess better properties than traditional wavelets. Multiwavelet packet transformation has more high-frequency information. Spectral entropy can be applied as an analysis index to the complexity or uncertainty of a signal. This paper tries to define four multiwavelet packet entropies to extract the features of different transmission line faults, and uses a radial basis function (RBF) neural network to recognize and classify 10 fault types of power transmission lines. First, the preprocessing and postprocessing problems of multiwavelets are presented. Shannon entropy and Tsallis entropy are introduced, and their difference is discussed. Second, multiwavelet packet energy entropy, time entropy, Shannon singular entropy, and Tsallis singular entropy are defined as the feature extraction methods of transmission line fault signals. Third, the plan of transmission line fault recognition using multiwavelet packet entropies and an RBF neural network is proposed. Finally, the experimental results show that the plan with the four multiwavelet packet energy entropies defined in this paper achieves better performance in fault recognition. The performance with SA4 (symmetric antisymmetric) multiwavelet packet Tsallis singular entropy is the best among the combinations of different multiwavelet packets and the four multiwavelet packet entropies.

  14. Layered clustering multi-fault diagnosis for hydraulic piston pump

    Science.gov (United States)

    Du, Jun; Wang, Shaoping; Zhang, Haiyan

    2013-04-01

    Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump. In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.

  15. Fault tolerant control based on active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2005-01-01

    An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR......) part. The FTC architecture can be applied for additive faults, parametric faults, and for system structural changes. Only parametric faults will be considered in this paper. The main focus in this paper is on the use of the new approach of active fault diagnosis in connection with FTC. The active fault...... diagnosis approach is based on including an auxiliary input in the system. A fault signature matrix is introduced in connection with AFD, given as the transfer function from the auxiliary input to the residual output. This can be considered as a generalization of the passive fault diagnosis case, where...

  16. Can we replace curation with information extraction software?

    Science.gov (United States)

    Karp, Peter D

    2016-01-01

    Can we use programs for automated or semi-automated information extraction from scientific texts as practical alternatives to professional curation? I show that error rates of current information extraction programs are too high to replace professional curation today. Furthermore, current IEP programs extract single narrow slivers of information, such as individual protein interactions; they cannot extract the large breadth of information extracted by professional curators for databases such as EcoCyc. They also cannot arbitrate among conflicting statements in the literature as curators can. Therefore, funding agencies should not hobble the curation efforts of existing databases on the assumption that a problem that has stymied Artificial Intelligence researchers for more than 60 years will be solved tomorrow. Semi-automated extraction techniques appear to have significantly more potential based on a review of recent tools that enhance curator productivity. But a full cost-benefit analysis for these tools is lacking. Without such analysis it is possible to expend significant effort developing information-extraction tools that automate small parts of the overall curation workflow without achieving a significant decrease in curation costs.Database URL. © The Author(s) 2016. Published by Oxford University Press.

  17. Fault Tree Analysis with Temporal Gates and Model Checking Technique for Qualitative System Safety Analysis

    International Nuclear Information System (INIS)

    Koh, Kwang Yong; Seong, Poong Hyun

    2010-01-01

    Fault tree analysis (FTA) has suffered from several drawbacks such that it uses only static gates and hence can not capture dynamic behaviors of the complex system precisely, and it is in lack of rigorous semantics, and reasoning process which is to check whether basic events really cause top events is done manually and hence very labor-intensive and time-consuming for the complex systems while it has been one of the most widely used safety analysis technique in nuclear industry. Although several attempts have been made to overcome this problem, they can not still do absolute or actual time modeling because they adapt relative time concept and can capture only sequential behaviors of the system. In this work, to resolve the problems, FTA and model checking are integrated to provide formal, automated and qualitative assistance to informal and/or quantitative safety analysis. Our approach proposes to build a formal model of the system together with fault trees. We introduce several temporal gates based on timed computational tree logic (TCTL) to capture absolute time behaviors of the system and to give concrete semantics to fault tree gates to reduce errors during the analysis, and use model checking technique to automate the reasoning process of FTA

  18. Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Yunxiao Fu

    2016-06-01

    Full Text Available Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM based correntropy extracted from the Intrinsic Mode Functions (IMFs, which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD and employ Least Square Support Vector Machine (LSSVM to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.

  19. Quantitative evaluation of the fault tolerance of systems important to the safety of atomic power plants

    International Nuclear Information System (INIS)

    Malkin, S.D.; Sivokon, V.P.; Shmatkova, L.V.

    1989-01-01

    Fault tolerance is the property of a system to preserve its performance upon failures of its components. Thus, in nuclear-reactor technology one has only a qualitative evaluation of fault tolerance - the single-failure criterion, which does not enable one to compare and perform goal-directed design of fault-tolerant systems, and in the field of computer technology there are no generally accepted evaluations of fault tolerance that could be applied effectively to reactor systems. This paper considers alternative evaluations of fault tolerance and a method of comprehensive automated calculation of the reliability and fault tolerance of complex systems. The authors presented quantitative estimates of fault tolerance that develop the single-failure criterion. They have limiting processes that allow simple and graphical standardization. They worked out a method and a program for comprehensive calculation of the reliability and fault tolerance of systems of complex structure that are important to the safety of atomic power plants. The quantitative evaluation of the fault tolerance of these systems exhibits a degree of insensitivity to failures and shows to what extent their reliability is determined by a rigorously defined structure, and to what extent by the probabilistic reliability characteristics of the components. To increase safety, one must increase the fault tolerance of the most important systems of atomic power plants

  20. Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

    DEFF Research Database (Denmark)

    Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede

    2015-01-01

    for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...

  1. Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports.

    Science.gov (United States)

    Qiu, John X; Yoon, Hong-Jun; Fearn, Paul A; Tourassi, Georgia D

    2018-01-01

    Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.

  2. An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

    Science.gov (United States)

    Li, De Z.; Wang, Wilson; Ismail, Fathy

    2017-11-01

    Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

  3. Longwall automation 2

    Energy Technology Data Exchange (ETDEWEB)

    David Hainsworth; David Reid; Con Caris; J.C. Ralston; C.O. Hargrave; Ron McPhee; I.N. Hutchinson; A. Strange; C. Wesner [CSIRO (Australia)

    2008-05-15

    This report covers a nominal two-year extension to the Major Longwall Automation Project (C10100). Production standard implementation of Longwall Automation Steering Committee (LASC) automation systems has been achieved at Beltana and Broadmeadow mines. The systems are now used on a 24/7 basis and have provided production benefits to the mines. The LASC Information System (LIS) has been updated and has been implemented successfully in the IT environment of major coal mining houses. This enables 3D visualisation of the longwall environment and equipment to be accessed on line. A simulator has been specified and a prototype system is now ready for implementation. The Shearer Position Measurement System (SPMS) has been upgraded to a modular commercial production standard hardware solution.A compact hardware solution for visual face monitoring has been developed, an approved enclosure for a thermal infrared camera has been produced and software for providing horizon control through faulted conditions has been delivered. The incorporation of the LASC Cut Model information into OEM horizon control algorithms has been bench and underground tested. A prototype system for shield convergence monitoring has been produced and studies to identify techniques for coal flow optimisation and void monitoring have been carried out. Liaison with equipment manufacturers has been maintained and technology delivery mechanisms for LASC hardware and software have been established.

  4. Fault Severity Estimation of Rotating Machinery Based on Residual Signals

    Directory of Open Access Journals (Sweden)

    Fan Jiang

    2012-01-01

    Full Text Available Fault severity estimation is an important part of a condition-based maintenance system, which can monitor the performance of an operation machine and enhance its level of safety. In this paper, a novel method based on statistical property and residual signals is developed for estimating the fault severity of rotating machinery. The fast Fourier transformation (FFT is applied to extract the so-called multifrequency-band energy (MFBE from the vibration signals of rotating machinery with different fault severity levels in the first stage. Usually these features of the working conditions with different fault sensitivities are different. Therefore a sensitive features-selecting algorithm is defined to construct the feature matrix and calculate the statistic parameter (mean in the second stage. In the last stage, the residual signals computed by the zero space vector are used to estimate the fault severity. Simulation and experimental results reveal that the proposed method based on statistics and residual signals is effective and feasible for estimating the severity of a rotating machine fault.

  5. Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform.

    Science.gov (United States)

    Pang, Bin; Tang, Guiji; Tian, Tian; Zhou, Chong

    2018-04-14

    When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time-time (IHTT) transform, by combining a Hilbert time-time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures.

  6. Fault Diagnosis for Electrical Distribution Systems using Structural Analysis

    DEFF Research Database (Denmark)

    Knüppel, Thyge; Blanke, Mogens; Østergaard, Jacob

    2014-01-01

    redundancies in large sets of equations only from the structure (topology) of the equations. A salient feature is automated generation of redundancy relations. The method is indeed feasible in electrical networks where circuit theory and network topology together formulate the constraints that define...... relations (ARR) are likely to change. The algorithms used for diagnosis may need to change accordingly, and finding efficient methods to ARR generation is essential to employ fault-tolerant methods in the grid. Structural analysis (SA) is based on graph-theoretical results, that offer to find analytic...... a structure graph. This paper shows how three-phase networks are modelled and analysed using structural methods, and it extends earlier results by showing how physical faults can be identified such that adequate remedial actions can be taken. The paper illustrates a feasible modelling technique for structural...

  7. Fault detection and isolation in systems with parametric faults

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1999-01-01

    The problem of fault detection and isolation of parametric faults is considered in this paper. A fault detection problem based on parametric faults are associated with internal parameter variations in the dynamical system. A fault detection and isolation method for parametric faults is formulated...

  8. Comparison of Cenozoic Faulting at the Savannah River Site to Fault Characteristics of the Atlantic Coast Fault Province: Implications for Fault Capability

    International Nuclear Information System (INIS)

    Cumbest, R.J.

    2000-01-01

    This study compares the faulting observed on the Savannah River Site and vicinity with the faults of the Atlantic Coastal Fault Province and concludes that both sets of faults exhibit the same general characteristics and are closely associated. Based on the strength of this association it is concluded that the faults observed on the Savannah River Site and vicinity are in fact part of the Atlantic Coastal Fault Province. Inclusion in this group means that the historical precedent established by decades of previous studies on the seismic hazard potential for the Atlantic Coastal Fault Province is relevant to faulting at the Savannah River Site. That is, since these faults are genetically related the conclusion of ''not capable'' reached in past evaluations applies.In addition, this study establishes a set of criteria by which individual faults may be evaluated in order to assess their inclusion in the Atlantic Coast Fault Province and the related association of the ''not capable'' conclusion

  9. Determination of Low Concentrations of Acetochlor in Water by Automated Solid-Phase Extraction and Gas Chromatography with Mass-Selective Detection

    Science.gov (United States)

    Lindley, C.E.; Stewart, J.T.; Sandstrom, M.W.

    1996-01-01

    A sensitive and reliable gas chromatographic/mass spectrometric (GC/MS) method for determining acetochlor in environmental water samples was developed. The method involves automated extraction of the herbicide from a filtered 1 L water sample through a C18 solid-phase extraction column, elution from the column with hexane-isopropyl alcohol (3 + 1), and concentration of the extract with nitrogen gas. The herbicide is quantitated by capillary/column GC/MS with selected-ion monitoring of 3 characteristic ions. The single-operator method detection limit for reagent water samples is 0.0015 ??g/L. Mean recoveries ranged from about 92 to 115% for 3 water matrixes fortified at 0.05 and 0.5 ??g/L. Average single-operator precision, over the course of 1 week, was better than 5%.

  10. Automated extraction and validation of children's gait parameters with the Kinect.

    Science.gov (United States)

    Motiian, Saeid; Pergami, Paola; Guffey, Keegan; Mancinelli, Corrie A; Doretto, Gianfranco

    2015-12-02

    Gait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas. This is why this work develops a low-cost, portable, and automated approach for in-home gait analysis, based on the Microsoft Kinect. A robust and efficient method for extracting gait parameters is introduced, which copes with the high variability of noisy Kinect skeleton tracking data experienced across the population of young children. This is achieved by temporally segmenting the data with an approach based on coupling a probabilistic matching of stride template models, learned offline, with the estimation of their global and local temporal scaling. A preliminary study conducted on healthy children between 2 and 4 years of age is performed to analyze the accuracy, precision, repeatability, and concurrent validity of the proposed method against the GAITRite when measuring several spatial and temporal children's gait parameters. The method has excellent accuracy and good precision, with segmenting temporal sequences of body joint locations into stride and step cycles. Also, the spatial and temporal gait parameters, estimated automatically, exhibit good concurrent validity with those provided by the GAITRite, as well as very good repeatability. In particular, on a range of nine gait parameters, the relative and absolute agreements were found to be good and excellent, and the overall agreements were found to be good and moderate. This work enables and validates the automated use of the Kinect for children's gait analysis in healthy subjects. In particular, the approach makes a step forward towards developing a low-cost, portable, parent-operated in-home tool for clinicians assisting young children.

  11. Engineering Application Way of Faults Knowledge Discovery Based on Rough Set Theory

    International Nuclear Information System (INIS)

    Zhao Rongzhen; Deng Linfeng; Li Chao

    2011-01-01

    For the knowledge acquisition puzzle of intelligence decision-making technology in mechanical industry, to use the Rough Set Theory (RST) as a kind of tool to solve the puzzle was researched. And the way to realize the knowledge discovery in engineering application is explored. A case extracting out the knowledge rules from a concise data table shows out some important information. It is that the knowledge discovery similar to the mechanical faults diagnosis is an item of complicated system engineering project. In where, first of all-important tasks is to preserve the faults knowledge into a table with data mode. And the data must be derived from the plant site and should also be as concise as possible. On the basis of the faults knowledge data obtained so, the methods and algorithms to process the data and extract the knowledge rules from them by means of RST can be processed only. The conclusion is that the faults knowledge discovery by the way is a process of rising upward. But to develop the advanced faults diagnosis technology by the way is a large-scale knowledge engineering project for long time. Every step in which should be designed seriously according to the tool's demands firstly. This is the basic guarantees to make the knowledge rules obtained have the values of engineering application and the studies have scientific significance. So, a general framework is designed for engineering application to go along the route developing the faults knowledge discovery technology.

  12. Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

    Directory of Open Access Journals (Sweden)

    Lei Luo

    2014-12-01

    Full Text Available Qanats in northern Xinjiang of China provide valuable information for agriculturists and anthropologists who seek fundamental understanding of the distribution of qanat water supply systems with regard to water resource utilization, the development of oasis agriculture, and eventually climate change. Only the tops of qanat shafts (TQSs, indicating the course of the qanats, can be observed from space, and their circular archaeological traces can also be seen in very high resolution imagery in Google Earth. The small size of the TQSs, vast search regions, and degraded features make manually extracting them from remote sensing images difficult and costly. This paper proposes an automated TQS extraction method that adopts mathematical morphological processing methods before an edge detecting module is used in the circular Hough transform approach. The accuracy assessment criteria for the proposed method include: (i extraction percentage (E = 95.9%, branch factor (B = 0 and quality percentage (Q = 95.9% in Site 1; and (ii extraction percentage (E = 83.4%, branch factor (B = 0.058 and quality percentage (Q = 79.5% in Site 2. Compared with the standard circular Hough transform, the quality percentages (Q of our proposed method were improved to 95.9% and 79.5% from 86.3% and 65.8% in test sites 1 and 2, respectively. The results demonstrate that wide-area discovery and mapping can be performed much more effectively based on our proposed method.

  13. Automated concept-level information extraction to reduce the need for custom software and rules development.

    Science.gov (United States)

    D'Avolio, Leonard W; Nguyen, Thien M; Goryachev, Sergey; Fiore, Louis D

    2011-01-01

    Despite at least 40 years of promising empirical performance, very few clinical natural language processing (NLP) or information extraction systems currently contribute to medical science or care. The authors address this gap by reducing the need for custom software and rules development with a graphical user interface-driven, highly generalizable approach to concept-level retrieval. A 'learn by example' approach combines features derived from open-source NLP pipelines with open-source machine learning classifiers to automatically and iteratively evaluate top-performing configurations. The Fourth i2b2/VA Shared Task Challenge's concept extraction task provided the data sets and metrics used to evaluate performance. Top F-measure scores for each of the tasks were medical problems (0.83), treatments (0.82), and tests (0.83). Recall lagged precision in all experiments. Precision was near or above 0.90 in all tasks. Discussion With no customization for the tasks and less than 5 min of end-user time to configure and launch each experiment, the average F-measure was 0.83, one point behind the mean F-measure of the 22 entrants in the competition. Strong precision scores indicate the potential of applying the approach for more specific clinical information extraction tasks. There was not one best configuration, supporting an iterative approach to model creation. Acceptable levels of performance can be achieved using fully automated and generalizable approaches to concept-level information extraction. The described implementation and related documentation is available for download.

  14. Fault trees for decision making in systems analysis

    International Nuclear Information System (INIS)

    Lambert, H.E.

    1975-01-01

    The application of fault tree analysis (FTA) to system safety and reliability is presented within the framework of system safety analysis. The concepts and techniques involved in manual and automated fault tree construction are described and their differences noted. The theory of mathematical reliability pertinent to FTA is presented with emphasis on engineering applications. An outline of the quantitative reliability techniques of the Reactor Safety Study is given. Concepts of probabilistic importance are presented within the fault tree framework and applied to the areas of system design, diagnosis and simulation. The computer code IMPORTANCE ranks basic events and cut sets according to a sensitivity analysis. A useful feature of the IMPORTANCE code is that it can accept relative failure data as input. The output of the IMPORTANCE code can assist an analyst in finding weaknesses in system design and operation, suggest the most optimal course of system upgrade, and determine the optimal location of sensors within a system. A general simulation model of system failure in terms of fault tree logic is described. The model is intended for efficient diagnosis of the causes of system failure in the event of a system breakdown. It can also be used to assist an operator in making decisions under a time constraint regarding the future course of operations. The model is well suited for computer implementation. New results incorporated in the simulation model include an algorithm to generate repair checklists on the basis of fault tree logic and a one-step-ahead optimization procedure that minimizes the expected time to diagnose system failure. (80 figures, 20 tables)

  15. An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

    International Nuclear Information System (INIS)

    Jiang Li; Shi Tielin; Xuan Jianping

    2012-01-01

    Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

  16. Semi-automated extraction of longitudinal subglacial bedforms from digital terrain models - Two new methods

    Science.gov (United States)

    Jorge, Marco G.; Brennand, Tracy A.

    2017-07-01

    Relict drumlin and mega-scale glacial lineation (positive relief, longitudinal subglacial bedforms - LSBs) morphometry has been used as a proxy for paleo ice-sheet dynamics. LSB morphometric inventories have relied on manual mapping, which is slow and subjective and thus potentially difficult to reproduce. Automated methods are faster and reproducible, but previous methods for LSB semi-automated mapping have not been highly successful. Here, two new object-based methods for the semi-automated extraction of LSBs (footprints) from digital terrain models are compared in a test area in the Puget Lowland, Washington, USA. As segmentation procedures to create LSB-candidate objects, the normalized closed contour method relies on the contouring of a normalized local relief model addressing LSBs on slopes, and the landform elements mask method relies on the classification of landform elements derived from the digital terrain model. For identifying which LSB-candidate objects correspond to LSBs, both methods use the same LSB operational definition: a ruleset encapsulating expert knowledge, published morphometric data, and the morphometric range of LSBs in the study area. The normalized closed contour method was separately applied to four different local relief models, two computed in moving windows and two hydrology-based. Overall, the normalized closed contour method outperformed the landform elements mask method. The normalized closed contour method performed on a hydrological relief model from a multiple direction flow routing algorithm performed best. For an assessment of its transferability, the normalized closed contour method was evaluated on a second area, the Chautauqua drumlin field, Pennsylvania and New York, USA where it performed better than in the Puget Lowland. A broad comparison to previous methods suggests that the normalized relief closed contour method may be the most capable method to date, but more development is required.

  17. Comparing Two Different Approaches to the Modeling of the Common Cause Failures in Fault Trees

    International Nuclear Information System (INIS)

    Vukovic, I.; Mikulicic, V.; Vrbanic, I.

    2002-01-01

    The potential for common cause failures in systems that perform critical functions has been recognized as very important contributor to risk associated with operation of nuclear power plants. Consequentially, modeling of common cause failures (CCF) in fault trees has become one among the essential elements in any probabilistic safety assessment (PSA). Detailed and realistic representation of CCF potential in fault tree structure is sometimes very challenging task. This is especially so in the cases where a common cause group involves more than two components. During the last ten years the difficulties associated with this kind of modeling have been overcome to some degree by development of integral PSA tools with high capabilities. Some of them allow for the definition of CCF groups and their automated expanding in the process of Boolean resolution and generation of minimal cutsets. On the other hand, in PSA models developed and run by more traditional tools, CCF-potential had to be modeled in the fault trees explicitly. With explicit CCF modeling, fault trees can grow very large, especially in the cases when they involve CCF groups with 3 or more members, which can become an issue for the management of fault trees and basic events with traditional non-integral PSA models. For these reasons various simplifications had to be made. Speaking in terms of an overall PSA model, there are also some other issues that need to be considered, such as maintainability and accessibility of the model. In this paper a comparison is made between the two approaches to CCF modeling. Analysis is based on a full-scope Level 1 PSA model for internal initiating events that had originally been developed by a traditional PSA tool and later transferred to a new-generation PSA tool with automated CCF modeling capabilities. Related aspects and issues mentioned above are discussed in the paper. (author)

  18. A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters.

    Directory of Open Access Journals (Sweden)

    Daniel H Rapoport

    Full Text Available Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters

  19. Multimodal Teaching Analytics: Automated Extraction of Orchestration Graphs from Wearable Sensor Data.

    Science.gov (United States)

    Prieto, Luis P; Sharma, Kshitij; Kidzinski, Łukasz; Rodríguez-Triana, María Jesús; Dillenbourg, Pierre

    2018-04-01

    The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time), on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye-tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time-independent and time-aware models, achieving median F1 scores of about 0.7-0.8 on leave-one-session-out k-fold cross-validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers.

  20. Design and development of an automated D.C. ground fault detection and location system for Cirus

    International Nuclear Information System (INIS)

    Marik, S.K.; Ramesh, N.; Jain, J.K.; Srivastava, A.P.

    2002-01-01

    Full text: The original design of Cirus safety system provided for automatic detection of ground fault in class I D.C. power supply system and its annunciation followed by delayed reactor trip. Identification of a faulty section was required to be done manually by switching off various sections one at a time thus requiring a lot of shutdown time to identify the faulty section. Since class I power supply is provided for safety control system, quick detection and location of ground faults in this supply is necessary as these faults have potential to bypass safety interlocks and hence the need for a new system for automatic location of a faulty section. Since such systems are not readily available in the market, in-house efforts were made to design and develop a plant-specific system, which has been installed and commissioned

  1. Identification of independent modules in fault trees which contain dependent basic events

    International Nuclear Information System (INIS)

    Sun, H.; Andrews, J.D.

    2004-01-01

    The reliability performance of a system is frequently a function of component failures of which some are independent whilst others are interdependent. It is possible to represent the system failure logic in a fault tree diagram, however only the sections containing independent events can be assessed using the conventional fault tree analysis methodology. The analysis of the dependent sections will require a Markov analysis. Since the efficiency of the Markov analysis largely depends on the size of the established Markov model, the key is to extract from the fault tree the smallest sections which contain dependencies. This paper proposes a method aimed at establishing the smallest Markov model for the dependencies contained within the fault tree

  2. Fault-specific verification (FSV) - An alternative VV ampersand T strategy for high reliability nuclear software systems

    International Nuclear Information System (INIS)

    Miller, L.A.

    1994-01-01

    The author puts forth an argument that digital instrumentation and control systems can be safely applied in the nuclear industry, but it will require changes to the way software for such systems is developed and tested. He argues for a fault-specific verification procedure to be applied to software development. This plan includes enumerating and classifying all software faults at all levels of the product development, over the whole development process. While collecting this data, develop and validate different methods for software verification, validation and testing, and apply them against all the detected faults. Force all of this development toward an automated product for doing this testing. Continue to develop, expand, test, and share these testing methods across a wide array of software products

  3. A fault diagnosis approach for diesel engine valve train based on improved ITD and SDAG-RVM

    International Nuclear Information System (INIS)

    Yu, Liu; Junhong, Zhang; Fengrong, Bi; Jiewei, Lin; Wenpeng, Ma

    2015-01-01

    Targeting the non-stationary characteristics of the vibration signals of a diesel engine valve train, and the limitation of the autoregressive (AR) model, a novel approach based on the improved intrinsic time-scale decomposition (ITD) and relevance vector machine (RVM) is proposed in this paper for the identification of diesel engine valve train faults. The approach mainly consists of three stages: First, prior to the feature extraction, non-uniform B-spline interpolation is introduced to the ITD method for the fitting of baseline signal, then the improved ITD is used to decompose the non-stationary signals into a set of stationary proper rotation components (PRCs). Second, the AR model is established for each PRC, and the first several AR coefficients together with the remnant variance of all PRCs are regarded as the fault feature vectors. Finally, a new separability based directed acyclic graph (SDAG) method is proposed to determine the structure of multi-class RVM, and the fault feature vectors are classified using the SDAG-RVM classifier to recognize the fault of the diesel engine valve train. The experimental results demonstrate that the proposed fault diagnosis approach can effectively extract the fault features and accurately identify the fault patterns. (paper)

  4. Fault-tolerant digital microfluidic biochips compilation and synthesis

    CERN Document Server

    Pop, Paul; Stuart, Elena; Madsen, Jan

    2016-01-01

    This book describes for researchers in the fields of compiler technology, design and test, and electronic design automation the new area of digital microfluidic biochips (DMBs), and thus offers a new application area for their methods.  The authors present a routing-based model of operation execution, along with several associated compilation approaches, which progressively relax the assumption that operations execute inside fixed rectangular modules.  Since operations can experience transient faults during the execution of a bioassay, the authors show how to use both offline (design time) and online (runtime) recovery strategies. The book also presents methods for the synthesis of fault-tolerant application-specific DMB architectures. ·         Presents the current models used for the research on compilation and synthesis techniques of DMBs in a tutorial fashion; ·         Includes a set of “benchmarks”, which are presented in great detail and includes the source code of most of the t...

  5. From fault classification to fault tolerance for multi-agent systems

    CERN Document Server

    Potiron, Katia; Taillibert, Patrick

    2013-01-01

    Faults are a concern for Multi-Agent Systems (MAS) designers, especially if the MAS are built for industrial or military use because there must be some guarantee of dependability. Some fault classification exists for classical systems, and is used to define faults. When dependability is at stake, such fault classification may be used from the beginning of the system's conception to define fault classes and specify which types of faults are expected. Thus, one may want to use fault classification for MAS; however, From Fault Classification to Fault Tolerance for Multi-Agent Systems argues that

  6. Using UAVSAR to Estimate Creep Along the Superstition Hills Fault, Southern California

    Science.gov (United States)

    Donnellan, A.; Parker, J. W.; Pierce, M.; Wang, J.

    2012-12-01

    UAVSAR data were first acquired over the Salton Trough region, just north of the Mexican border in October 2009. Second passes of data were acquired on 12 and 13 April 2010, about one week following the 5 April 2010 M 7.2 El Mayor - Cucapah earthquake. The earthquake resulted in creep on several faults north of the main rupture, including the Yuha, Imperial, and Superstition Hills faults. The UAVSAR platform acquires data about every six meters in swaths about 15 km wide. Tropospheric effects and residual aircraft motion contribute to error in the estimation of surface deformation in the Repeat Pass Interferometry products. The Superstition Hills fault shows clearly in the associated radar interferogram; however, error in the data product makes it difficult to infer deformation from long profiles that cross the fault. Using the QuakeSim InSAR Profile tool we extracted line of site profiles on either side of the fault delineated in the interferogram. We were able to remove much of the correlated error by differencing profiles 250 m on either side of the fault. The result shows right-lateral creep of 1.5±.4 mm along the northern 7 km of the fault in the interferogram. The amount of creep abruptly changes to 8.4±.4 mm of right lateral creep along at least 9 km of the fault covered in the image to the south. The transition occurs within less than 100 m along the fault. We also extracted 2 km long line of site profiles perpendicular to this section of the fault. Averaging these profiles shows a step across the fault of 14.9±.3 mm with greater creep on the order of 20 mm on the northern two profiles and lower creep of about 10 mm on the southern two profiles. Nearby GPS stations P503 and P493 are consistent with this result. They also confirm that the creep event occurred at the time of the El Mayor - Cucapah earthquake. By removing regional deformation resulting from the main rupture we were able to invert for the depth of creep from the surface. Results indicate

  7. Summary: beyond fault trees to fault graphs

    International Nuclear Information System (INIS)

    Alesso, H.P.; Prassinos, P.; Smith, C.F.

    1984-09-01

    Fault Graphs are the natural evolutionary step over a traditional fault-tree model. A Fault Graph is a failure-oriented directed graph with logic connectives that allows cycles. We intentionally construct the Fault Graph to trace the piping and instrumentation drawing (P and ID) of the system, but with logical AND and OR conditions added. Then we evaluate the Fault Graph with computer codes based on graph-theoretic methods. Fault Graph computer codes are based on graph concepts, such as path set (a set of nodes traveled on a path from one node to another) and reachability (the complete set of all possible paths between any two nodes). These codes are used to find the cut-sets (any minimal set of component failures that will fail the system) and to evaluate the system reliability

  8. Fault-related clay authigenesis along the Moab Fault: Implications for calculations of fault rock composition and mechanical and hydrologic fault zone properties

    Science.gov (United States)

    Solum, J.G.; Davatzes, N.C.; Lockner, D.A.

    2010-01-01

    The presence of clays in fault rocks influences both the mechanical and hydrologic properties of clay-bearing faults, and therefore it is critical to understand the origin of clays in fault rocks and their distributions is of great importance for defining fundamental properties of faults in the shallow crust. Field mapping shows that layers of clay gouge and shale smear are common along the Moab Fault, from exposures with throws ranging from 10 to ???1000 m. Elemental analyses of four locations along the Moab Fault show that fault rocks are enriched in clays at R191 and Bartlett Wash, but that this clay enrichment occurred at different times and was associated with different fluids. Fault rocks at Corral and Courthouse Canyons show little difference in elemental composition from adjacent protolith, suggesting that formation of fault rocks at those locations is governed by mechanical processes. Friction tests show that these authigenic clays result in fault zone weakening, and potentially influence the style of failure along the fault (seismogenic vs. aseismic) and potentially influence the amount of fluid loss associated with coseismic dilation. Scanning electron microscopy shows that authigenesis promotes that continuity of slip surfaces, thereby enhancing seal capacity. The occurrence of the authigenesis, and its influence on the sealing properties of faults, highlights the importance of determining the processes that control this phenomenon. ?? 2010 Elsevier Ltd.

  9. Seismic anisotropy in the vicinity of the Alpine fault, New Zealand, estimated by seismic interferometry

    Science.gov (United States)

    Takagi, R.; Okada, T.; Yoshida, K.; Townend, J.; Boese, C. M.; Baratin, L. M.; Chamberlain, C. J.; Savage, M. K.

    2016-12-01

    We estimate shear wave velocity anisotropy in shallow crust near the Alpine fault using seismic interferometry of borehole vertical arrays. We utilized four borehole observations: two sensors are deployed in two boreholes of the Deep Fault Drilling Project in the hanging wall side, and the other two sites are located in the footwall side. Surface sensors deployed just above each borehole are used to make vertical arrays. Crosscorrelating rotated horizontal seismograms observed by the borehole and surface sensors, we extracted polarized shear waves propagating from the bottom to the surface of each borehole. The extracted shear waves show polarization angle dependence of travel time, indicating shear wave anisotropy between the two sensors. In the hanging wall side, the estimated fast shear wave directions are parallel to the Alpine fault. Strong anisotropy of 20% is observed at the site within 100 m from the Alpine fault. The hanging wall consists of mylonite and schist characterized by fault parallel foliation. In addition, an acoustic borehole imaging reveals fractures parallel to the Alpine fault. The fault parallel anisotropy suggest structural anisotropy is predominant in the hanging wall, demonstrating consistency of geological and seismological observations. In the footwall side, on the other hand, the angle between the fast direction and the strike of the Alpine fault is 33-40 degrees. Since the footwall is composed of granitoid that may not have planar structure, stress induced anisotropy is possibly predominant. The direction of maximum horizontal stress (SHmax) estimated by focal mechanisms of regional earthquakes is 55 degrees of the Alpine fault. Possible interpretation of the difference between the fast direction and SHmax direction is depth rotation of stress field near the Alpine fault. Similar depth rotation of stress field is also observed in the SAFOD borehole at the San Andreas fault.

  10. Blind Source Separation and Dynamic Fuzzy Neural Network for Fault Diagnosis in Machines

    International Nuclear Information System (INIS)

    Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli

    2015-01-01

    Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis. (paper)

  11. A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhaowen Chen

    2014-01-01

    Full Text Available Mathematical morphology (MM is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE. Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.

  12. Bearing fault detection utilizing group delay and the Hilbert-Huang transform

    International Nuclear Information System (INIS)

    Jin, Shuai; Lee, Sang-Kwon

    2017-01-01

    Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method

  13. Bearing fault detection utilizing group delay and the Hilbert-Huang transform

    Energy Technology Data Exchange (ETDEWEB)

    Jin, Shuai; Lee, Sang-Kwon [Inha University, Incheon (Korea, Republic of)

    2017-03-15

    Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method.

  14. A review on data-driven fault severity assessment in rolling bearings

    Science.gov (United States)

    Cerrada, Mariela; Sánchez, René-Vinicio; Li, Chuan; Pacheco, Fannia; Cabrera, Diego; Valente de Oliveira, José; Vásquez, Rafael E.

    2018-01-01

    Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.

  15. A Novel Busbar Protection Based on the Average Product of Fault Components

    Directory of Open Access Journals (Sweden)

    Guibin Zou

    2018-05-01

    Full Text Available This paper proposes an original busbar protection method, based on the characteristics of the fault components. The method firstly extracts the fault components of the current and voltage after the occurrence of a fault, secondly it uses a novel phase-mode transformation array to obtain the aerial mode components, and lastly, it obtains the sign of the average product of the aerial mode voltage and current. For a fault on the busbar, the average products that are detected on all of the lines that are linked to the faulted busbar are all positive within a specific duration of the post-fault. However, for a fault at any one of these lines, the average product that has been detected on the faulted line is negative, while those on the non-faulted lines are positive. On the basis of the characteristic difference that is mentioned above, the identification criterion of the fault direction is established. Through comparing the fault directions on all of the lines, the busbar protection can quickly discriminate between an internal fault and an external fault. By utilizing the PSCAD/EMTDC software (4.6.0.0, Manitoba HVDC Research Centre, Winnipeg, MB, Canada, a typical 500 kV busbar model, with one and a half circuit breakers configuration, was constructed. The simulation results show that the proposed busbar protection has a good adjustability, high reliability, and rapid operation speed.

  16. A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

    Directory of Open Access Journals (Sweden)

    Yu Yang

    2008-05-01

    Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

  17. A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement.

    Science.gov (United States)

    Ren, Bangyue; Hao, Yansong; Wang, Huaqing; Song, Liuyang; Tang, Gang; Yuan, Hongfang

    2018-03-28

    Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.

  18. Research on Fault Prediction of Distribution Network Based on Large Data

    Directory of Open Access Journals (Sweden)

    Jinglong Zhou

    2017-01-01

    Full Text Available With the continuous development of information technology and the improvement of distribution automation level. Especially, the amount of on-line monitoring and statistical data is increasing, and large data is used data distribution system, describes the technology to collect, data analysis and data processing of the data distribution system. The artificial neural network mining algorithm and the large data are researched in the fault diagnosis and prediction of the distribution network.

  19. High-throughput analysis of sulfatides in cerebrospinal fluid using automated extraction and UPLC-MS/MS.

    Science.gov (United States)

    Blomqvist, Maria; Borén, Jan; Zetterberg, Henrik; Blennow, Kaj; Månsson, Jan-Eric; Ståhlman, Marcus

    2017-07-01

    Sulfatides (STs) are a group of glycosphingolipids that are highly expressed in brain. Due to their importance for normal brain function and their potential involvement in neurological diseases, development of accurate and sensitive methods for their determination is needed. Here we describe a high-throughput oriented and quantitative method for the determination of STs in cerebrospinal fluid (CSF). The STs were extracted using a fully automated liquid/liquid extraction method and quantified using ultra-performance liquid chromatography coupled to tandem mass spectrometry. With the high sensitivity of the developed method, quantification of 20 ST species from only 100 μl of CSF was performed. Validation of the method showed that the STs were extracted with high recovery (90%) and could be determined with low inter- and intra-day variation. Our method was applied to a patient cohort of subjects with an Alzheimer's disease biomarker profile. Although the total ST levels were unaltered compared with an age-matched control group, we show that the ratio of hydroxylated/nonhydroxylated STs was increased in the patient cohort. In conclusion, we believe that the fast, sensitive, and accurate method described in this study is a powerful new tool for the determination of STs in clinical as well as preclinical settings. Copyright © 2017 by the American Society for Biochemistry and Molecular Biology, Inc.

  20. Fault Classification and Location in Transmission Lines Using Traveling Waves Modal Components and Continuous Wavelet Transform (CWT

    Directory of Open Access Journals (Sweden)

    Farhad Namdari

    2016-06-01

    Full Text Available Accurate fault classification and localization are the bases of protection for transmission systems. This paper presents a new method for classifying and showing location of faults by travelling waves and modal analysis. In the proposed method, characteristics of different faults are investigated using Clarke transformation and initial current traveling wave; then, appropriate indices are introduced to identify different types of faults. Continuous wavelet transform (CWT is employed to extract information of current and voltage travelling waves. Fault location and classification algorithm is being designed according to wavelet transform coefficients relating to current and voltage modal components. The performance of the proposed method is tested for different fault conditions (different fault distance, different fault resistances, and different fault inception angles by using PSCAD and MATLAB with satisfactory results

  1. Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

    Science.gov (United States)

    Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

    2017-06-20

    Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

  2. Prototype Software for Automated Structural Analysis of Systems

    DEFF Research Database (Denmark)

    Jørgensen, A.; Izadi-Zamanabadi, Roozbeh; Kristensen, M.

    2004-01-01

    In this paper we present a prototype software tool that is developed to analyse the structural model of automated systems in order to identify redundant information that is hence utilized for Fault detection and Isolation (FDI) purposes. The dedicated algorithms in this software tool use a tri......-partite graph that represents the structural model of the system. A component-based approach has been used to address issues such as system complexity and reconfigurability possibilities....

  3. Fault tolerant architecture for artificial olfactory system

    International Nuclear Information System (INIS)

    Lotfivand, Nasser; Hamidon, Mohd Nizar; Abdolzadeh, Vida

    2015-01-01

    In this paper, to cover and mask the faults that occur in the sensing unit of an artificial olfactory system, a novel architecture is offered. The proposed architecture is able to tolerate failures in the sensors of the array and the faults that occur are masked. The proposed architecture for extracting the correct results from the output of the sensors can provide the quality of service for generated data from the sensor array. The results of various evaluations and analysis proved that the proposed architecture has acceptable performance in comparison with the classic form of the sensor array in gas identification. According to the results, achieving a high odor discrimination based on the suggested architecture is possible. (paper)

  4. Electromagnetic Transient Response Analysis of DFIG under Cascading Grid Faults Considering Phase Angel Jumps

    DEFF Research Database (Denmark)

    Wang, Yun; Wu, Qiuwei

    2014-01-01

    This paper analysis the electromagnetic transient response characteristics of DFIG under symmetrical and asymmetrical cascading grid fault conditions considering phaseangel jump of grid. On deriving the dynamic equations of the DFIG with considering multiple constraints on balanced and unbalanced...... conditions, phase angel jumps, interval of cascading fault, electromagnetic transient characteristics, the principle of the DFIG response under cascading voltage fault can be extract. The influence of grid angel jump on the transient characteristic of DFIG is analyzed and electromagnetic response...

  5. Development and validation of an automated liquid-liquid extraction GC/MS method for the determination of THC, 11-OH-THC, and free THC-carboxylic acid (THC-COOH) from blood serum.

    Science.gov (United States)

    Purschke, Kirsten; Heinl, Sonja; Lerch, Oliver; Erdmann, Freidoon; Veit, Florian

    2016-06-01

    The analysis of Δ(9)-tetrahydrocannabinol (THC) and its metabolites 11-hydroxy-Δ(9)-tetrahydrocannabinol (11-OH-THC), and 11-nor-9-carboxy-Δ(9)-tetrahydrocannabinol (THC-COOH) from blood serum is a routine task in forensic toxicology laboratories. For examination of consumption habits, the concentration of the phase I metabolite THC-COOH is used. Recommendations for interpretation of analysis values in medical-psychological assessments (regranting of driver's licenses, Germany) include threshold values for the free, unconjugated THC-COOH. Using a fully automated two-step liquid-liquid extraction, THC, 11-OH-THC, and free, unconjugated THC-COOH were extracted from blood serum, silylated with N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), and analyzed by GC/MS. The automation was carried out by an x-y-z sample robot equipped with modules for shaking, centrifugation, and solvent evaporation. This method was based on a previously developed manual sample preparation method. Validation guidelines of the Society of Toxicological and Forensic Chemistry (GTFCh) were fulfilled for both methods, at which the focus of this article is the automated one. Limits of detection and quantification for THC were 0.3 and 0.6 μg/L, for 11-OH-THC were 0.1 and 0.8 μg/L, and for THC-COOH were 0.3 and 1.1 μg/L, when extracting only 0.5 mL of blood serum. Therefore, the required limit of quantification for THC of 1 μg/L in driving under the influence of cannabis cases in Germany (and other countries) can be reached and the method can be employed in that context. Real and external control samples were analyzed, and a round robin test was passed successfully. To date, the method is employed in the Institute of Legal Medicine in Giessen, Germany, in daily routine. Automation helps in avoiding errors during sample preparation and reduces the workload of the laboratory personnel. Due to its flexibility, the analysis system can be employed for other liquid-liquid extractions as

  6. Development of a relatively cheap and simple automated separation system for a routine separation procedure based on extraction chromatography

    International Nuclear Information System (INIS)

    Petro Zoriy; Reinhold Flucht; Mechthild Burow; Peter Ostapczuk; Reinhard Lennartz; Myroslav Zoriy

    2010-01-01

    A robust analytical method has been developed in our laboratory for the separation of radionuclides by means of extraction chromatography using an automated separation system. The proposed method is both cheap and simple and provides the advantageous, rapid and accurate separation of the element of interest. The automated separation system enables a shorter separation time by maintaining a constant flow rate of solution and by avoiding clogging or bubbling in the chromatographic column. The present separation method was tested with two types of samples (water and urine) using UTEVA-, TRU- and Sr-specific resins for the separation of U, Th, Am, Pu and Sr. The total separation time for one radionuclide ranged from 60 to 100 min with the separation yield ranging from 68 to 98% depending on the elements separated. We used ICP-QMS, multi-low-level counter and alpha spectroscopy to measure the corresponding elements. (author)

  7. Pore pressure control on faulting behavior in a block-gouge system

    Science.gov (United States)

    Yang, Z.; Juanes, R.

    2016-12-01

    Pore fluid pressure in a fault zone can be altered by natural processes (e.g., mineral dehydration and thermal pressurization) and industrial operations involving subsurface fluid injection/extraction for the development of energy and water resources. However, the effect of pore pressure change on the stability and slip motion of a preexisting geologic fault remain poorly understood; yet they are critical for the assessment of seismic risk. In this work, we develop a micromechanical model to investigate the effect of pore pressure on faulting behavior. The model couples pore network fluid flow and mechanics of the solid grains. We conceptualize the fault zone as a gouge layer sandwiched between two blocks; the block material is represented by a group of contact-bonded grains and the gouge is composed of unbonded grains. A pore network is extracted from the particulate pack of the block-gouge system with pore body volumes and pore throat conductivities calculated rigorously based on the geometry of the local pore space. Pore fluid exerts pressure force onto the grains, the motion of which is solved using the discrete element method (DEM). The model updates the pore network regularly in response to deformation of the solid matrix. We study the fault stability in the presence of a pressure inhomogeneity (gradient) across the gouge layer, and compare it with the case of homogeneous pore pressure. We consider both normal and thrust faulting scenarios with a focus on the onset of shear failure along the block-gouge interfaces. Numerical simulations show that the slip behavior is characterized by intermittent dynamics, which is evident in the number of slipping contacts at the block-gouge interfaces and the total kinetic energy of the gouge particles. Numerical results also show that, for the case of pressure inhomogeneity, the onset of slip occurs earlier for the side with higher pressure, and that this onset appears to be controlled by the maximum pressure of both sides

  8. Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

    Science.gov (United States)

    Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

    2018-05-01

    This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

  9. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

    Directory of Open Access Journals (Sweden)

    HungLinh Ao

    2014-01-01

    Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

  10. Fault tolerant control for uncertain systems with parametric faults

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2006-01-01

    A fault tolerant control (FTC) architecture based on active fault diagnosis (AFD) and the YJBK (Youla, Jarb, Bongiorno and Kucera)parameterization is applied in this paper. Based on the FTC architecture, fault tolerant control of uncertain systems with slowly varying parametric faults...... is investigated. Conditions are given for closed-loop stability in case of false alarms or missing fault detection/isolation....

  11. LAMPF first-fault identifier for fast transient faults

    International Nuclear Information System (INIS)

    Swanson, A.R.; Hill, R.E.

    1979-01-01

    The LAMPF accelerator is presently producing 800-MeV proton beams at 0.5 mA average current. Machine protection for such a high-intensity accelerator requires a fast shutdown mechanism, which can turn off the beam within a few microseconds of the occurrence of a machine fault. The resulting beam unloading transients cause the rf systems to exceed control loop tolerances and consequently generate multiple fault indications for identification by the control computer. The problem is to isolate the primary fault or cause of beam shutdown while disregarding as many as 50 secondary fault indications that occur as a result of beam shutdown. The LAMPF First-Fault Identifier (FFI) for fast transient faults is operational and has proven capable of first-fault identification. The FFI design utilized features of the Fast Protection System that were previously implemented for beam chopping and rf power conservation. No software changes were required

  12. Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow.

    Science.gov (United States)

    Stockton, David B; Santamaria, Fidel

    2017-10-01

    We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.

  13. Why the 2002 Denali fault rupture propagated onto the Totschunda fault: implications for fault branching and seismic hazards

    Science.gov (United States)

    Schwartz, David P.; Haeussler, Peter J.; Seitz, Gordon G.; Dawson, Timothy E.

    2012-01-01

    The propagation of the rupture of the Mw7.9 Denali fault earthquake from the central Denali fault onto the Totschunda fault has provided a basis for dynamic models of fault branching in which the angle of the regional or local prestress relative to the orientation of the main fault and branch plays a principal role in determining which fault branch is taken. GeoEarthScope LiDAR and paleoseismic data allow us to map the structure of the Denali-Totschunda fault intersection and evaluate controls of fault branching from a geological perspective. LiDAR data reveal the Denali-Totschunda fault intersection is structurally simple with the two faults directly connected. At the branch point, 227.2 km east of the 2002 epicenter, the 2002 rupture diverges southeast to become the Totschunda fault. We use paleoseismic data to propose that differences in the accumulated strain on each fault segment, which express differences in the elapsed time since the most recent event, was one important control of the branching direction. We suggest that data on event history, slip rate, paleo offsets, fault geometry and structure, and connectivity, especially on high slip rate-short recurrence interval faults, can be used to assess the likelihood of branching and its direction. Analysis of the Denali-Totschunda fault intersection has implications for evaluating the potential for a rupture to propagate across other types of fault intersections and for characterizing sources of future large earthquakes.

  14. Automation model of sewerage rehabilitation planning.

    Science.gov (United States)

    Yang, M D; Su, T C

    2006-01-01

    The major steps of sewerage rehabilitation include inspection of sewerage, assessment of structural conditions, computation of structural condition grades, and determination of rehabilitation methods and materials. Conventionally, sewerage rehabilitation planning relies on experts with professional background that is tedious and time-consuming. This paper proposes an automation model of planning optimal sewerage rehabilitation strategies for the sewer system by integrating image process, clustering technology, optimization, and visualization display. Firstly, image processing techniques, such as wavelet transformation and co-occurrence features extraction, were employed to extract various characteristics of structural failures from CCTV inspection images. Secondly, a classification neural network was established to automatically interpret the structural conditions by comparing the extracted features with the typical failures in a databank. Then, to achieve optimal rehabilitation efficiency, a genetic algorithm was used to determine appropriate rehabilitation methods and substitution materials for the pipe sections with a risk of mal-function and even collapse. Finally, the result from the automation model can be visualized in a geographic information system in which essential information of the sewer system and sewerage rehabilitation plans are graphically displayed. For demonstration, the automation model of optimal sewerage rehabilitation planning was applied to a sewer system in east Taichung, Chinese Taiwan.

  15. Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification

    Science.gov (United States)

    Li, Shuanghong; Cao, Hongliang; Yang, Yupu

    2018-02-01

    Fault diagnosis is a key process for the reliability and safety of solid oxide fuel cell (SOFC) systems. However, it is difficult to rapidly and accurately identify faults for complicated SOFC systems, especially when simultaneous faults appear. In this research, a data-driven Multi-Label (ML) pattern identification approach is proposed to address the simultaneous fault diagnosis of SOFC systems. The framework of the simultaneous-fault diagnosis primarily includes two components: feature extraction and ML-SVM classifier. The simultaneous-fault diagnosis approach can be trained to diagnose simultaneous SOFC faults, such as fuel leakage, air leakage in different positions in the SOFC system, by just using simple training data sets consisting only single fault and not demanding simultaneous faults data. The experimental result shows the proposed framework can diagnose the simultaneous SOFC system faults with high accuracy requiring small number training data and low computational burden. In addition, Fault Inference Tree Analysis (FITA) is employed to identify the correlations among possible faults and their corresponding symptoms at the system component level.

  16. The Development of Design Tools for Fault Tolerant Quantum Dot Cellular Automata Based Logic

    Science.gov (United States)

    Armstrong, Curtis D.; Humphreys, William M.

    2003-01-01

    We are developing software to explore the fault tolerance of quantum dot cellular automata gate architectures in the presence of manufacturing variations and device defects. The Topology Optimization Methodology using Applied Statistics (TOMAS) framework extends the capabilities of the A Quantum Interconnected Network Array Simulator (AQUINAS) by adding front-end and back-end software and creating an environment that integrates all of these components. The front-end tools establish all simulation parameters, configure the simulation system, automate the Monte Carlo generation of simulation files, and execute the simulation of these files. The back-end tools perform automated data parsing, statistical analysis and report generation.

  17. Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm

    Directory of Open Access Journals (Sweden)

    Behrooz Attaran

    2015-01-01

    Full Text Available Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estimation values, which are derived from the vibration signals of test data. The results shows that the performance of the proposed optimized system is better than most previous studies, even though it uses only two features. Effectiveness of the above method is illustrated using obtained bearing vibration data.

  18. Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia

    Directory of Open Access Journals (Sweden)

    Yongxue Liu

    2016-03-01

    Full Text Available Wadis, ephemeral dry rivers in arid desert regions that contain water in the rainy season, are often manifested as braided linear channels and are of vital importance for local hydrological environments and regional hydrological management. Conventional methods for effectively delineating wadis from heterogeneous backgrounds are limited for the following reasons: (1 the occurrence of numerous morphological irregularities which disqualify methods based on physical shape; (2 inconspicuous spectral contrast with backgrounds, resulting in frequent false alarms; and (3 the extreme complexity of wadi systems, with numerous tiny tributaries characterized by spectral anisotropy, resulting in a conflict between global and local accuracy. To overcome these difficulties, an automated method for extracting wadis (AMEW from Landsat-8 Operational Land Imagery (OLI was developed in order to take advantage of the complementarity between Water Indices (WIs, which is a technique of mathematically combining different bands to enhance water bodies and suppress backgrounds, and image processing technologies in the morphological field involving multi-scale Gaussian matched filtering and a local adaptive threshold segmentation. Evaluation of the AMEW was carried out in representative areas deliberately selected from Jordan, SW Arabian Peninsula in order to ensure a rigorous assessment. Experimental results indicate that the AMEW achieved considerably higher accuracy than other effective extraction methods in terms of visual inspection and statistical comparison, with an overall accuracy of up to 95.05% for the entire area. In addition, the AMEW (based on the New Water Index (NWI achieved higher accuracy than other methods (the maximum likelihood classifier and the support vector machine classifier used for bulk wadi extraction.

  19. A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings

    Directory of Open Access Journals (Sweden)

    Huaqing Wang

    2016-09-01

    Full Text Available The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples.

  20. Prototype Software for Automated Structural Analysis of Systems

    DEFF Research Database (Denmark)

    Jørgensen, A.; Izadi-Zamanabadi, Roozbeh; Kristensen, M.

    2004-01-01

    In this paper we present a prototype software tool that is developed to analyse the structural model of automated systems in order to identify redundant information that is hence utilized for Fault detection and Isolation (FDI) purposes. The dedicated algorithms in this software tool use a tri......-partite graph that represents the structural model of the system. A component-based approach has been used to address issues such as system complexity and recon¯gurability possibilities....

  1. Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

    Directory of Open Access Journals (Sweden)

    Cheng-Wei Fei

    2014-01-01

    Full Text Available To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE and Support Vector Machine (SVM (PPSE-SVM, for short method was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness were collected under multipoint (multiple channels and multispeed. By using PPSE method, the PPSE values of these data were extracted as fault feature vectors to establish the SVM model of rotor vibration fault diagnosis. From rotor vibration fault diagnosis, the results demonstrate that the proposed method possesses high precision, good learning ability, good generalization ability, and strong fault-tolerant ability (robustness in four aspects of distinguishing fault types, fault severity, fault location, and noise immunity of rotor stochastic vibration. This paper presents a novel method (PPSE-SVM for rotor vibration fault diagnosis and real-time vibration monitoring. The presented effort is promising to improve the fault diagnosis precision of rotating machinery like gas turbine.

  2. Novel neural networks-based fault tolerant control scheme with fault alarm.

    Science.gov (United States)

    Shen, Qikun; Jiang, Bin; Shi, Peng; Lim, Cheng-Chew

    2014-11-01

    In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the fault tolerant controller, is explored. Based on the analysis of the design of normal and passive fault tolerant controllers, by using the implicit function theorem, a novel NN-based active fault-tolerant control scheme with fault alarm is proposed. Comparing with results in the literature, the fault-tolerant control scheme can minimize the time delay between fault occurrence and accommodation that is called the time delay due to fault diagnosis, and reduce the adverse effect on system performance. In addition, the FTC scheme has the advantages of a passive fault-tolerant control scheme as well as the traditional active fault-tolerant control scheme's properties. Furthermore, the fault-tolerant control scheme requires no additional fault detection and isolation model which is necessary in the traditional active fault-tolerant control scheme. Finally, simulation results are presented to demonstrate the efficiency of the developed techniques.

  3. Modern optimization algorithms for fault location estimation in power systems

    Directory of Open Access Journals (Sweden)

    A. Sanad Ahmed

    2017-10-01

    Full Text Available This paper presents a fault location estimation approach in two terminal transmission lines using Teaching Learning Based Optimization (TLBO technique, and Harmony Search (HS technique. Also, previous methods were discussed such as Genetic Algorithm (GA, Artificial Bee Colony (ABC, Artificial neural networks (ANN and Cause & effect (C&E with discussing advantages and disadvantages of all methods. Initial data for proposed techniques are post-fault measured voltages and currents from both ends, along with line parameters as initial inputs as well. This paper deals with several types of faults, L-L-L, L-L-L-G, L-L-G and L-G. Simulation of the model was performed on SIMULINK by extracting initial inputs from SIMULINK to MATLAB, where the objective function specifies the fault location with a very high accuracy, precision and within a very short time. Future works are discussed showing the benefit behind using the Differential Learning TLBO (DLTLBO was discussed as well.

  4. Research on bearing fault diagnosis of large machinery based on mathematical morphology

    Science.gov (United States)

    Wang, Yu

    2018-04-01

    To study the automatic diagnosis of large machinery fault based on support vector machine, combining the four common faults of the large machinery, the support vector machine is used to classify and identify the fault. The extracted feature vectors are entered. The feature vector is trained and identified by multi - classification method. The optimal parameters of the support vector machine are searched by trial and error method and cross validation method. Then, the support vector machine is compared with BP neural network. The results show that the support vector machines are short in time and high in classification accuracy. It is more suitable for the research of fault diagnosis in large machinery. Therefore, it can be concluded that the training speed of support vector machines (SVM) is fast and the performance is good.

  5. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Muhammad Sohaib

    2017-12-01

    Full Text Available Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE-based deep neural networks (DNNs to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs and backpropagation neural networks (BPNNs.

  6. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

    Science.gov (United States)

    Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon

    2017-12-11

    Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

  7. Technology Corner: Automated Data Extraction Using Facebook

    Directory of Open Access Journals (Sweden)

    Nick Flor

    2012-06-01

    Full Text Available Because of Facebook’s popularity, law enforcement agents often use it as a key source of evidence. But like many user digital trails, there can be a large amount of data to extract for analysis. In this paper, we explore the basics of extracting data programmatically from a user’s Facebook via a Web app. A data extraction app requests data using the Facebook Graph API, and Facebook returns a JSON object containing the data. Before an app can access a user’s Facebook data, the user must log into Facebook and give permission. Thus, this approach is limited to situations where users give consent to the data extraction.

  8. BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

    Science.gov (United States)

    Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E; Robbins, Kay A

    2017-01-01

    Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

  9. Semi-automated procedures for shoreline extraction using single RADARSAT-1 SAR image

    Science.gov (United States)

    Al Fugura, A.'kif; Billa, Lawal; Pradhan, Biswajeet

    2011-12-01

    Coastline identification is important for surveying and mapping reasons. Coastline serves as the basic point of reference and is used on nautical charts for navigation purposes. Its delineation has become crucial and more important in the wake of the many recent earthquakes and tsunamis resulting in complete change and redraw of some shorelines. In a tropical country like Malaysia, presence of cloud cover hinders the application of optical remote sensing data. In this study a semi-automated technique and procedures are presented for shoreline delineation from RADARSAT-1 image. A scene of RADARSAT-1 satellite image was processed using enhanced filtering technique to identify and extract the shoreline coast of Kuala Terengganu, Malaysia. RADSARSAT image has many advantages over the optical data because of its ability to penetrate cloud cover and its night sensing capabilities. At first, speckles were removed from the image by using Lee sigma filter which was used to reduce random noise and to enhance the image and discriminate the boundary between land and water. The results showed an accurate and improved extraction and delineation of the entire coastline of Kuala Terrenganu. The study demonstrated the reliability of the image averaging filter in reducing random noise over the sea surface especially near the shoreline. It enhanced land-water boundary differentiation, enabling better delineation of the shoreline. Overall, the developed techniques showed the potential of radar imagery for accurate shoreline mapping and will be useful for monitoring shoreline changes during high and low tides as well as shoreline erosion in a tropical country like Malaysia.

  10. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

    Directory of Open Access Journals (Sweden)

    Chuan Li

    2016-06-01

    Full Text Available Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM. The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

  11. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

    Science.gov (United States)

    Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

    2016-06-17

    Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

  12. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    Science.gov (United States)

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Analysis of halogenated and priority pesticides at different concentration levels. Automated SPE extraction followed by isotope dilution-GC/MS

    Energy Technology Data Exchange (ETDEWEB)

    Planas, C.; Saulo, J.; Rivera, J.; Caixach, J. [Institut Investigacions Quimiques i Ambientals (IIQAB-CSIC), Barcelona (Spain)

    2004-09-15

    In this work, automatic SPE extraction of 16 pesticides and metabolites with the automated Power-Prep trademark system is evaluated at different concentration levels using polymeric (ENV+) and C{sub 18} sorbent phases. The method was optimised by comparing recoveries obtained using different eluting solvents. The optimised procedure was then applied to spiked water samples at concentration levels of 0.1{mu}g/L (quality standard for individual pesticides in drinking water) and 0.02{mu}g/L (close to the detection limit of most pesticides).

  14. Surface faults in the gulf coastal plain between Victoria and Beaumont, Texas

    Science.gov (United States)

    Verbeek, Earl R.

    1979-01-01

    Displacement of the land surface by faulting is widespread in the Houston-Galveston region, an area which has undergone moderate to severe land subsidence associated with fluid withdrawal (principally water, and to a lesser extent, oil and gas). A causative link between subsidence and fluid extraction has been convincingly reported in the published literature. However, the degree to which fluid withdrawal affects fault movement in the Texas Gulf Coast, and the mechanism(s) by which this occurs are as yet unclear. Faults that offset the ground surface are not confined to the large (>6000-km2) subsidence “bowl” centered on Houston, but rather are common and characteristic features of Gulf Coast geology. Current observations and conclusions concerning surface faults mapped in a 35,000-km2 area between Victoria and Beaumont, Texas (which area includes the Houston subsidence bowl) may be summarized as follows: (1) Hundreds of faults cutting the Pleistocene and Holocene sediments exposed in the coastal plain have been mapped. Many faults lie well outside the Houston-Galveston region; of these, more than 10% are active, as shown by such features as displaced, fractured, and patched road surfaces, structural failure of buildings astride faults, and deformed railroad tracks. (2) Complex patterns of surface faults are common above salt domes. Both radial patterns (for example, in High Island, Blue Ridge, Clam Lake, and Clinton domes) and crestal grabens (for example, in the South Houston and Friendswood-Webster domes) have been recognized. Elongate grabens connecting several known and suspected salt domes, such as the fault zone connecting Mykawa, Friendswood-Webster, and Clear Lake domes, suggest fault development above rising salt ridges. (3) Surface faults associated with salt domes tend to be short (10 km), occur singly or in simple grabens, have gently sinuous traces, and tend to lie roughly parallel to the ENE-NE “coastwise” trend common to regional growth

  15. A Novel Arc Fault Detector for Early Detection of Electrical Fires.

    Science.gov (United States)

    Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

    2016-04-09

    Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires.

  16. A Novel Arc Fault Detector for Early Detection of Electrical Fires

    Science.gov (United States)

    Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

    2016-01-01

    Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires. PMID:27070618

  17. Fault tree handbook

    International Nuclear Information System (INIS)

    Haasl, D.F.; Roberts, N.H.; Vesely, W.E.; Goldberg, F.F.

    1981-01-01

    This handbook describes a methodology for reliability analysis of complex systems such as those which comprise the engineered safety features of nuclear power generating stations. After an initial overview of the available system analysis approaches, the handbook focuses on a description of the deductive method known as fault tree analysis. The following aspects of fault tree analysis are covered: basic concepts for fault tree analysis; basic elements of a fault tree; fault tree construction; probability, statistics, and Boolean algebra for the fault tree analyst; qualitative and quantitative fault tree evaluation techniques; and computer codes for fault tree evaluation. Also discussed are several example problems illustrating the basic concepts of fault tree construction and evaluation

  18. Fault isolability conditions for linear systems with additive faults

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob

    2006-01-01

    In this paper, we shall show that an unlimited number of additive single faults can be isolated under mild conditions if a general isolation scheme is applied. Multiple faults are also covered. The approach is algebraic and is based on a set representation of faults, where all faults within a set...

  19. An adaptive and tacholess order analysis method based on enhanced empirical wavelet transform for fault detection of bearings with varying speeds

    Science.gov (United States)

    Hu, Yue; Tu, Xiaotong; Li, Fucai; Li, Hongguang; Meng, Guang

    2017-11-01

    The order tracking method based on time-frequency representation is regarded as an effective tool for fault detection of bearings with varying rotating speeds. In the traditional order tracking methods, a tachometer is required to obtain the instantaneous speed which is hardly satisfied in practice due to the technical and economical limitations. Some tacholess order tracking methods have been developed in recent years. In these methods, the instantaneous frequency ridge extraction is one of the most important parts. However, the current ridge extraction methods are sensitive to noise and may easily get trapped in a local optimum. Due to the presence of noise and other unrelated components of the signal, bearing fault features are difficult to be detected from the envelope spectrum or envelope order spectrum. To overcome the abovementioned drawbacks, an adaptive and tacholess order analysis method is proposed in this paper. In this method, a novel ridge extraction algorithm based on dynamic path optimization is adopted to estimate the instantaneous frequency. This algorithm can overcome the shortcomings of the current ridge extraction algorithms. Meanwhile, the enhanced empirical wavelet transform (EEWT) algorithm is applied to extract the bearing fault features. Both simulated and experimental results demonstrate that the proposed method is robust to noise and effective for bearing fault detection under variable speed conditions.

  20. Fault diagnosis and fault-tolerant control based on adaptive control approach

    CERN Document Server

    Shen, Qikun; Shi, Peng

    2017-01-01

    This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .

  1. A summary of the active fault investigation in the extension sea area of Kikugawa fault and the Nishiyama fault , N-S direction fault in south west Japan

    Science.gov (United States)

    Abe, S.

    2010-12-01

    In this study, we carried out two sets of active fault investigation by the request from Ministry of Education, Culture, Sports, Science and Technology in the sea area of the extension of Kikugawa fault and the Nishiyama fault. We want to clarify the five following matters about both active faults based on those results. (1)Fault continuity of the land and the sea. (2) The length of the active fault. (3) The division of the segment. (4) Activity characteristics. In this investigation, we carried out a digital single channel seismic reflection survey in the whole area of both active faults. In addition, a high-resolution multichannel seismic reflection survey was carried out to recognize the detailed structure of a shallow stratum. Furthermore, the sampling with the vibrocoring to get information of the sedimentation age was carried out. The reflection profile of both active faults was extremely clear. The characteristics of the lateral fault such as flower structure, the dispersion of the active fault were recognized. In addition, from analysis of the age of the stratum, it was recognized that the thickness of the sediment was extremely thin in Holocene epoch on the continental shelf in this sea area. It was confirmed that the Kikugawa fault extended to the offing than the existing results of research by a result of this investigation. In addition, the width of the active fault seems to become wide toward the offing while dispersing. At present, we think that we can divide Kikugawa fault into some segments based on the distribution form of the segment. About the Nishiyama fault, reflection profiles to show the existence of the active fault was acquired in the sea between Ooshima and Kyushu. From this result and topographical existing results of research in Ooshima, it is thought that Nishiyama fault and the Ooshima offing active fault are a series of structure. As for Ooshima offing active fault, the upheaval side changes, and a direction changes too. Therefore, we

  2. TecLines: A MATLAB-Based Toolbox for Tectonic Lineament Analysis from Satellite Images and DEMs, Part 1: Line Segment Detection and Extraction

    Directory of Open Access Journals (Sweden)

    Mehdi Rahnama

    2014-06-01

    Full Text Available Geological structures, such as faults and fractures, appear as image discontinuities or lineaments in remote sensing data. Geologic lineament mapping is a very important issue in geo-engineering, especially for construction site selection, seismic, and risk assessment, mineral exploration and hydrogeological research. Classical methods of lineaments extraction are based on semi-automated (or visual interpretation of optical data and digital elevation models. We developed a freely available Matlab based toolbox TecLines (Tectonic Lineament Analysis for locating and quantifying lineament patterns using satellite data and digital elevation models. TecLines consists of a set of functions including frequency filtering, spatial filtering, tensor voting, Hough transformation, and polynomial fitting. Due to differences in the mathematical background of the edge detection and edge linking procedure as well as the breadth of the methods, we introduce the approach in two-parts. In this first study, we present the steps that lead to edge detection. We introduce the data pre-processing using selected filters in spatial and frequency domains. We then describe the application of the tensor-voting framework to improve position and length accuracies of the detected lineaments. We demonstrate the robustness of the approach in a complex area in the northeast of Afghanistan using a panchromatic QUICKBIRD-2 image with 1-meter resolution. Finally, we compare the results of TecLines with manual lineament extraction, and other lineament extraction algorithms, as well as a published fault map of the study area.

  3. Restructuring of workflows to minimise errors via stochastic model checking: An automated evolutionary approach

    International Nuclear Information System (INIS)

    Herbert, L.T.; Hansen, Z.N.L.

    2016-01-01

    This paper presents a framework for the automated restructuring of stochastic workflows to reduce the impact of faults. The framework allows for the modelling of workflows by means of a formalised subset of the BPMN workflow language. We extend this modelling formalism to describe faults and incorporate an intention preserving stochastic semantics able to model both probabilistic- and non-deterministic behaviour. Stochastic model checking techniques are employed to generate the state-space of a given workflow. Possible improvements obtained by restructuring are measured by employing the framework's capacity for tracking real-valued quantities associated with states and transitions of the workflow. The space of possible restructurings of a workflow is explored by means of an evolutionary algorithm, where the goals for improvement are defined in terms of optimising quantities, typically employed to model resources, associated with a workflow. The approach is fully automated and only the modelling of the production workflows, potential faults and the expression of the goals require manual input. We present the design of a software tool implementing this framework and explore the practical utility of this approach through an industrial case study in which the risk of production failures and their impact are reduced by restructuring the workflow. - Highlights: • We present a framework which allows for the automated restructuring of workflows. • This framework seeks to minimise the impact of errors on the workflow. • We illustrate a scalable software implementation of this framework. • We explore the practical utility of this approach through an industry case. • The impact of errors can be substantially reduced by restructuring the workflow.

  4. Fault finder

    Science.gov (United States)

    Bunch, Richard H.

    1986-01-01

    A fault finder for locating faults along a high voltage electrical transmission line. Real time monitoring of background noise and improved filtering of input signals is used to identify the occurrence of a fault. A fault is detected at both a master and remote unit spaced along the line. A master clock synchronizes operation of a similar clock at the remote unit. Both units include modulator and demodulator circuits for transmission of clock signals and data. All data is received at the master unit for processing to determine an accurate fault distance calculation.

  5. A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

    Directory of Open Access Journals (Sweden)

    Huimin Zhao

    2016-12-01

    Full Text Available Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD, mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

  6. The Sorong Fault Zone, Indonesia: Mapping a Fault Zone Offshore

    Science.gov (United States)

    Melia, S.; Hall, R.

    2017-12-01

    The Sorong Fault Zone is a left-lateral strike-slip fault zone in eastern Indonesia, extending westwards from the Bird's Head peninsula of West Papua towards Sulawesi. It is the result of interactions between the Pacific, Caroline, Philippine Sea, and Australian Plates and much of it is offshore. Previous research on the fault zone has been limited by the low resolution of available data offshore, leading to debates over the extent, location, and timing of movements, and the tectonic evolution of eastern Indonesia. Different studies have shown it north of the Sula Islands, truncated south of Halmahera, continuing to Sulawesi, or splaying into a horsetail fan of smaller faults. Recently acquired high resolution multibeam bathymetry of the seafloor (with a resolution of 15-25 meters), and 2D seismic lines, provide the opportunity to trace the fault offshore. The position of different strands can be identified. On land, SRTM topography shows that in the northern Bird's Head the fault zone is characterised by closely spaced E-W trending faults. NW of the Bird's Head offshore there is a fold and thrust belt which terminates some strands. To the west of the Bird's Head offshore the fault zone diverges into multiple strands trending ENE-WSW. Regions of Riedel shearing are evident west of the Bird's Head, indicating sinistral strike-slip motion. Further west, the ENE-WSW trending faults turn to an E-W trend and there are at least three fault zones situated immediately south of Halmahera, north of the Sula Islands, and between the islands of Sanana and Mangole where the fault system terminates in horsetail strands. South of the Sula islands some former normal faults at the continent-ocean boundary with the North Banda Sea are being reactivated as strike-slip faults. The fault zone does not currently reach Sulawesi. The new fault map differs from previous interpretations concerning the location, age and significance of different parts of the Sorong Fault Zone. Kinematic

  7. Screening for anabolic steroids in urine of forensic cases using fully automated solid phase extraction and LC-MS-MS.

    Science.gov (United States)

    Andersen, David W; Linnet, Kristian

    2014-01-01

    A screening method for 18 frequently measured exogenous anabolic steroids and the testosterone/epitestosterone (T/E) ratio in forensic cases has been developed and validated. The method involves a fully automated sample preparation including enzyme treatment, addition of internal standards and solid phase extraction followed by analysis by liquid chromatography-tandem mass spectrometry (LC-MS-MS) using electrospray ionization with adduct formation for two compounds. Urine samples from 580 forensic cases were analyzed to determine the T/E ratio and occurrence of exogenous anabolic steroids. Extraction recoveries ranged from 77 to 95%, matrix effects from 48 to 78%, overall process efficiencies from 40 to 54% and the lower limit of identification ranged from 2 to 40 ng/mL. In the 580 urine samples analyzed from routine forensic cases, 17 (2.9%) were found positive for one or more anabolic steroids. Only seven different steroids including testosterone were found in the material, suggesting that only a small number of common steroids are likely to occur in a forensic context. The steroids were often in high concentrations (>100 ng/mL), and a combination of steroids and/or other drugs of abuse were seen in the majority of cases. The method presented serves as a fast and automated screening procedure, proving the suitability of LC-MS-MS for analyzing anabolic steroids. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  8. Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

    Directory of Open Access Journals (Sweden)

    Lang Xue

    2017-06-01

    Full Text Available Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

  9. A New Acoustic Emission Sensor Based Gear Fault Detection Approach

    Directory of Open Access Journals (Sweden)

    Junda Zhu

    2013-01-01

    Full Text Available In order to reduce wind energy costs, prognostics and health management (PHM of wind turbine is needed to ensure the reliability and availability of wind turbines. A gearbox is an important component of a wind turbine. Therefore, developing effective gearbox fault detection tools is important to the PHM of wind turbine. In this paper, a new acoustic emission (AE sensor based gear fault detection approach is presented. This approach combines a heterodyne based frequency reduction technique with time synchronous average (TSA and spectrum kurtosis (SK to process AE sensor signals and extract features as condition indictors for gear fault detection. Heterodyne technique commonly used in communication is first employed to preprocess the AE signals before sampling. By heterodyning, the AE signal frequency is down shifted from several hundred kHz to below 50 kHz. This reduced AE signal sampling rate is comparable to that of vibration signals. The presented approach is validated using seeded gear tooth crack fault tests on a notational split torque gearbox. The approach presented in this paper is physics based and the validation results have showed that it could effectively detect the gear faults.

  10. A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

    Science.gov (United States)

    Li, Yongbo; Li, Guoyan; Yang, Yuantao; Liang, Xihui; Xu, Minqiang

    2018-05-01

    The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

  11. A bench-top automated workstation for nucleic acid isolation from clinical sample types.

    Science.gov (United States)

    Thakore, Nitu; Garber, Steve; Bueno, Arial; Qu, Peter; Norville, Ryan; Villanueva, Michael; Chandler, Darrell P; Holmberg, Rebecca; Cooney, Christopher G

    2018-04-18

    Systems that automate extraction of nucleic acid from cells or viruses in complex clinical matrices have tremendous value even in the absence of an integrated downstream detector. We describe our bench-top automated workstation that integrates our previously-reported extraction method - TruTip - with our newly-developed mechanical lysis method. This is the first report of this method for homogenizing viscous and heterogeneous samples and lysing difficult-to-disrupt cells using "MagVor": a rotating magnet that rotates a miniature stir disk amidst glass beads confined inside of a disposable tube. Using this system, we demonstrate automated nucleic acid extraction from methicillin-resistant Staphylococcus aureus (MRSA) in nasopharyngeal aspirate (NPA), influenza A in nasopharyngeal swabs (NPS), human genomic DNA from whole blood, and Mycobacterium tuberculosis in NPA. The automated workstation yields nucleic acid with comparable extraction efficiency to manual protocols, which include commercially-available Qiagen spin column kits, across each of these sample types. This work expands the scope of applications beyond previous reports of TruTip to include difficult-to-disrupt cell types and automates the process, including a method for removal of organics, inside a compact bench-top workstation. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Ecological interface design : supporting fault diagnosis of automated advice in a supervisory air traffic control task

    NARCIS (Netherlands)

    Borst, C.; Bijsterbosch, V.A.; van Paassen, M.M.; Mulder, M.

    2017-01-01

    Future air traffic control will have to rely on more advanced automation to support human controllers in their job of safely handling increased traffic volumes. A prerequisite for the success of such automation is that the data driving it are reliable. Current technology, however, still warrants

  13. Statistical techniques for automating the detection of anomalous performance in rotating machinery

    International Nuclear Information System (INIS)

    Piety, K.R.; Magette, T.E.

    1978-01-01

    Surveillance techniques which extend the sophistication existing in automated systems monitoring in industrial rotating equipment are described. The monitoring system automatically established limiting criteria during an initial learning period of a few days; and subsequently, while monitoring the test rotor during an extended period of normal operation, experienced a false alarm rate of 0.5%. At the same time, the monitoring system successfully detected all fault types that introduced into the test setup. Tests on real equipment are needed to provide final verification of the monitoring techniques. There are areas that would profit from additional investigation in the laboratory environment. A comparison of the relative value of alternate descriptors under given fault conditions would be worthwhile. This should be pursued in conjunction with extending the set of fault types available, e.g., lecaring problems. Other tests should examine the effects of using fewer (more coarse) intervals to define the lumped operational states. finally, techniques to diagnose the most probable fault should be developed by drawing upon the extensive data automatically logged by the monitoring system

  14. Wind turbine fault detection and fault tolerant control

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Johnson, Kathryn

    2013-01-01

    In this updated edition of a previous wind turbine fault detection and fault tolerant control challenge, we present a more sophisticated wind turbine model and updated fault scenarios to enhance the realism of the challenge and therefore the value of the solutions. This paper describes...

  15. Ocean Economy and Fault Diagnosis of Electric Submersible Pump applied in Floating platform

    Directory of Open Access Journals (Sweden)

    Panlong Zhang

    2017-04-01

    Full Text Available Ocean economy plays a crucial role in the strengthening maritime safety industry and in the welfare of human beings. Electric Submersible Pumps (ESP have been widely used in floating platforms on the sea to provide oil for machines. However, the ESP fault may lead to ocean environment pollution, on the other hand, a timely fault diagnosis of ESP can improve the ocean economy. In order to meet the strict regulations of the ocean economy and environmental protection, the fault diagnosis of ESP system has become more and more popular in many countries. The vibration mechanical models of typical faults have been able to successfully diagnose the faults of ESP. And different types of sensors are used to monitor the vibration signal for the signal analysis and fault diagnosis in the ESP system. Meanwhile, physical sensors would increase the fault diagnosis challenge. Nowadays, the method of neural network for the fault diagnosis of ESP has been applied widely, which can diagnose the fault of an electric pump accurately based on the large database. To reduce the number of sensors and to avoid the large database, in this paper, algorithms are designed based on feature extraction to diagnose the fault of the ESP system. Simulation results show that the algorithms can achieve the prospective objectives superbly.

  16. Fault-weighted quantification method of fault detection coverage through fault mode and effect analysis in digital I&C systems

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Jaehyun; Lee, Seung Jun, E-mail: sjlee420@unist.ac.kr; Jung, Wondea

    2017-05-15

    Highlights: • We developed the fault-weighted quantification method of fault detection coverage. • The method has been applied to specific digital reactor protection system. • The unavailability of the module had 20-times difference with the traditional method. • Several experimental tests will be effectively prioritized using this method. - Abstract: The one of the most outstanding features of a digital I&C system is the use of a fault-tolerant technique. With an awareness regarding the importance of thequantification of fault detection coverage of fault-tolerant techniques, several researches related to the fault injection method were developed and employed to quantify a fault detection coverage. In the fault injection method, each injected fault has a different importance because the frequency of realization of every injected fault is different. However, there have been no previous studies addressing the importance and weighting factor of each injected fault. In this work, a new method for allocating the weighting to each injected fault using the failure mode and effect analysis data was proposed. For application, the fault-weighted quantification method has also been applied to specific digital reactor protection system to quantify the fault detection coverage. One of the major findings in an application was that we may estimate the unavailability of the specific module in digital I&C systems about 20-times smaller than real value when we use a traditional method. The other finding was that we can also classify the importance of the experimental case. Therefore, this method is expected to not only suggest an accurate quantification procedure of fault-detection coverage by weighting the injected faults, but to also contribute to an effective fault injection experiment by sorting the importance of the failure categories.

  17. Fault diagnosis

    Science.gov (United States)

    Abbott, Kathy

    1990-01-01

    The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

  18. Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals

    Science.gov (United States)

    Chen, Jian; Randall, Robert Bond; Peeters, Bart

    2016-06-01

    Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.

  19. Systematic review automation technologies

    Science.gov (United States)

    2014-01-01

    Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time. PMID:25005128

  20. Current advances and strategies towards fully automated sample preparation for regulated LC-MS/MS bioanalysis.

    Science.gov (United States)

    Zheng, Naiyu; Jiang, Hao; Zeng, Jianing

    2014-09-01

    Robotic liquid handlers (RLHs) have been widely used in automated sample preparation for liquid chromatography-tandem mass spectrometry (LC-MS/MS) bioanalysis. Automated sample preparation for regulated bioanalysis offers significantly higher assay efficiency, better data quality and potential bioanalytical cost-savings. For RLHs that are used for regulated bioanalysis, there are additional requirements, including 21 CFR Part 11 compliance, software validation, system qualification, calibration verification and proper maintenance. This article reviews recent advances in automated sample preparation for regulated bioanalysis in the last 5 years. Specifically, it covers the following aspects: regulated bioanalysis requirements, recent advances in automation hardware and software development, sample extraction workflow simplification, strategies towards fully automated sample extraction, and best practices in automated sample preparation for regulated bioanalysis.

  1. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Jun He

    2017-07-01

    Full Text Available Artificial intelligence (AI techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN and support vector machine (SVM. The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  2. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

    Science.gov (United States)

    He, Jun; Yang, Shixi; Gan, Chunbiao

    2017-07-04

    Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  3. Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

    Science.gov (United States)

    He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong

    2018-04-01

    This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the fault signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the fault. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit faults classification have demonstrated that the proposed diagnosis scheme has an advantage over other approaches.

  4. Composite Wavelet Filters for Enhanced Automated Target Recognition

    Science.gov (United States)

    Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

    2012-01-01

    Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

  5. Extensible automated dispersive liquid–liquid microextraction

    Energy Technology Data Exchange (ETDEWEB)

    Li, Songqing; Hu, Lu; Chen, Ketao; Gao, Haixiang, E-mail: hxgao@cau.edu.cn

    2015-05-04

    Highlights: • An extensible automated dispersive liquid–liquid microextraction was developed. • A fully automatic SPE workstation with a modified operation program was used. • Ionic liquid-based in situ DLLME was used as model method. • SPE columns packed with nonwoven polypropylene fiber was used for phase separation. • The approach was applied to the determination of benzoylurea insecticides in water. - Abstract: In this study, a convenient and extensible automated ionic liquid-based in situ dispersive liquid–liquid microextraction (automated IL-based in situ DLLME) was developed. 1-Octyl-3-methylimidazolium bis[(trifluoromethane)sulfonyl]imide ([C{sub 8}MIM]NTf{sub 2}) is formed through the reaction between [C{sub 8}MIM]Cl and lithium bis[(trifluoromethane)sulfonyl]imide (LiNTf{sub 2}) to extract the analytes. Using a fully automatic SPE workstation, special SPE columns packed with nonwoven polypropylene (NWPP) fiber, and a modified operation program, the procedures of the IL-based in situ DLLME, including the collection of a water sample, injection of an ion exchange solvent, phase separation of the emulsified solution, elution of the retained extraction phase, and collection of the eluent into vials, can be performed automatically. The developed approach, coupled with high-performance liquid chromatography–diode array detection (HPLC–DAD), was successfully applied to the detection and concentration determination of benzoylurea (BU) insecticides in water samples. Parameters affecting the extraction performance were investigated and optimized. Under the optimized conditions, the proposed method achieved extraction recoveries of 80% to 89% for water samples. The limits of detection (LODs) of the method were in the range of 0.16–0.45 ng mL{sup −1}. The intra-column and inter-column relative standard deviations (RSDs) were <8.6%. Good linearity (r > 0.9986) was obtained over the calibration range from 2 to 500 ng mL{sup −1}. The proposed

  6. Extensible automated dispersive liquid–liquid microextraction

    International Nuclear Information System (INIS)

    Li, Songqing; Hu, Lu; Chen, Ketao; Gao, Haixiang

    2015-01-01

    Highlights: • An extensible automated dispersive liquid–liquid microextraction was developed. • A fully automatic SPE workstation with a modified operation program was used. • Ionic liquid-based in situ DLLME was used as model method. • SPE columns packed with nonwoven polypropylene fiber was used for phase separation. • The approach was applied to the determination of benzoylurea insecticides in water. - Abstract: In this study, a convenient and extensible automated ionic liquid-based in situ dispersive liquid–liquid microextraction (automated IL-based in situ DLLME) was developed. 1-Octyl-3-methylimidazolium bis[(trifluoromethane)sulfonyl]imide ([C 8 MIM]NTf 2 ) is formed through the reaction between [C 8 MIM]Cl and lithium bis[(trifluoromethane)sulfonyl]imide (LiNTf 2 ) to extract the analytes. Using a fully automatic SPE workstation, special SPE columns packed with nonwoven polypropylene (NWPP) fiber, and a modified operation program, the procedures of the IL-based in situ DLLME, including the collection of a water sample, injection of an ion exchange solvent, phase separation of the emulsified solution, elution of the retained extraction phase, and collection of the eluent into vials, can be performed automatically. The developed approach, coupled with high-performance liquid chromatography–diode array detection (HPLC–DAD), was successfully applied to the detection and concentration determination of benzoylurea (BU) insecticides in water samples. Parameters affecting the extraction performance were investigated and optimized. Under the optimized conditions, the proposed method achieved extraction recoveries of 80% to 89% for water samples. The limits of detection (LODs) of the method were in the range of 0.16–0.45 ng mL −1 . The intra-column and inter-column relative standard deviations (RSDs) were <8.6%. Good linearity (r > 0.9986) was obtained over the calibration range from 2 to 500 ng mL −1 . The proposed method opens a new avenue

  7. Automated extraction of DNA from blood and PCR setup using a Tecan Freedom EVO liquid handler for forensic genetic STR typing of reference samples

    DEFF Research Database (Denmark)

    Stangegaard, Michael; Frøslev, Tobias G; Frank-Hansen, Rune

    2011-01-01

    We have implemented and validated automated protocols for DNA extraction and PCR setup using a Tecan Freedom EVO liquid handler mounted with the Te-MagS magnetic separation device (Tecan, Männedorf, Switzerland). The protocols were validated for accredited forensic genetic work according to ISO...... handler leading to the reduction of manual work, and increased quality and throughput....

  8. Incipient fault detection and power system protection for spaceborne systems

    Science.gov (United States)

    Russell, B. Don; Hackler, Irene M.

    1987-01-01

    A program was initiated to study the feasibility of using advanced terrestrial power system protection techniques for spacecraft power systems. It was designed to enhance and automate spacecraft power distribution systems in the areas of safety, reliability and maintenance. The proposed power management/distribution system is described as well as security assessment and control, incipient and low current fault detection, and the proposed spaceborne protection system. It is noted that the intelligent remote power controller permits the implementation of digital relaying algorithms with both adaptive and programmable characteristics.

  9. evelopment of a boiling water reactor fault diagnostic system with a signed directed graph method

    International Nuclear Information System (INIS)

    Chen, M.; Yu, C.C.; Liou, C.T.; Liao, L.Y.

    1990-01-01

    The fault diagnostic system for a nuclear power reactor is expected to be a useful decision support system for the operators during transients and accident conditions. A considerable research effort has been devoted to the development of automated fault diagnostic systems. One major approach, which has been widely used in chemical engineering, is to identify the possible causes of process disturbance using a logic-oriented method called signed directed graph (SDG). A knowledge based system was developed with the rules derived from the SDG representation. The SDG for the Chinshan nuclear power plant, which is a typical boiling water reactor, is established. The personal consultant system is used as the expert system development tool in this paper

  10. Fault zone hydrogeology

    Science.gov (United States)

    Bense, V. F.; Gleeson, T.; Loveless, S. E.; Bour, O.; Scibek, J.

    2013-12-01

    Deformation along faults in the shallow crust (research effort of structural geologists and hydrogeologists. However, we find that these disciplines often use different methods with little interaction between them. In this review, we document the current multi-disciplinary understanding of fault zone hydrogeology. We discuss surface- and subsurface observations from diverse rock types from unlithified and lithified clastic sediments through to carbonate, crystalline, and volcanic rocks. For each rock type, we evaluate geological deformation mechanisms, hydrogeologic observations and conceptual models of fault zone hydrogeology. Outcrop observations indicate that fault zones commonly have a permeability structure suggesting they should act as complex conduit-barrier systems in which along-fault flow is encouraged and across-fault flow is impeded. Hydrogeological observations of fault zones reported in the literature show a broad qualitative agreement with outcrop-based conceptual models of fault zone hydrogeology. Nevertheless, the specific impact of a particular fault permeability structure on fault zone hydrogeology can only be assessed when the hydrogeological context of the fault zone is considered and not from outcrop observations alone. To gain a more integrated, comprehensive understanding of fault zone hydrogeology, we foresee numerous synergistic opportunities and challenges for the discipline of structural geology and hydrogeology to co-evolve and address remaining challenges by co-locating study areas, sharing approaches and fusing data, developing conceptual models from hydrogeologic data, numerical modeling, and training interdisciplinary scientists.

  11. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

    Science.gov (United States)

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-03-09

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

  12. Imaging of Subsurface Faults using Refraction Migration with Fault Flooding

    KAUST Repository

    Metwally, Ahmed Mohsen Hassan

    2017-05-31

    We propose a novel method for imaging shallow faults by migration of transmitted refraction arrivals. The assumption is that there is a significant velocity contrast across the fault boundary that is underlain by a refracting interface. This procedure, denoted as refraction migration with fault flooding, largely overcomes the difficulty in imaging shallow faults with seismic surveys. Numerical results successfully validate this method on three synthetic examples and two field-data sets. The first field-data set is next to the Gulf of Aqaba and the second example is from a seismic profile recorded in Arizona. The faults detected by refraction migration in the Gulf of Aqaba data were in agreement with those indicated in a P-velocity tomogram. However, a new fault is detected at the end of the migration image that is not clearly seen in the traveltime tomogram. This result is similar to that for the Arizona data where the refraction image showed faults consistent with those seen in the P-velocity tomogram, except it also detected an antithetic fault at the end of the line. This fault cannot be clearly seen in the traveltime tomogram due to the limited ray coverage.

  13. Imaging of Subsurface Faults using Refraction Migration with Fault Flooding

    KAUST Repository

    Metwally, Ahmed Mohsen Hassan; Hanafy, Sherif; Guo, Bowen; Kosmicki, Maximillian Sunflower

    2017-01-01

    We propose a novel method for imaging shallow faults by migration of transmitted refraction arrivals. The assumption is that there is a significant velocity contrast across the fault boundary that is underlain by a refracting interface. This procedure, denoted as refraction migration with fault flooding, largely overcomes the difficulty in imaging shallow faults with seismic surveys. Numerical results successfully validate this method on three synthetic examples and two field-data sets. The first field-data set is next to the Gulf of Aqaba and the second example is from a seismic profile recorded in Arizona. The faults detected by refraction migration in the Gulf of Aqaba data were in agreement with those indicated in a P-velocity tomogram. However, a new fault is detected at the end of the migration image that is not clearly seen in the traveltime tomogram. This result is similar to that for the Arizona data where the refraction image showed faults consistent with those seen in the P-velocity tomogram, except it also detected an antithetic fault at the end of the line. This fault cannot be clearly seen in the traveltime tomogram due to the limited ray coverage.

  14. Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

    Science.gov (United States)

    Yuan, Rui; Lv, Yong; Song, Gangbing

    2018-04-16

    Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

  15. Architecture of thrust faults with alongstrike variations in fault-plane dip: anatomy of the Lusatian Fault, Bohemian Massif

    Czech Academy of Sciences Publication Activity Database

    Coubal, Miroslav; Adamovič, Jiří; Málek, Jiří; Prouza, V.

    2014-01-01

    Roč. 59, č. 3 (2014), s. 183-208 ISSN 1802-6222 Institutional support: RVO:67985831 ; RVO:67985891 Keywords : fault architecture * fault plane geometry * drag structures * thrust fault * sandstone * Lusatian Fault Subject RIV: DB - Geology ; Mineralogy Impact factor: 1.405, year: 2014

  16. Fault condition recognition of rolling bearing in bridge crane based on PSO–KPCA

    Directory of Open Access Journals (Sweden)

    He Yan

    2017-01-01

    Full Text Available When the rolling bearing in bridge crane gets out of order and often accompanies with occurrence of nonlinear behaviours, its fault information is weak and it is difficult to extract fault features and to distinguish diverse failure modes. Kernel principal component analysis (KPCA may realize nonlinear mapping to solve nonlinear problems. In the paper the particle swarm optimization (PSO)is applied to optimization of kernel function parameter to reduce its bind set-up. The optimal mathematical model of kernel parameters is constructed by means of thought of fisher discriminate functions .And then it is used to bridge crane rolling bearing simulated faults recognition. The simulation results show that KPCA optimized by PSO can effectively classify fault conditions of rolling bearing. It can be concluded that non-linear mapping capability of KPCA after its function parameter by PSO is greatly improved and the KPCA-PSO is very suit for slight and incipient mechanical fault condition recognition.

  17. Residual signal feature extraction for gearbox planetary stage fault detection

    DEFF Research Database (Denmark)

    Skrimpas, Georgios Alexandros; Ursin, Thomas; Sweeney, Christian Walsted

    2017-01-01

    Faults in planetary gears and related bearings, e.g. planet bearings and planet carrier bearings, pose inherent difficulties on their accurate and consistent detection associated mainly to the low energy in slow rotating stages and the operating complexity of planetary gearboxes. In this work......, identification of the expected spectral signature for proper residual signal calculation and filtering of any frequency component not related to the planetary stage. Two field cases of planet carrier bearing defect and planet wheel spalling are presented and discussed, showing the efficiency of the followed...

  18. Data-Driven Assistance Functions for Industrial Automation Systems

    International Nuclear Information System (INIS)

    Windmann, Stefan; Niggemann, Oliver

    2015-01-01

    The increasing amount of data in industrial automation systems overburdens the user in process control and diagnosis tasks. One possibility to cope with these challenges consists of using smart assistance systems that automatically monitor and optimize processes. This article deals with aspects of data-driven assistance systems such as assistance functions, process models and data acquisition. The paper describes novel approaches for self-diagnosis and self-optimization, and shows how these assistance functions can be integrated in different industrial environments. The considered assistance functions are based on process models that are automatically learned from process data. Fault detection and isolation is based on the comparison of observations of the real system with predictions obtained by application of the process models. The process models are further employed for energy efficiency optimization of industrial processes. Experimental results are presented for fault detection and energy efficiency optimization of a drive system. (paper)

  19. Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

    Science.gov (United States)

    Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke

    2018-06-01

    Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

  20. Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

    Science.gov (United States)

    Qin, Liguo; He, Xiao; Zhou, D. H.

    2017-10-01

    This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.

  1. A Simple Method for Automated Solid Phase Extraction of Water Samples for Immunological Analysis of Small Pollutants.

    Science.gov (United States)

    Heub, Sarah; Tscharner, Noe; Kehl, Florian; Dittrich, Petra S; Follonier, Stéphane; Barbe, Laurent

    2016-01-01

    A new method for solid phase extraction (SPE) of environmental water samples is proposed. The developed prototype is cost-efficient and user friendly, and enables to perform rapid, automated and simple SPE. The pre-concentrated solution is compatible with analysis by immunoassay, with a low organic solvent content. A method is described for the extraction and pre-concentration of natural hormone 17β-estradiol in 100 ml water samples. Reverse phase SPE is performed with octadecyl-silica sorbent and elution is done with 200 µl of methanol 50% v/v. Eluent is diluted by adding di-water to lower the amount of methanol. After preparing manually the SPE column, the overall procedure is performed automatically within 1 hr. At the end of the process, estradiol concentration is measured by using a commercial enzyme-linked immune-sorbent assay (ELISA). 100-fold pre-concentration is achieved and the methanol content in only 10% v/v. Full recoveries of the molecule are achieved with 1 ng/L spiked de-ionized and synthetic sea water samples.

  2. Constraints on the stress state of the San Andreas Fault with analysis based on core and cuttings from San Andreas Fault Observatory at Depth (SAFOD) drilling phases 1 and 2

    Science.gov (United States)

    Tembe, S.; Lockner, D.; Wong, T.-F.

    2009-01-01

    Analysis of field data has led different investigators to conclude that the San Andreas Fault (SAF) has either anomalously low frictional sliding strength (?? 0.6). Arguments for the apparent weakness of the SAF generally hinge on conceptual models involving intrinsically weak gouge or elevated pore pressure within the fault zone. Some models assert that weak gouge and/or high pore pressure exist under static conditions while others consider strength loss or fluid pressure increase due to rapid coseismic fault slip. The present paper is composed of three parts. First, we develop generalized equations, based on and consistent with the Rice (1992) fault zone model to relate stress orientation and magnitude to depth-dependent coefficient of friction and pore pressure. Second, we present temperature-and pressure-dependent friction measurements from wet illite-rich fault gouge extracted from San Andreas Fault Observatory at Depth (SAFOD) phase 1 core samples and from weak minerals associated with the San Andreas Fault. Third, we reevaluate the state of stress on the San Andreas Fault in light of new constraints imposed by SAFOD borehole data. Pure talc (?????0.1) had the lowest strength considered and was sufficiently weak to satisfy weak fault heat flow and stress orientation constraints with hydrostatic pore pressure. Other fault gouges showed a systematic increase in strength with increasing temperature and pressure. In this case, heat flow and stress orientation constraints would require elevated pore pressure and, in some cases, fault zone pore pressure in excess of vertical stress. Copyright 2009 by the American Geophysical Union.

  3. Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

    Directory of Open Access Journals (Sweden)

    Xu Feng

    2017-03-01

    Full Text Available In this paper, a fault-tolerant control (FTC scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC as well as set-based fault detection and isolation (FDI. In the class of MPC techniques, tubebased MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD is passive by using invariant sets, while fault isolation (FI is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs.

  4. Application-Oriented Optimal Shift Schedule Extraction for a Dual-Motor Electric Bus with Automated Manual Transmission

    Directory of Open Access Journals (Sweden)

    Mingjie Zhao

    2018-02-01

    Full Text Available The conventional battery electric buses (BEBs have limited potential to optimize the energy consumption and reach a better dynamic performance. A practical dual-motor equipped with 4-speed Automated Manual Transmission (AMT propulsion system is proposed, which can eliminate the traction interruption in conventional AMT. A discrete model of the dual-motor-AMT electric bus (DMAEB is built and used to optimize the gear shift schedule. Dynamic programming (DP algorithm is applied to find the optimal results where the efficiency and shift time of each gear are considered to handle the application problem of global optimization. A rational penalty factor and a proper shift time delay based on bench test results are set to reduce the shift frequency by 82.5% in Chinese-World Transient Vehicle Cycle (C-WTVC. Two perspectives of applicable shift rule extraction methods, i.e., the classification method based on optimal operating points and clustering method based on optimal shifting points, are explored and compared. Eventually, the hardware-in-the-loop (HIL simulation results demonstrate that the proposed structure and extracted shift schedule can realize a significant improvement in reducing energy loss by 20.13% compared to traditional empirical strategies.

  5. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2016-01-01

    Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD technique based local characteristic-scale decomposition (LCD was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN and mean impact value (MIV, ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

  6. Determining on-fault magnitude distributions for a connected, multi-fault system

    Science.gov (United States)

    Geist, E. L.; Parsons, T.

    2017-12-01

    A new method is developed to determine on-fault magnitude distributions within a complex and connected multi-fault system. A binary integer programming (BIP) method is used to distribute earthquakes from a 10 kyr synthetic regional catalog, with a minimum magnitude threshold of 6.0 and Gutenberg-Richter (G-R) parameters (a- and b-values) estimated from historical data. Each earthquake in the synthetic catalog can occur on any fault and at any location. In the multi-fault system, earthquake ruptures are allowed to branch or jump from one fault to another. The objective is to minimize the slip-rate misfit relative to target slip rates for each of the faults in the system. Maximum and minimum slip-rate estimates around the target slip rate are used as explicit constraints. An implicit constraint is that an earthquake can only be located on a fault (or series of connected faults) if it is long enough to contain that earthquake. The method is demonstrated in the San Francisco Bay area, using UCERF3 faults and slip-rates. We also invoke the same assumptions regarding background seismicity, coupling, and fault connectivity as in UCERF3. Using the preferred regional G-R a-value, which may be suppressed by the 1906 earthquake, the BIP problem is deemed infeasible when faults are not connected. Using connected faults, however, a solution is found in which there is a surprising diversity of magnitude distributions among faults. In particular, the optimal magnitude distribution for earthquakes that participate along the Peninsula section of the San Andreas fault indicates a deficit of magnitudes in the M6.0- 7.0 range. For the Rodgers Creek-Hayward fault combination, there is a deficit in the M6.0- 6.6 range. Rather than solving this as an optimization problem, we can set the objective function to zero and solve this as a constraint problem. Among the solutions to the constraint problem is one that admits many more earthquakes in the deficit magnitude ranges for both faults

  7. Fracture Modes and Identification of Fault Zones in Wenchuan Earthquake Fault Scientific Drilling Boreholes

    Science.gov (United States)

    Deng, C.; Pan, H.; Zhao, P.; Qin, R.; Peng, L.

    2017-12-01

    After suffering from the disaster of Wenchuan earthquake on May 12th, 2008, scientists are eager to figure out the structure of formation, the geodynamic processes of faults and the mechanism of earthquake in Wenchuan by drilling five holes into the Yingxiu-Beichuan fault zone and Anxian-Guanxian fault zone. Fractures identification and in-situ stress determination can provide abundant information for formation evaluation and earthquake study. This study describe all the fracture modes in the five boreholes on the basis of cores and image logs, and summarize the response characteristics of fractures in conventional logs. The results indicate that the WFSD boreholes encounter enormous fractures, including natural fractures and induced fractures, and high dip-angle conductive fractures are the most common fractures. The maximum horizontal stress trends along the borehole are deduced as NWW-SEE according to orientations of borehole breakouts and drilling-induced fractures, which is nearly parallel to the strikes of the younger natural fracture sets. Minor positive deviations of AC (acoustic log) and negative deviation of DEN (density log) demonstrate their responses to fracture, followed by CNL (neutron log), resistivity logs and GR (gamma ray log) at different extent of intensity. Besides, considering the fact that the reliable methods for identifying fracture zone, like seismic, core recovery and image logs, can often be hampered by their high cost and limited application, this study propose a method by using conventional logs, which are low-cost and available in even old wells. We employ wavelet decomposition to extract the high frequency information of conventional logs and reconstruction a new log in special format of enhance fracture responses and eliminate nonfracture influence. Results reveal that the new log shows obvious deviations in fault zones, which confirm the potential of conventional logs in fracture zone identification.

  8. An international crowdsourcing study into people's statements on fully automated driving

    NARCIS (Netherlands)

    Bazilinskyy, P.; Kyriakidis, M.; de Winter, J.C.F.; Ahram, Tareq; Karwowski, Waldemar; Schmorrow, Dylan

    2015-01-01

    Fully automated driving can potentially provide enormous benefits to society. However, it has been unclear whether people will appreciate such far-reaching technology. This study investigated anonymous textual comments regarding fully automated driving, based on data extracted from three online

  9. Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

    Science.gov (United States)

    Jiao, Jing; Yue, Jianhai; Pei, Di

    2017-10-01

    Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

  10. Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method

    Science.gov (United States)

    Jiang, Zhinong; Wang, Zijia; Zhang, Jinjie

    2017-01-01

    Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable. PMID:29244722

  11. Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method.

    Science.gov (United States)

    Jiang, Zhinong; Mao, Zhiwei; Wang, Zijia; Zhang, Jinjie

    2017-12-15

    Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable.

  12. Automated extraction of radiation dose information from CT dose report images.

    Science.gov (United States)

    Li, Xinhua; Zhang, Da; Liu, Bob

    2011-06-01

    The purpose of this article is to describe the development of an automated tool for retrieving texts from CT dose report images. Optical character recognition was adopted to perform text recognitions of CT dose report images. The developed tool is able to automate the process of analyzing multiple CT examinations, including text recognition, parsing, error correction, and exporting data to spreadsheets. The results were precise for total dose-length product (DLP) and were about 95% accurate for CT dose index and DLP of scanned series.

  13. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data.

    Science.gov (United States)

    Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

    2018-02-05

    The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis.

  14. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

    Science.gov (United States)

    Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong

    2018-01-01

    The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. PMID:29401730

  15. Fault-Tolerant Approach for Modular Multilevel Converters under Submodule Faults

    DEFF Research Database (Denmark)

    Deng, Fujin; Tian, Yanjun; Zhu, Rongwu

    2016-01-01

    The modular multilevel converter (MMC) is attractive for medium- or high-power applications because of the advantages of its high modularity, availability, and high power quality. The fault-tolerant operation is one of the important issues for the MMC. This paper proposed a fault-tolerant approach...... for the MMC under submodule (SM) faults. The characteristic of the MMC with arms containing different number of healthy SMs under faults is analyzed. Based on the characteristic, the proposed approach can effectively keep the MMC operation as normal under SM faults. It can effectively improve the MMC...

  16. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

    Science.gov (United States)

    Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart; Thomas, Sobi; Andreasen, Søren Juhl; Kær, Søren Knudsen

    2017-08-01

    This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode- and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%.

  17. Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

    Science.gov (United States)

    Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

    2017-06-01

    In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

  18. A novel wavelet-based feature extraction from common mode currents for fault location in a residential DC microgrid

    DEFF Research Database (Denmark)

    Beheshtaein, Siavash; Yu, Junyang; Cuzner, Rob

    2017-01-01

    approaches have been developed that enable construction of scalable microgrids based on PV and battery storage. However, as these systems proliferate, it will be necessary to develop safe and reliable methods for fault protection. Ground faults are of specific concern because, in many cases, cables...... are buried underground. At the same time, microgrids include current monitoring and processing capability wherever an energy resource interfaces to the microgrid through a power electronic converter. This paper discusses methods for identifying ground fault behavior within standard DC microgrid structures...

  19. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    Directory of Open Access Journals (Sweden)

    Xianfeng Yuan

    2015-01-01

    presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

  20. An investigation of the relations between fault tree analysis and cause consequence analysis with special reference to a photometry and conductimetry system

    International Nuclear Information System (INIS)

    Weber, G.

    1980-02-01

    For an automated photometry and conductimetry system, the relations between cause consequence analysis and fault tree analysis have been investigated. It has been shown how failure combinations of a cause consequence diagram and minimal cuts of a fault tree can be identified. This procedure allows a mutual control of fault tree analysis and cause consequence analysis. From a representation of all failure combinations of the system by means of a matrix we obtain a control of our analysis. Moreover, heuristic rules improving and simplifying the cause consequence analysis can be found. Necessary assumptions for the validity of these rules are discussed. Methodologically, the relation of a fault tree and a cause consequence diagram can be represented (under certain conditions) as a relation of a Boolean function and a binary decision tree. (orig.) 891 HP/orig. 892 MKO [de

  1. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Lingli Jiang

    2011-01-01

    Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

  2. A phase angle based diagnostic scheme to planetary gear faults diagnostics under non-stationary operational conditions

    Science.gov (United States)

    Feng, Ke; Wang, Kesheng; Ni, Qing; Zuo, Ming J.; Wei, Dongdong

    2017-11-01

    Planetary gearbox is a critical component for rotating machinery. It is widely used in wind turbines, aerospace and transmission systems in heavy industry. Thus, it is important to monitor planetary gearboxes, especially for fault diagnostics, during its operational conditions. However, in practice, operational conditions of planetary gearbox are often characterized by variations of rotational speeds and loads, which may bring difficulties for fault diagnosis through the measured vibrations. In this paper, phase angle data extracted from measured planetary gearbox vibrations is used for fault detection under non-stationary operational conditions. Together with sample entropy, fault diagnosis on planetary gearbox is implemented. The proposed scheme is explained and demonstrated in both simulation and experimental studies. The scheme proves to be effective and features advantages on fault diagnosis of planetary gearboxes under non-stationary operational conditions.

  3. Rectifier Fault Diagnosis and Fault Tolerance of a Doubly Fed Brushless Starter Generator

    Directory of Open Access Journals (Sweden)

    Liwei Shi

    2015-01-01

    Full Text Available This paper presents a rectifier fault diagnosis method with wavelet packet analysis to improve the fault tolerant four-phase doubly fed brushless starter generator (DFBLSG system reliability. The system components and fault tolerant principle of the high reliable DFBLSG are given. And the common fault of the rectifier is analyzed. The process of wavelet packet transforms fault detection/identification algorithm is introduced in detail. The fault tolerant performance and output voltage experiments were done to gather the energy characteristics with a voltage sensor. The signal is analyzed with 5-layer wavelet packets, and the energy eigenvalue of each frequency band is obtained. Meanwhile, the energy-eigenvalue tolerance was introduced to improve the diagnostic accuracy. With the wavelet packet fault diagnosis, the fault tolerant four-phase DFBLSG can detect the usual open-circuit fault and operate in the fault tolerant mode if there is a fault. The results indicate that the fault analysis techniques in this paper are accurate and effective.

  4. Fully automated synthesis of ¹¹C-acetate as tumor PET tracer by simple modified solid-phase extraction purification.

    Science.gov (United States)

    Tang, Xiaolan; Tang, Ganghua; Nie, Dahong

    2013-12-01

    Automated synthesis of (11)C-acetate ((11)C-AC) as the most commonly used radioactive fatty acid tracer is performed by a simple, rapid, and modified solid-phase extraction (SPE) purification. Automated synthesis of (11)C-AC was implemented by carboxylation reaction of MeMgBr on a polyethylene Teflon loop ring with (11)C-CO2, followed by acidic hydrolysis with acid and SCX cartridge, and purification on SCX, AG11A8 and C18 SPE cartridges using a commercially available (11)C-tracer synthesizer. Quality control test and animals positron emission tomography (PET) imaging were also carried out. A high and reproducible decay-uncorrected radiochemical yield of (41.0 ± 4.6)% (n=10) was obtained from (11)C-CO2 within the whole synthesis time about 8 min. The radiochemical purity of (11)C-AC was over 95% by high-performance liquid chromatography (HPLC) analysis. Quality control test and PET imaging showed that (11)C-AC injection produced by the simple SPE procedure was safe and efficient, and was in agreement with the current Chinese radiopharmaceutical quality control guidelines. The novel, simple, and rapid method is readily adapted to the fully automated synthesis of (11)C-AC on several existing commercial synthesis module. The method can be used routinely to produce (11)C-AC for preclinical and clinical studies with PET imaging. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network.

    Science.gov (United States)

    Guo, Sheng; Yang, Tao; Gao, Wei; Zhang, Chen

    2018-05-04

    Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.

  6. Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

    Science.gov (United States)

    Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun

    2017-11-01

    The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.

  7. The Design of Fault Tolerant Quantum Dot Cellular Automata Based Logic

    Science.gov (United States)

    Armstrong, C. Duane; Humphreys, William M.; Fijany, Amir

    2002-01-01

    As transistor geometries are reduced, quantum effects begin to dominate device performance. At some point, transistors cease to have the properties that make them useful computational components. New computing elements must be developed in order to keep pace with Moore s Law. Quantum dot cellular automata (QCA) represent an alternative paradigm to transistor-based logic. QCA architectures that are robust to manufacturing tolerances and defects must be developed. We are developing software that allows the exploration of fault tolerant QCA gate architectures by automating the specification, simulation, analysis and documentation processes.

  8. Sensors and Automated Analyzers for Radionuclides

    International Nuclear Information System (INIS)

    Grate, Jay W.; Egorov, Oleg B.

    2003-01-01

    The production of nuclear weapons materials has generated large quantities of nuclear waste and significant environmental contamination. We have developed new, rapid, automated methods for determination of radionuclides using sequential injection methodologies to automate extraction chromatographic separations, with on-line flow-through scintillation counting for real time detection. This work has progressed in two main areas: radionuclide sensors for water monitoring and automated radiochemical analyzers for monitoring nuclear waste processing operations. Radionuclide sensors have been developed that collect and concentrate radionuclides in preconcentrating minicolumns with dual functionality: chemical selectivity for radionuclide capture and scintillation for signal output. These sensors can detect pertechnetate to below regulatory levels and have been engineered into a prototype for field testing. A fully automated process monitor has been developed for total technetium in nuclear waste streams. This instrument performs sample acidification, speciation adjustment, separation and detection in fifteen minutes or less

  9. Fault displacement along the Naruto-South fault, the Median Tectonic Line active fault system in the eastern part of Shikoku, southwestern Japan

    OpenAIRE

    高田, 圭太; 中田, 高; 後藤, 秀昭; 岡田, 篤正; 原口, 強; 松木, 宏彰

    1998-01-01

    The Naruto-South fault is situated of about 1000m south of the Naruto fault, the Median Tectonic Line active fault system in the eastern part of Shikoku. We investigated fault topography and subsurface geology of this fault by interpretation of large scale aerial photographs, collecting borehole data and Geo-Slicer survey. The results obtained are as follows; 1) The Naruto-South fault runs on the Yoshino River deltaic plain at least 2.5 km long with fault scarplet. the Naruto-South fault is o...

  10. Fault Detection And Diagnosis For Air Conditioners And Heat Pumps Based On Virtual Sensors

    OpenAIRE

    Kim, Woohyun

    2013-01-01

    The primary goal of this research is to develop and demonstrate an integrated, on-line performance monitoring and diagnostic system with low cost sensors for air conditioning and heat pump equipment. Automated fault detection and diagnostics (FDD) has the potential for improving energy efficiency along with reducing service costs and comfort complaints. To achieve this goal, virtual sensors with low cost measurements and simple models were developed to estimate quantities that would be expens...

  11. Robust Fault Diagnosis Design for Linear Multiagent Systems with Incipient Faults

    Directory of Open Access Journals (Sweden)

    Jingping Xia

    2015-01-01

    Full Text Available The design of a robust fault estimation observer is studied for linear multiagent systems subject to incipient faults. By considering the fact that incipient faults are in low-frequency domain, the fault estimation of such faults is proposed for discrete-time multiagent systems based on finite-frequency technique. Moreover, using the decomposition design, an equivalent conclusion is given. Simulation results of a numerical example are presented to demonstrate the effectiveness of the proposed techniques.

  12. The use of automatic programming techniques for fault tolerant computing systems

    Science.gov (United States)

    Wild, C.

    1985-01-01

    It is conjectured that the production of software for ultra-reliable computing systems such as required by Space Station, aircraft, nuclear power plants and the like will require a high degree of automation as well as fault tolerance. In this paper, the relationship between automatic programming techniques and fault tolerant computing systems is explored. Initial efforts in the automatic synthesis of code from assertions to be used for error detection as well as the automatic generation of assertions and test cases from abstract data type specifications is outlined. Speculation on the ability to generate truly diverse designs capable of recovery from errors by exploring alternate paths in the program synthesis tree is discussed. Some initial thoughts on the use of knowledge based systems for the global detection of abnormal behavior using expectations and the goal-directed reconfiguration of resources to meet critical mission objectives are given. One of the sources of information for these systems would be the knowledge captured during the automatic programming process.

  13. Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

    Energy Technology Data Exchange (ETDEWEB)

    Du, Zhimin; Jin, Xinqiao; Yang, Yunyu [School of Mechanical Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai (China)

    2009-09-15

    Wavelet neural network, the integration of wavelet analysis and neural network, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation. Wavelet analysis is used to process the original data collected from the building automation first. With three-level wavelet decomposition, the series of characteristic information representing various operation conditions of the system are obtained. In addition, neural network is developed to diagnose the source of the fault. To improve the diagnosis efficiency, three data groups based on several physical models or balances are classified and constructed. Using the data decomposed by three-level wavelet, the neural network can be well trained and series of convergent networks are obtained. Finally, the new measurements to diagnose are similarly processed by wavelet. And the well-trained convergent neural networks are used to identify the operation condition and isolate the source of the fault. (author)

  14. Comparison of QIAsymphony automated and QIAamp manual DNA extraction systems for measuring Epstein-Barr virus DNA load in whole blood using real-time PCR.

    Science.gov (United States)

    Laus, Stella; Kingsley, Lawrence A; Green, Michael; Wadowsky, Robert M

    2011-11-01

    Automated and manual extraction systems have been used with real-time PCR for quantification of Epstein-Barr virus [human herpesvirus 4 (HHV-4)] DNA in whole blood, but few studies have evaluated relative performances. In the present study, the automated QIAsymphony and manual QIAamp extraction systems (Qiagen, Valencia, CA) were assessed using paired aliquots derived from clinical whole-blood specimens and an in-house, real-time PCR assay. The detection limits using the QIAsymphony and QIAamp systems were similar (270 and 560 copies/mL, respectively). For samples estimated as having ≥10,000 copies/mL, the intrarun and interrun variations were significantly lower using QIAsymphony (10.0% and 6.8%, respectively), compared with QIAamp (18.6% and 15.2%, respectively); for samples having ≤1000 copies/mL, the two variations ranged from 27.9% to 43.9% and were not significantly different between the two systems. Among 68 paired clinical samples, 48 pairs yielded viral loads ≥1000 copies/mL under both extraction systems. Although the logarithmic linear correlation from these positive samples was high (r(2) = 0.957), the values obtained using QIAsymphony were on average 0.2 log copies/mL higher than those obtained using QIAamp. Thus, the QIAsymphony and QIAamp systems provide similar EBV DNA load values in whole blood. Copyright © 2011 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

  15. Modification of Duval Triangle for Diagnostic Transformer Fault through a Procedure of Dissolved Gases Analysis

    Directory of Open Access Journals (Sweden)

    Sobhy Serry Dessouky

    2016-08-01

        The evaluation is carried out on DGA data obtained from three different groups of transformers. A Matlab program was developed to automate the evaluation of  Duval Triangle graph to numerical modification, Also the fault gases can be generated due to oil decomposing effected by transformer over excitation which increasing thetransformer exciting current lead to rising the temperature inside transformer core beside the other causes.

  16. Stafford fault system: 120 million year fault movement history of northern Virginia

    Science.gov (United States)

    Powars, David S.; Catchings, Rufus D.; Horton, J. Wright; Schindler, J. Stephen; Pavich, Milan J.

    2015-01-01

    The Stafford fault system, located in the mid-Atlantic coastal plain of the eastern United States, provides the most complete record of fault movement during the past ~120 m.y. across the Virginia, Washington, District of Columbia (D.C.), and Maryland region, including displacement of Pleistocene terrace gravels. The Stafford fault system is close to and aligned with the Piedmont Spotsylvania and Long Branch fault zones. The dominant southwest-northeast trend of strong shaking from the 23 August 2011, moment magnitude Mw 5.8 Mineral, Virginia, earthquake is consistent with the connectivity of these faults, as seismic energy appears to have traveled along the documented and proposed extensions of the Stafford fault system into the Washington, D.C., area. Some other faults documented in the nearby coastal plain are clearly rooted in crystalline basement faults, especially along terrane boundaries. These coastal plain faults are commonly assumed to have undergone relatively uniform movement through time, with average slip rates from 0.3 to 1.5 m/m.y. However, there were higher rates during the Paleocene–early Eocene and the Pliocene (4.4–27.4 m/m.y), suggesting that slip occurred primarily during large earthquakes. Further investigation of the Stafford fault system is needed to understand potential earthquake hazards for the Virginia, Maryland, and Washington, D.C., area. The combined Stafford fault system and aligned Piedmont faults are ~180 km long, so if the combined fault system ruptured in a single event, it would result in a significantly larger magnitude earthquake than the Mineral earthquake. Many structures most strongly affected during the Mineral earthquake are along or near the Stafford fault system and its proposed northeastward extension.

  17. Feature selection and classification of mechanical fault of an induction motor using random forest classifier

    OpenAIRE

    Patel, Raj Kumar; Giri, V.K.

    2016-01-01

    Fault detection and diagnosis is the most important technology in condition-based maintenance (CBM) system for rotating machinery. This paper experimentally explores the development of a random forest (RF) classifier, a recently emerged machine learning technique, for multi-class mechanical fault diagnosis in bearing of an induction motor. Firstly, the vibration signals are collected from the bearing using accelerometer sensor. Parameters from the vibration signal are extracted in the form of...

  18. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.

    Science.gov (United States)

    Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao

    2017-05-25

    Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

  19. Optimal fault signal estimation

    NARCIS (Netherlands)

    Stoorvogel, Antonie Arij; Niemann, H.H.; Saberi, A.; Sannuti, P.

    2002-01-01

    We consider here both fault identification and fault signal estimation. Regarding fault identification, we seek either exact or almost fault identification. On the other hand, regarding fault signal estimation, we seek either $H_2$ optimal, $H_2$ suboptimal or Hinfinity suboptimal estimation. By

  20. Clay mineral formation and fabric development in the DFDP-1B borehole, central Alpine Fault, New Zealand

    International Nuclear Information System (INIS)

    Schleicher, A.M.; Sutherland, R.; Townend, J.; Toy, V.G.; Van der Pluijm, B.A.

    2015-01-01

    Clay minerals are increasingly recognised as important controls on the state and mechanical behaviour of fault systems in the upper crust. Samples retrieved by shallow drilling from two principal slip zones within the central Alpine Fault, South Island, New Zealand, offer an excellent opportunity to investigate clay formation and fluid-rock interaction in an active fault zone. Two shallow boreholes, DFDP-1A (100.6 m deep) and DFDP-1B (151.4 m) were drilled in Phase 1 of the Deep Fault Drilling Project (DFDP-1) in 2011. We provide a mineralogical and textural analysis of clays in fault gouge extracted from the Alpine Fault. Newly formed smectitic clays are observed solely in the narrow zones of fault gouge in drill core, indicating that localised mineral reactions are restricted to the fault zone. The weak preferred orientation of the clay minerals in the fault gouge indicates minimal strain-driven modification of rock fabrics. While limited in extent, our results support observations from surface outcrops and faults systems elsewhere regarding the key role of clays in fault zones and emphasise the need for future, deeper drilling into the Alpine Fault in order to understand correlative mineralogies and fabrics as a function of higher temperature and pressure conditions. (author).

  1. Faulting at Mormon Point, Death Valley, California: A low-angle normal fault cut by high-angle faults

    Science.gov (United States)

    Keener, Charles; Serpa, Laura; Pavlis, Terry L.

    1993-04-01

    New geophysical and fault kinematic studies indicate that late Cenozoic basin development in the Mormon Point area of Death Valley, California, was accommodated by fault rotations. Three of six fault segments recognized at Mormon Point are now inactive and have been rotated to low dips during extension. The remaining three segments are now active and moderately to steeply dipping. From the geophysical data, one active segment appears to offset the low-angle faults in the subsurface of Death Valley.

  2. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Science.gov (United States)

    Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

    2016-05-01

    Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

  3. Expert systems and advanced automation for space missions operations

    Science.gov (United States)

    Durrani, Sajjad H.; Perkins, Dorothy C.; Carlton, P. Douglas

    1990-01-01

    Increased complexity of space missions during the 1980s led to the introduction of expert systems and advanced automation techniques in mission operations. This paper describes several technologies in operational use or under development at the National Aeronautics and Space Administration's Goddard Space Flight Center. Several expert systems are described that diagnose faults, analyze spacecraft operations and onboard subsystem performance (in conjunction with neural networks), and perform data quality and data accounting functions. The design of customized user interfaces is discussed, with examples of their application to space missions. Displays, which allow mission operators to see the spacecraft position, orientation, and configuration under a variety of operating conditions, are described. Automated systems for scheduling are discussed, and a testbed that allows tests and demonstrations of the associated architectures, interface protocols, and operations concepts is described. Lessons learned are summarized.

  4. Real-time fault diagnosis and fault-tolerant control

    OpenAIRE

    Gao, Zhiwei; Ding, Steven X.; Cecati, Carlo

    2015-01-01

    This "Special Section on Real-Time Fault Diagnosis and Fault-Tolerant Control" of the IEEE Transactions on Industrial Electronics is motivated to provide a forum for academic and industrial communities to report recent theoretic/application results in real-time monitoring, diagnosis, and fault-tolerant design, and exchange the ideas about the emerging research direction in this field. Twenty-three papers were eventually selected through a strict peer-reviewed procedure, which represent the mo...

  5. A Review of Automated Decision Support System

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... Intelligence AI that enable decision automation based on existing facts, knowledge ... The growing reliance on data impacts dynamic data extraction and retrieval of the ... entertainment, medical, and the web. III. DECISION ...

  6. An adaptive deep convolutional neural network for rolling bearing fault diagnosis

    International Nuclear Information System (INIS)

    Fuan, Wang; Hongkai, Jiang; Haidong, Shao; Wenjing, Duan; Shuaipeng, Wu

    2017-01-01

    The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods. (paper)

  7. Comprehensive automation and monitoring of MV grids as the key element of improvement of energy supply reliability and continuity

    Directory of Open Access Journals (Sweden)

    Stanisław Kubacki

    2012-03-01

    Full Text Available The paper presents the issue of comprehensive automation and monitoring of medium voltage (MV grids as a key element of the Smart Grid concept. The existing condition of MV grid control and monitoring is discussed, and the concept of a solution which will provide the possibility of remote automatic grid reconfiguration and ensure full grid observability from the dispatching system level is introduced. Automation of MV grid switching is discussed in detail to isolate a faulty line section and supply electricity at the time of the failure to the largest possible number of recipients. An example of such automation controls’ operation is also presented. The paper’s second part presents the key role of the quick fault location function and the possibility of the MV grid’s remote reconfiguration for improving power supply reliability (SAIDI and SAIFI indices. It is also shown how an increase in the number of points fitted with faulted circuit indicators with the option of remote control of switches from the dispatch system in MV grids may affect reduction of SAIDI and SAIFI indices across ENERGA-OPERATOR SA divisions.

  8. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

    Science.gov (United States)

    Shao, Haidong; Jiang, Hongkai; Lin, Ying; Li, Xingqiu

    2018-03-01

    Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis. In this paper, a novel method called ensemble deep auto-encoders (EDAEs) is proposed for intelligent fault diagnosis of rolling bearings. Firstly, different activation functions are employed as the hidden functions to design a series of auto-encoders (AEs) with different characteristics. Secondly, EDAEs are constructed with various auto-encoders for unsupervised feature learning from the measured vibration signals. Finally, a combination strategy is designed to ensure accurate and stable diagnosis results. The proposed method is applied to analyze the experimental bearing vibration signals. The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.

  9. Fault kinematics and localised inversion within the Troms-Finnmark Fault Complex, SW Barents Sea

    Science.gov (United States)

    Zervas, I.; Omosanya, K. O.; Lippard, S. J.; Johansen, S. E.

    2018-04-01

    The areas bounding the Troms-Finnmark Fault Complex are affected by complex tectonic evolution. In this work, the history of fault growth, reactivation, and inversion of major faults in the Troms-Finnmark Fault Complex and the Ringvassøy Loppa Fault Complex is interpreted from three-dimensional seismic data, structural maps and fault displacement plots. Our results reveal eight normal faults bounding rotated fault blocks in the Troms-Finnmark Fault Complex. Both the throw-depth and displacement-distance plots show that the faults exhibit complex configurations of lateral and vertical segmentation with varied profiles. Some of the faults were reactivated by dip-linkages during the Late Jurassic and exhibit polycyclic fault growth, including radial, syn-sedimentary, and hybrid propagation. Localised positive inversion is the main mechanism of fault reactivation occurring at the Troms-Finnmark Fault Complex. The observed structural styles include folds associated with extensional faults, folded growth wedges and inverted depocentres. Localised inversion was intermittent with rifting during the Middle Jurassic-Early Cretaceous at the boundaries of the Troms-Finnmark Fault Complex to the Finnmark Platform. Additionally, tectonic inversion was more intense at the boundaries of the two fault complexes, affecting Middle Triassic to Early Cretaceous strata. Our study shows that localised folding is either a product of compressional forces or of lateral movements in the Troms-Finnmark Fault Complex. Regional stresses due to the uplift in the Loppa High and halokinesis in the Tromsø Basin are likely additional causes of inversion in the Troms-Finnmark Fault Complex.

  10. Airborne gamma-ray survey around the Negoro fault. 1; Negoro danso shuhen chiiki ni okeru kuchu {gamma} sen tansa. 1

    Energy Technology Data Exchange (ETDEWEB)

    Nakayama, E.; Kasuya, Y.; Hasegawa, H. [Aero Asahi Corp., Tokyo (Japan); Tsukuda, E. [Geological Survey of Japan, Tsukuba (Japan)

    1997-05-27

    An airborne gamma-ray survey was carried out to investigate the active fault system in the central structure line in the peripheral area of the city of Wakayama. At the same time, with an objective to enhance applicability of the airborne gamma-ray survey to active fault investigation, fundamental data were acquired and discussed. The measurement data were processed according to the standard method specified by IAEA. An ID-FFT filter and a nonlinear filter were employed to extract anomalous gamma-ray intensity values. The gamma-ray intensity distribution chart shows a noticeable positive anomalous area extending from the central part of the western edge to the north-east direction. This area agrees nearly well with the Negoro fault, but its peak portion is positioned slightly more to south than the position of the Negoro fault shown in existing data. The Sakuraike fault and the vicinity of the central structure line also show positive anomaly as a whole, particularly remarkably in the vicinity of their converging portion. However, differing from the vicinity of the Negoro fault, the areas are not extracted as an anomalous area which has directionality and extends in a line form. One of the factors for this would be that it is a fault in unsolidified deposits with low opening trend, differing from the one in solidified rocks. 1 fig., 3 tabs.

  11. ICECAP: an integrated, general-purpose, automation-assisted IC50/EC50 assay platform.

    Science.gov (United States)

    Li, Ming; Chou, Judy; King, Kristopher W; Jing, Jing; Wei, Dong; Yang, Liyu

    2015-02-01

    IC50 and EC50 values are commonly used to evaluate drug potency. Mass spectrometry (MS)-centric bioanalytical and biomarker labs are now conducting IC50/EC50 assays, which, if done manually, are tedious and error-prone. Existing bioanalytical sample preparation automation systems cannot meet IC50/EC50 assay throughput demand. A general-purpose, automation-assisted IC50/EC50 assay platform was developed to automate the calculations of spiking solutions and the matrix solutions preparation scheme, the actual spiking and matrix solutions preparations, as well as the flexible sample extraction procedures after incubation. In addition, the platform also automates the data extraction, nonlinear regression curve fitting, computation of IC50/EC50 values, graphing, and reporting. The automation-assisted IC50/EC50 assay platform can process the whole class of assays of varying assay conditions. In each run, the system can handle up to 32 compounds and up to 10 concentration levels per compound, and it greatly improves IC50/EC50 assay experimental productivity and data processing efficiency. © 2014 Society for Laboratory Automation and Screening.

  12. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Karthikeyan Elangovan

    2017-10-01

    Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.

  13. Repeated extraction of DNA from FTA cards

    DEFF Research Database (Denmark)

    Stangegaard, Michael; Ferrero, Laura; Børsting, Claus

    2011-01-01

    Extraction of DNA using magnetic bead based techniques on automated DNA extraction instruments provides a fast, reliable and reproducible method for DNA extraction from various matrices. However, the yield of extracted DNA from FTA-cards is typically low. Here, we demonstrate that it is possible...... to repeatedly extract DNA from the processed FTA-disk. The method increases the yield from the nanogram range to the microgram range....

  14. Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)

    2017-02-15

    The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.

  15. Optimal Non-Invasive Fault Classification Model for Packaged Ceramic Tile Quality Monitoring Using MMW Imaging

    Science.gov (United States)

    Agarwal, Smriti; Singh, Dharmendra

    2016-04-01

    Millimeter wave (MMW) frequency has emerged as an efficient tool for different stand-off imaging applications. In this paper, we have dealt with a novel MMW imaging application, i.e., non-invasive packaged goods quality estimation for industrial quality monitoring applications. An active MMW imaging radar operating at 60 GHz has been ingeniously designed for concealed fault estimation. Ceramic tiles covered with commonly used packaging cardboard were used as concealed targets for undercover fault classification. A comparison of computer vision-based state-of-the-art feature extraction techniques, viz, discrete Fourier transform (DFT), wavelet transform (WT), principal component analysis (PCA), gray level co-occurrence texture (GLCM), and histogram of oriented gradient (HOG) has been done with respect to their efficient and differentiable feature vector generation capability for undercover target fault classification. An extensive number of experiments were performed with different ceramic tile fault configurations, viz., vertical crack, horizontal crack, random crack, diagonal crack along with the non-faulty tiles. Further, an independent algorithm validation was done demonstrating classification accuracy: 80, 86.67, 73.33, and 93.33 % for DFT, WT, PCA, GLCM, and HOG feature-based artificial neural network (ANN) classifier models, respectively. Classification results show good capability for HOG feature extraction technique towards non-destructive quality inspection with appreciably low false alarm as compared to other techniques. Thereby, a robust and optimal image feature-based neural network classification model has been proposed for non-invasive, automatic fault monitoring for a financially and commercially competent industrial growth.

  16. Active Tectonics Revealed by River Profiles along the Puqu Fault

    Directory of Open Access Journals (Sweden)

    Ping Lu

    2015-04-01

    Full Text Available The Puqu Fault is situated in Southern Tibet. It is influenced by the eastward extrusion of Northern Tibet and carries the clockwise rotation followed by the southward extrusion. Thus, the Puqu Fault is bounded by the principal dynamic zones and the tectonic evolution remains active alongside. This study intends to understand the tectonic activity in the Puqu Fault Region from the river profiles obtained from the remotely sensed satellite imagery. A medium resolution Digital Elevation Model (DEM, 20 m was generated from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER stereo pair of images and the stream network in this region was extracted from this DEM. The indices of slope and drainage area were subsequently calculated from this ASTER DEM. Based on the stream power law, the area-slope plots of the streams were delineated to derive the indices of channel concavity and steepness, which are closely related to tectonic activity. The results show the active tectonics varying significantly along the Puqu Fault, although the potential influence of glaciations may exist. These results are expected to be useful for a better understanding of tectonic evolution in Southeastern Tibet.

  17. Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

    DEFF Research Database (Denmark)

    Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart

    2017-01-01

    This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells....... The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation......, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high...

  18. Flexible Control Strategy for Grid-Connected Inverter under Unbalanced Grid Faults without PLL

    DEFF Research Database (Denmark)

    Guo, Xiaoqiang; Liu, W.; Zhang, X.

    2015-01-01

    Power oscillation and current quality are the important performance targets for the grid-connected inverter under unbalanced grid faults. Firstly, the inherent reason for the current harmonic and power oscillation of the inverter is discussed with a quantitative analysis. Secondly, a new control...... strategy is proposed to achieve the coordinate control of power and current quality without the need for a phase-locked loop or voltage/current positive/negative sequence extraction calculation. Finally, the experimental tests are conducted under unbalanced grid faults, and the results verify...

  19. Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest.

    Science.gov (United States)

    Ma, Suliang; Chen, Mingxuan; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

    2018-04-16

    Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

  20. Application of higher order spectral features and support vector machines for bearing faults classification.

    Science.gov (United States)

    Saidi, Lotfi; Ben Ali, Jaouher; Fnaiech, Farhat

    2015-01-01

    Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals. Copyright © 2014 ISA

  1. Signal de-noising methods for fault diagnosis and troubleshooting at CANDU{sup ®} stations

    Energy Technology Data Exchange (ETDEWEB)

    Nasimi, Elnara; Gabbar, Hossam A., E-mail: hossam.gabbar@uoit.ca

    2014-12-15

    Highlights: • Fault modelling using a Fault Semantic Network (FSN). • Intelligent filtering techniques for signal de-noise in NPP. • Signal feature extraction is applied as integrated with FSN. • Increase signal-to-noise ratio (SNR). - Abstract: Over the past several years a number of domestic CANDU{sup ®} stations have experienced issues with neutron detection systems that challenged safety and operation. Intelligent troubleshooting methodology is required to aid in making risk-informed decisions related to design and operational activities, which can aid current stations and be used for the future generation of CANDU{sup ®} designs. Fault modelling approach using Fault Semantic Network (FSN) with risk estimation is proposed for this purpose. One major challenge in troubleshooting is the determination of accurate data. It is typical to have missing, incomplete or corrupted data points in large process data sets from dynamically changing systems. Therefore, it is expected that quality of obtained data will have a direct impact on the system's ability to recognize developing trends in the process upset situations. In order to enable fault detection process, intelligent filtering techniques are required to de-noise process data and extract valuable signal features in the presence of background noise. In this study, the impact of applying an optimized and intelligent filtering of process signals prior to data analysis is discussed. This is particularly important for neutronic signals in order to increase signal-to-noise ratio (SNR) which suffers the most during start-ups and low power operation. This work is complimentary to the previously published studies on FSN-based fault modelling in CANDU stations. The main objective of this work is to explore the potential research methods using a specific case study and, based on the results and outcomes from this work, to note the possible future improvements and innovation areas.

  2. Fiber fault location utilizing traffic signal in optical network.

    Science.gov (United States)

    Zhao, Tong; Wang, Anbang; Wang, Yuncai; Zhang, Mingjiang; Chang, Xiaoming; Xiong, Lijuan; Hao, Yi

    2013-10-07

    We propose and experimentally demonstrate a method for fault location in optical communication network. This method utilizes the traffic signal transmitted across the network as probe signal, and then locates the fault by correlation technique. Compared with conventional techniques, our method has a simple structure and low operation expenditure, because no additional device is used, such as light source, modulator and signal generator. The correlation detection in this method overcomes the tradeoff between spatial resolution and measurement range in pulse ranging technique. Moreover, signal extraction process can improve the location result considerably. Experimental results show that we achieve a spatial resolution of 8 cm and detection range of over 23 km with -8-dBm mean launched power in optical network based on synchronous digital hierarchy protocols.

  3. Automated Nucleic Acid Extraction Systems for Detecting Cytomegalovirus and Epstein-Barr Virus Using Real-Time PCR: A Comparison Study Between the QIAsymphony RGQ and QIAcube Systems.

    Science.gov (United States)

    Kim, Hanah; Hur, Mina; Kim, Ji Young; Moon, Hee Won; Yun, Yeo Min; Cho, Hyun Chan

    2017-03-01

    Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) are increasingly important in immunocompromised patients. Nucleic acid extraction methods could affect the results of viral nucleic acid amplification tests. We compared two automated nucleic acid extraction systems for detecting CMV and EBV using real-time PCR assays. One hundred and fifty-three whole blood (WB) samples were tested for CMV detection, and 117 WB samples were tested for EBV detection. Viral nucleic acid was extracted in parallel by using QIAsymphony RGQ and QIAcube (Qiagen GmbH, Germany), and real-time PCR assays for CMV and EBV were performed with a Rotor-Gene Q real-time PCR cycler (Qiagen). Detection rates for CMV and EBV were compared, and agreements between the two systems were analyzed. The detection rate of CMV and EBV differed significantly between the QIAsymphony RGQ and QIAcube systems (CMV, 59.5% [91/153] vs 43.8% [67/153], P=0.0005; EBV, 59.0% [69/117] vs 42.7% [50/117], P=0.0008). The two systems showed moderate agreement for CMV and EBV detection (kappa=0.43 and 0.52, respectively). QIAsymphony RGQ showed a negligible correlation with QIAcube for quantitative EBV detection. QIAcube exhibited EBV PCR inhibition in 23.9% (28/117) of samples. Automated nucleic acid extraction systems have different performances and significantly affect the detection of viral pathogens. The QIAsymphony RGQ system appears to be superior to the QIAcube system for detecting CMV and EBV. A suitable sample preparation system should be considered for optimized nucleic acid amplification in clinical laboratories.

  4. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs

    International Nuclear Information System (INIS)

    Lei, Yaguo; Zuo, Ming J

    2009-01-01

    A Hilbert–Huang transform (HHT) is a time–frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD

  5. Design of fault simulator

    Energy Technology Data Exchange (ETDEWEB)

    Gabbar, Hossam A. [Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology (UOIT), Ontario, L1H 7K4 (Canada)], E-mail: hossam.gabbar@uoit.ca; Sayed, Hanaa E.; Osunleke, Ajiboye S. [Okayama University, Graduate School of Natural Science and Technology, Division of Industrial Innovation Sciences Department of Intelligent Systems Engineering, Okayama 700-8530 (Japan); Masanobu, Hara [AspenTech Japan Co., Ltd., Kojimachi Crystal City 10F, Kojimachi, Chiyoda-ku, Tokyo 102-0083 (Japan)

    2009-08-15

    Fault simulator is proposed to understand and evaluate all possible fault propagation scenarios, which is an essential part of safety design and operation design and support of chemical/production processes. Process models are constructed and integrated with fault models, which are formulated in qualitative manner using fault semantic networks (FSN). Trend analysis techniques are used to map real time and simulation quantitative data into qualitative fault models for better decision support and tuning of FSN. The design of the proposed fault simulator is described and applied on experimental plant (G-Plant) to diagnose several fault scenarios. The proposed fault simulator will enable industrial plants to specify and validate safety requirements as part of safety system design as well as to support recovery and shutdown operation and disaster management.

  6. Automated Sleep Stage Scoring by Decision Tree Learning

    National Research Council Canada - National Science Library

    Hanaoka, Masaaki

    2001-01-01

    In this paper we describe a waveform recognition method that extracts characteristic parameters from wave- forms and a method of automated sleep stage scoring using decision tree learning that is in...

  7. Fault Management Metrics

    Science.gov (United States)

    Johnson, Stephen B.; Ghoshal, Sudipto; Haste, Deepak; Moore, Craig

    2017-01-01

    This paper describes the theory and considerations in the application of metrics to measure the effectiveness of fault management. Fault management refers here to the operational aspect of system health management, and as such is considered as a meta-control loop that operates to preserve or maximize the system's ability to achieve its goals in the face of current or prospective failure. As a suite of control loops, the metrics to estimate and measure the effectiveness of fault management are similar to those of classical control loops in being divided into two major classes: state estimation, and state control. State estimation metrics can be classified into lower-level subdivisions for detection coverage, detection effectiveness, fault isolation and fault identification (diagnostics), and failure prognosis. State control metrics can be classified into response determination effectiveness and response effectiveness. These metrics are applied to each and every fault management control loop in the system, for each failure to which they apply, and probabilistically summed to determine the effectiveness of these fault management control loops to preserve the relevant system goals that they are intended to protect.

  8. Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Muhammad Sohaib

    2018-01-01

    Full Text Available Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing, but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE- based deep neural networks (DNNs, a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

  9. Gear fault diagnosis based on the structured sparsity time-frequency analysis

    Science.gov (United States)

    Sun, Ruobin; Yang, Zhibo; Chen, Xuefeng; Tian, Shaohua; Xie, Yong

    2018-03-01

    Over the last decade, sparse representation has become a powerful paradigm in mechanical fault diagnosis due to its excellent capability and the high flexibility for complex signal description. The structured sparsity time-frequency analysis (SSTFA) is a novel signal processing method, which utilizes mixed-norm priors on time-frequency coefficients to obtain a fine match for the structure of signals. In order to extract the transient feature from gear vibration signals, a gear fault diagnosis method based on SSTFA is proposed in this work. The steady modulation components and impulsive components of the defective gear vibration signals can be extracted simultaneously by choosing different time-frequency neighborhood and generalized thresholding operators. Besides, the time-frequency distribution with high resolution is obtained by piling different components in the same diagram. The diagnostic conclusion can be made according to the envelope spectrum of the impulsive components or by the periodicity of impulses. The effectiveness of the method is verified by numerical simulations, and the vibration signals registered from a gearbox fault simulator and a wind turbine. To validate the efficiency of the presented methodology, comparisons are made among some state-of-the-art vibration separation methods and the traditional time-frequency analysis methods. The comparisons show that the proposed method possesses advantages in separating feature signals under strong noise and accounting for the inner time-frequency structure of the gear vibration signals.

  10. Study on seismic hazard assessment of large active fault systems. Evolution of fault systems and associated geomorphic structures: fault model test and field survey

    International Nuclear Information System (INIS)

    Ueta, Keichi; Inoue, Daiei; Miyakoshi, Katsuyoshi; Miyagawa, Kimio; Miura, Daisuke

    2003-01-01

    Sandbox experiments and field surveys were performed to investigate fault system evolution and fault-related deformation of ground surface, the Quaternary deposits and rocks. The summary of the results is shown below. 1) In the case of strike-slip faulting, the basic fault sequence runs from early en echelon faults and pressure ridges through linear trough. The fault systems associated with the 2000 western Tottori earthquake are shown as en echelon pattern that characterize the early stage of wrench tectonics, therefore no thoroughgoing surface faulting was found above the rupture as defined by the main shock and aftershocks. 2) Low-angle and high-angle reverse faults commonly migrate basinward with time, respectively. With increasing normal fault displacement in bedrock, normal fault develops within range after reverse fault has formed along range front. 3) Horizontal distance of surface rupture from the bedrock fault normalized by the height of the Quaternary deposits agrees well with those of model tests. 4) Upward-widening damage zone, where secondary fractures develop, forms in the handing wall side of high-angle reverse fault at the Kamioka mine. (author)

  11. Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

    Science.gov (United States)

    Kusumoto, Shigekazu

    2017-12-01

    The dips of boundaries in faults and caldera walls play an important role in understanding their formation mechanisms. The fault dip is a particularly important parameter in numerical simulations for hazard map creation as the fault dip affects estimations of the area of disaster occurrence. In this study, I introduce a technique for estimating the fault dip using the eigenvector of the observed or calculated gravity gradient tensor on a profile and investigating its properties through numerical simulations. From numerical simulations, it was found that the maximum eigenvector of the tensor points to the high-density causative body, and the dip of the maximum eigenvector closely follows the dip of the normal fault. It was also found that the minimum eigenvector of the tensor points to the low-density causative body and that the dip of the minimum eigenvector closely follows the dip of the reverse fault. It was shown that the eigenvector of the gravity gradient tensor for estimating fault dips is determined by fault type. As an application of this technique, I estimated the dip of the Kurehayama Fault located in Toyama, Japan, and obtained a result that corresponded to conventional fault dip estimations by geology and geomorphology. Because the gravity gradient tensor is required for this analysis, I present a technique that estimates the gravity gradient tensor from the gravity anomaly on a profile.

  12. Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2016-01-01

    Full Text Available Single-Stage Extreme Learning Machine (SS-ELM is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

  13. Scissoring Fault Rupture Properties along the Median Tectonic Line Fault Zone, Southwest Japan

    Science.gov (United States)

    Ikeda, M.; Nishizaka, N.; Onishi, K.; Sakamoto, J.; Takahashi, K.

    2017-12-01

    The Median Tectonic Line fault zone (hereinafter MTLFZ) is the longest and most active fault zone in Japan. The MTLFZ is a 400-km-long trench parallel right-lateral strike-slip fault accommodating lateral slip components of the Philippine Sea plate oblique subduction beneath the Eurasian plate [Fitch, 1972; Yeats, 1996]. Complex fault geometry evolves along the MTLFZ. The geomorphic and geological characteristics show a remarkable change through the MTLFZ. Extensional step-overs and pull-apart basins and a pop-up structure develop in western and eastern parts of the MTLFZ, respectively. It is like a "scissoring fault properties". We can point out two main factors to form scissoring fault properties along the MTLFZ. One is a regional stress condition, and another is a preexisting fault. The direction of σ1 anticlockwise rotate from N170°E [Famin et al., 2014] in the eastern Shikoku to Kinki areas and N100°E [Research Group for Crustral Stress in Western Japan, 1980] in central Shikoku to N85°E [Onishi et al., 2016] in western Shikoku. According to the rotation of principal stress directions, the western and eastern parts of the MTLFZ are to be a transtension and compression regime, respectively. The MTLFZ formed as a terrain boundary at Cretaceous, and has evolved with a long active history. The fault style has changed variously, such as left-lateral, thrust, normal and right-lateral. Under the structural condition of a preexisting fault being, the rupture does not completely conform to Anderson's theory for a newly formed fault, as the theory would require either purely dip-slip motion on the 45° dipping fault or strike-slip motion on a vertical fault. The fault rupture of the 2013 Barochistan earthquake in Pakistan is a rare example of large strike-slip reactivation on a relatively low angle dipping fault (thrust fault), though many strike-slip faults have vertical plane generally [Avouac et al., 2014]. In this presentation, we, firstly, show deep subsurface

  14. Demonstration and validation of automated agricultural field extraction from multi-temporal Landsat data for the majority of United States harvested cropland

    Science.gov (United States)

    Yan, L.; Roy, D. P.

    2014-12-01

    The spatial distribution of agricultural fields is a fundamental description of rural landscapes and the location and extent of fields is important to establish the area of land utilized for agricultural yield prediction, resource allocation, and for economic planning, and may be indicative of the degree of agricultural capital investment, mechanization, and labor intensity. To date, field objects have not been extracted from satellite data over large areas because of computational constraints, the complexity of the extraction task, and because consistently processed appropriate resolution data have not been available or affordable. A recently published automated methodology to extract agricultural crop fields from weekly 30 m Web Enabled Landsat data (WELD) time series was refined and applied to 14 states that cover 70% of harvested U.S. cropland (USDA 2012 Census). The methodology was applied to 2010 combined weekly Landsat 5 and 7 WELD data. The field extraction and quantitative validation results are presented for the following 14 states: Iowa, North Dakota, Illinois, Kansas, Minnesota, Nebraska, Texas, South Dakota, Missouri, Indiana, Ohio, Wisconsin, Oklahoma and Michigan (sorted by area of harvested cropland). These states include the top 11 U.S states by harvested cropland area. Implications and recommendations for systematic application to global coverage Landsat data are discussed.

  15. An Active Fault-Tolerant Control Method Ofunmanned Underwater Vehicles with Continuous and Uncertain Faults

    Directory of Open Access Journals (Sweden)

    Daqi Zhu

    2008-11-01

    Full Text Available This paper introduces a novel thruster fault diagnosis and accommodation system for open-frame underwater vehicles with abrupt faults. The proposed system consists of two subsystems: a fault diagnosis subsystem and a fault accommodation sub-system. In the fault diagnosis subsystem a ICMAC(Improved Credit Assignment Cerebellar Model Articulation Controllers neural network is used to realize the on-line fault identification and the weighting matrix computation. The fault accommodation subsystem uses a control algorithm based on weighted pseudo-inverse to find the solution of the control allocation problem. To illustrate the proposed method effective, simulation example, under multi-uncertain abrupt faults, is given in the paper.

  16. Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method

    Directory of Open Access Journals (Sweden)

    Cui Jiang

    2015-06-01

    Full Text Available Power electronic circuits (PECs are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc. are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc. are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs, and in our design these RAs are resolved with the one-against-one support vector machine (SVM classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.

  17. Rapid extraction of PCDD/Fs from soil and fly ash samples. Pressurized fluid extraction (PFE) and microwave-assisted extraction (MAE)

    Energy Technology Data Exchange (ETDEWEB)

    Sanz, P.; Fabrellas, B. [Centro de Investigaciones Energeticas Medioambientales y Tecnologicas (CIEMAT), Madrid (Spain)

    2004-09-15

    The main reference extraction method in the analysis of polychlorinated dibenzop- dioxins and dibenzofurans (PCDD/Fs) is still the Soxhlet extraction. But it requires long extraction times (up to 24 hs), large volumes of hazardous organic solvents (100-300 ml) and its automation is limited. Pressurized Fluid Extraction (PFE) and Microwave-Assisted Extraction (MAE) are two relatively new extraction techniques that reduce the time and the volume of solvent required for extraction. However, very different PFE extraction conditions are found for the same enviromental matrices in the literature. MAE is not a extraction technique very applied for the analysis of PCDD/Fs yet, although it is used for the determination of other organic compounds, such as PCBs and PAHs. In this study, PFE and MAE extraction conditions were optimized to determine PCDDs y PCDFs in fly ash and soil/sediment samples. Conventional Soxhlet extraction with toluene was used to compare the extraction efficiency of both techniques.

  18. Automated fuel fabrication- a vision comes true

    International Nuclear Information System (INIS)

    Hemantha Rao, G.V.S.; Prakash, M.S.; Setty, C.R.P.; Gupta, U.C.

    1997-01-01

    When New Uranium Fuel Assembly Project at Nuclear Fuel Complex (NFC) begins production, its operator will have equipment provided with intramachine handling systems working automatically by pressing a single button. Additionally simple low cost inter machine handling systems will further help in critical areas. All these inter and intra machine handling systems will result in improved reliability, productivity and quality. The fault diagnostics, mimics and real time data acquisition systems make the plant more operator friendly. The paper deals with the experience starting from layout, selection of product carriers, different handling systems, the latest technology and the integration of which made the vision on automation in fuel fabrication come true. (author)

  19. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These ...

  20. Fault Tolerant Feedback Control

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, H.

    2001-01-01

    An architecture for fault tolerant feedback controllers based on the Youla parameterization is suggested. It is shown that the Youla parameterization will give a residual vector directly in connection with the fault diagnosis part of the fault tolerant feedback controller. It turns out...... that there is a separation be-tween the feedback controller and the fault tolerant part. The closed loop feedback properties are handled by the nominal feedback controller and the fault tolerant part is handled by the design of the Youla parameter. The design of the fault tolerant part will not affect the design...... of the nominal feedback con-troller....