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Sample records for fault inferences based

  1. A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

    Sadeh, Javad; Afradi, Hamid [Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box: 91775-1111, Mashhad (Iran)

    2009-11-15

    This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines. (author)

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

    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...

  3. SDG multiple fault diagnosis by real-time inverse inference

    Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng

    2005-01-01

    In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency

  4. SDG multiple fault diagnosis by real-time inverse inference

    Zhang Zhaoqian; Wu Chongguang; Zhang Beike; Xia Tao; Li Anfeng

    2005-02-01

    In the past 20 years, one of the qualitative simulation technologies, signed directed graph (SDG) has been widely applied in the field of chemical fault diagnosis. However, the assumption of single fault origin was usually used by many former researchers. As a result, this will lead to the problem of combinatorial explosion and has limited SDG to the realistic application on the real process. This is mainly because that most of the former researchers used forward inference engine in the commercial expert system software to carry out the inverse diagnosis inference on the SDG model which violates the internal principle of diagnosis mechanism. In this paper, we present a new SDG multiple faults diagnosis method by real-time inverse inference. This is a method of multiple faults diagnosis from the genuine significance and the inference engine use inverse mechanism. At last, we give an example of 65t/h furnace diagnosis system to demonstrate its applicability and efficiency.

  5. Performance based fault diagnosis

    Niemann, Hans Henrik

    2002-01-01

    Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

  6. An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

    Ajay Kumar, M.; Srikanth, N. V.

    2014-03-01

    In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

  7. Information Based Fault Diagnosis

    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 ...

  8. Fault tolerant control based on active fault diagnosis

    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...

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

    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)

  10. Fault strength in Marmara region inferred from the geometry of the principle stress axes and fault orientations: A case study for the Prince's Islands fault segment

    Pinar, Ali; Coskun, Zeynep; Mert, Aydin; Kalafat, Dogan

    2015-04-01

    The general consensus based on historical earthquake data point out that the last major moment release on the Prince's islands fault was in 1766 which in turn signals an increased seismic risk for Istanbul Metropolitan area considering the fact that most of the 20 mm/yr GPS derived slip rate for the region is accommodated mostly by that fault segment. The orientation of the Prince's islands fault segment overlaps with the NW-SE direction of the maximum principle stress axis derived from the focal mechanism solutions of the large and moderate sized earthquakes occurred in the Marmara region. As such, the NW-SE trending fault segment translates the motion between the two E-W trending branches of the North Anatolian fault zone; one extending from the Gulf of Izmit towards Çınarcık basin and the other extending between offshore Bakırköy and Silivri. The basic relation between the orientation of the maximum and minimum principal stress axes, the shear and normal stresses, and the orientation of a fault provides clue on the strength of a fault, i.e., its frictional coefficient. Here, the angle between the fault normal and maximum compressive stress axis is a key parameter where fault normal and fault parallel maximum compressive stress might be a necessary and sufficient condition for a creeping event. That relation also implies that when the trend of the sigma-1 axis is close to the strike of the fault the shear stress acting on the fault plane approaches zero. On the other hand, the ratio between the shear and normal stresses acting on a fault plane is proportional to the coefficient of frictional coefficient of the fault. Accordingly, the geometry between the Prince's islands fault segment and a maximum principal stress axis matches a weak fault model. In the frame of the presentation we analyze seismological data acquired in Marmara region and interpret the results in conjuction with the above mentioned weak fault model.

  11. Inferring Fault Frictional and Reservoir Hydraulic Properties From Injection-Induced Seismicity

    Jagalur-Mohan, Jayanth; Jha, Birendra; Wang, Zheng; Juanes, Ruben; Marzouk, Youssef

    2018-02-01

    Characterizing the rheological properties of faults and the evolution of fault friction during seismic slip are fundamental problems in geology and seismology. Recent increases in the frequency of induced earthquakes have intensified the need for robust methods to estimate fault properties. Here we present a novel approach for estimation of aquifer and fault properties, which combines coupled multiphysics simulation of injection-induced seismicity with adaptive surrogate-based Bayesian inversion. In a synthetic 2-D model, we use aquifer pressure, ground displacements, and fault slip measurements during fluid injection to estimate the dynamic fault friction, the critical slip distance, and the aquifer permeability. Our forward model allows us to observe nonmonotonic evolutions of shear traction and slip on the fault resulting from the interplay of several physical mechanisms, including injection-induced aquifer expansion, stress transfer along the fault, and slip-induced stress relaxation. This interplay provides the basis for a successful joint inversion of induced seismicity, yielding well-informed Bayesian posterior distributions of dynamic friction and critical slip. We uncover an inverse relationship between dynamic friction and critical slip distance, which is in agreement with the small dynamic friction and large critical slip reported during seismicity on mature faults.

  12. Tidal Sensitivity of Declustered Low Frequency Earthquake Families and Inferred Creep Episodes on the San Andreas Fault

    Babb, A.; Thomas, A.; Bletery, Q.

    2017-12-01

    Low frequency earthquakes (LFEs) are detected at depths of 16-30 km on a 150 km section of the San Andreas Fault centered at Parkfield, CA. The LFEs are divided into 88 families based on waveform similarity. Each family is thought to represent a brittle asperity on the fault surface that repeatedly slips during aseismic slip of the surrounding fault. LFE occurrence is irregular which allows families to be divided into continuous and episodic. In continuous families a burst of a few LFE events recurs every few days while episodic families experience essentially quiescent periods often lasting months followed by bursts of hundreds of events over a few days. The occurrence of LFEs has also been shown to be sensitive to extremely small ( 1kPa) tidal stress perturbations. However, the clustered nature of LFE occurrence could potentially bias estimates of tidal sensitivity. Here we re-evaluate the tidal sensitivity of LFE families on the deep San Andreas using a declustered catalog. In this catalog LFE bursts are isolated based on the recurrence intervals between individual LFE events for each family. Preliminary analysis suggests that declustered LFE families are still highly sensitive to tidal stress perturbations, primarily right-lateral shear stress (RLSS) and to a lesser extent fault normal stress (FNS). We also find inferred creep episodes initiate preferentially during times of positive RLSS.

  13. A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

    Liu, Yong-Kuo; Wu, Guo-Hua; Xie, Chun-Li; Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun

    2016-01-01

    Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

  14. A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

    Liu, Yong-Kuo, E-mail: LYK08@126.com [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Wu, Guo-Hua [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Institute of Nuclear Energy Technology, Tsinghua University, Beijing 100084 (China); Xie, Chun-Li [Traffic College, Northeast Forestry University, Harbin, 150040 (China); Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China)

    2016-02-15

    Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.

  15. Norm based design of fault detectors

    Rank, Mike Lind; Niemann, Hans Henrik

    1999-01-01

    The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered in this paper from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well...

  16. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  17. Possible deep fault slip preceding the 2004 Parkfield earthquake, inferred from detailed observations of tectonic tremor

    Shelly, David R.

    2009-01-01

    Earthquake predictability depends, in part, on the degree to which sudden slip is preceded by slow aseismic slip. Recently, observations of deep tremor have enabled inferences of deep slow slip even when detection by other means is not possible, but these data are limited to certain areas and mostly the last decade. The region near Parkfield, California, provides a unique convergence of several years of high-quality tremor data bracketing a moderate earthquake, the 2004 magnitude 6.0 event. Here, I present detailed observations of tectonic tremor from mid-2001 through 2008 that indicate deep fault slip both before and after the Parkfield earthquake that cannot be detected with surface geodetic instruments. While there is no obvious short-term precursor, I find unidirectional tremor migration accompanied by elevated tremor rates in the 3 months prior to the earthquake, which suggests accelerated creep on the fault ∼16 km beneath the eventual earthquake hypocenter.

  18. Causal inference based on counterfactuals

    Höfler M

    2005-09-01

    Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

  19. Norm based Threshold Selection for Fault Detectors

    Rank, Mike Lind; Niemann, Henrik

    1998-01-01

    The design of fault detectors for fault detection and isolation (FDI) in dynamic systems is considered from a norm based point of view. An analysis of norm based threshold selection is given based on different formulations of FDI problems. Both the nominal FDI problem as well as the uncertain FDI...... problem are considered. Based on this analysis, a performance index based on norms of the involved transfer functions is given. The performance index allows us also to optimize the structure of the fault detection filter directly...

  20. Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines.

    Swetapadma, Aleena; Yadav, Anamika

    2015-01-01

    Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance.

  1. Inference

    Møller, Jesper

    2010-01-01

    Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....

  2. Case-Based Fault Diagnostic System

    Mohamed, A.H.

    2014-01-01

    Nowadays, case-based fault diagnostic (CBFD) systems have become important and widely applied problem solving technologies. They are based on the assumption that “similar faults have similar diagnosis”. On the other hand, CBFD systems still suffer from some limitations. Common ones of them are: (1) failure of CBFD to have the needed diagnosis for the new faults that have no similar cases in the case library. (2) Limited memorization when increasing the number of stored cases in the library. The proposed research introduces incorporating the neural network into the case based system to enable the system to diagnose all the faults. Neural networks have proved their success in the classification and diagnosis problems. The suggested system uses the neural network to diagnose the new faults (cases) that cannot be diagnosed by the traditional CBR diagnostic system. Besides, the proposed system can use the another neural network to control adding and deleting the cases in the library to manage the size of the cases in the case library. However, the suggested system has improved the performance of the case based fault diagnostic system when applied for the motor rolling bearing as a case of study

  3. Fault diagnosis and fault-tolerant control based on adaptive control approach

    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. .

  4. Reset Tree-Based Optical Fault Detection

    Howon Kim

    2013-05-01

    Full Text Available In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit’s reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool.

  5. Novel neural networks-based fault tolerant control scheme with fault alarm.

    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.

  6. Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers

    Salahshoor, Karim [Department of Instrumentation and Automation, Petroleum University of Technology, Tehran (Iran, Islamic Republic of); Kordestani, Mojtaba; Khoshro, Majid S. [Department of Control Engineering, Islamic Azad University South Tehran branch (Iran, Islamic Republic of)

    2010-12-15

    The subject of FDD (fault detection and diagnosis) has gained widespread industrial interest in machine condition monitoring applications. This is mainly due to the potential advantage to be achieved from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a new FDD scheme for condition machinery of an industrial steam turbine using a data fusion methodology. Fusion of a SVM (support vector machine) classifier with an ANFIS (adaptive neuro-fuzzy inference system) classifier, integrated into a common framework, is utilized to enhance the fault detection and diagnostic tasks. For this purpose, a multi-attribute data is fused into aggregated values of a single attribute by OWA (ordered weighted averaging) operators. The simulation studies indicate that the resulting fusion-based scheme outperforms the individual SVM and ANFIS systems to detect and diagnose incipient steam turbine faults. (author)

  7. Statistical inference based on divergence measures

    Pardo, Leandro

    2005-01-01

    The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, prese...

  8. Inference

    Møller, Jesper

    (This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...

  9. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost

  10. Fault prediction for nonlinear stochastic system with incipient faults based on particle filter and nonlinear regression.

    Ding, Bo; Fang, Huajing

    2017-05-01

    This paper is concerned with the fault prediction for the nonlinear stochastic system with incipient faults. Based on the particle filter and the reasonable assumption about the incipient faults, the modified fault estimation algorithm is proposed, and the system state is estimated simultaneously. According to the modified fault estimation, an intuitive fault detection strategy is introduced. Once each of the incipient fault is detected, the parameters of which are identified by a nonlinear regression method. Then, based on the estimated parameters, the future fault signal can be predicted. Finally, the effectiveness of the proposed method is verified by the simulations of the Three-tank system. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  11. TWT transmitter fault prediction based on ANFIS

    Li, Mengyan; Li, Junshan; Li, Shuangshuang; Wang, Wenqing; Li, Fen

    2017-11-01

    Fault prediction is an important component of health management, and plays an important role in the reliability guarantee of complex electronic equipments. Transmitter is a unit with high failure rate. The cathode performance of TWT is a common fault of transmitter. In this dissertation, a model based on a set of key parameters of TWT is proposed. By choosing proper parameters and applying adaptive neural network training model, this method, combined with analytic hierarchy process (AHP), has a certain reference value for the overall health judgment of TWT transmitters.

  12. Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation

    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.

  13. Study of fault diagnosis software design for complex system based on fault tree

    Yuan Run; Li Yazhou; Wang Jianye; Hu Liqin; Wang Jiaqun; Wu Yican

    2012-01-01

    Complex systems always have high-level reliability and safety requirements, and same does their diagnosis work. As a great deal of fault tree models have been acquired during the design and operation phases, a fault diagnosis method which combines fault tree analysis with knowledge-based technology has been proposed. The prototype of fault diagnosis software has been realized and applied to mobile LIDAR system. (authors)

  14. Mantle hydration along outer-rise faults inferred from serpentinite permeability.

    Hatakeyama, Kohei; Katayama, Ikuo; Hirauchi, Ken-Ichi; Michibayashi, Katsuyoshi

    2017-10-24

    Recent geophysical surveys indicate that hydration (serpentinization) of oceanic mantle is related to outer-rise faulting prior to subduction. The serpentinization of oceanic mantle influences the generation of intermediate-depth earthquakes and subduction water flux, thereby promoting arc volcanism. Since the chemical reactions that produce serpentinite are geologically rapid at low temperatures, the flux of water delivery to the reaction front appears to control the lateral extent of serpentinization. In this study, we measured the permeability of low-temperature serpentinites composed of lizardite and chrysotile, and calculated the lateral extent of serpentinization along an outer-rise fault based on Darcy's law. The experimental results indicate that serpentinization extends to a region several hundred meters wide in the direction normal to the outer-rise fault in the uppermost oceanic mantle. We calculated the global water flux carried by serpentinized oceanic mantle ranging from 1.7 × 10 11 to 2.4 × 10 12  kg/year, which is comparable or even higher than the water flux of hydrated oceanic crust.

  15. Spatial Inference Based on Geometric Proportional Analogies

    Mullally, Emma-Claire; O'Donoghue, Diarmuid P.

    2006-01-01

    We describe an instance-based reasoning solution to a variety of spatial reasoning problems. The solution centers on identifying an isomorphic mapping between labelled graphs that represent some problem data and a known solution instance. We describe a number of spatial reasoning problems that are solved by generating non-deductive inferences, integrating topology with area (and other) features. We report the accuracy of our algorithm on different categories of spatial reasoning tasks from th...

  16. Geometry of the Nojima fault at Nojima-Hirabayashi, Japan - I. A simple damage structure inferred from borehole core permeability

    Lockner, David A.; Tanaka, Hidemi; Ito, Hisao; Ikeda, Ryuji; Omura, Kentaro; Naka, Hisanobu

    2009-01-01

    The 1995 Kobe (Hyogo-ken Nanbu) earthquake, M = 7.2, ruptured the Nojima fault in southwest Japan. We have studied core samples taken from two scientific drillholes that crossed the fault zone SW of the epicentral region on Awaji Island. The shallower hole, drilled by the Geological Survey of Japan (GSJ), was started 75 m to the SE of the surface trace of the Nojima fault and crossed the fault at a depth of 624 m. A deeper hole, drilled by the National Research Institute for Earth Science and Disaster Prevention (NIED) was started 302 m to the SE of the fault and crossed fault strands below a depth of 1140 m. We have measured strength and matrix permeability of core samples taken from these two drillholes. We find a strong correlation between permeability and proximity to the fault zone shear axes. The half-width of the high permeability zone (approximately 15 to 25 m) is in good agreement with the fault zone width inferred from trapped seismic wave analysis and other evidence. The fault zone core or shear axis contains clays with permeabilities of approximately 0.1 to 1 microdarcy at 50 MPa effective confining pressure (10 to 30 microdarcy at in situ pressures). Within a few meters of the fault zone core, the rock is highly fractured but has sustained little net shear. Matrix permeability of this zone is approximately 30 to 60 microdarcy at 50 MPa effective confining pressure (300 to 1000 microdarcy at in situ pressures). Outside this damage zone, matrix permeability drops below 0.01 microdarcy. The clay-rich core material has the lowest strength with a coefficient of friction of approximately 0.55. Shear strength increases with distance from the shear axis. These permeability and strength observations reveal a simple fault zone structure with a relatively weak fine-grained core surrounded by a damage zone of fractured rock. In this case, the damage zone will act as a high-permeability conduit for vertical and horizontal flow in the plane of the

  17. Fault tolerant system based on IDDQ testing

    Guibane, Badi; Hamdi, Belgacem; Mtibaa, Abdellatif; Bensalem, Brahim

    2018-06-01

    Offline test is essential to ensure good manufacturing quality. However, for permanent or transient faults that occur during the use of the integrated circuit in an application, an online integrated test is needed as well. This procedure should ensure the detection and possibly the correction or the masking of these faults. This requirement of self-correction is sometimes necessary, especially in critical applications that require high security such as automotive, space or biomedical applications. We propose a fault-tolerant design for analogue and mixed-signal design complementary metal oxide (CMOS) circuits based on the quiescent current supply (IDDQ) testing. A defect can cause an increase in current consumption. IDDQ testing technique is based on the measurement of power supply current to distinguish between functional and failed circuits. The technique has been an effective testing method for detecting physical defects such as gate-oxide shorts, floating gates (open) and bridging defects in CMOS integrated circuits. An architecture called BICS (Built In Current Sensor) is used for monitoring the supply current (IDDQ) of the connected integrated circuit. If the measured current is not within the normal range, a defect is signalled and the system switches connection from the defective to a functional integrated circuit. The fault-tolerant technique is composed essentially by a double mirror built-in current sensor, allowing the detection of abnormal current consumption and blocks allowing the connection to redundant circuits, if a defect occurs. Spices simulations are performed to valid the proposed design.

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

    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

  19. Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System

    Ye, Z. [Department of Electrical & amp; Computer Engineering, Queen' s University, Kingston, Ont. (Canada K7L 3N6); Sadeghian, A. [Department of Computer Science, Ryerson University, Toronto, Ont. (Canada M5B 2K3); Wu, B. [Department of Electrical & amp; Computer Engineering, Ryerson University, Toronto, Ont. (Canada M5B 2K3)

    2006-06-15

    A novel online diagnostic algorithm for mechanical faults of electrical machines with variable speed drive systems is presented in this paper. Using Wavelet Packet Decomposition (WPD), a set of feature coefficients, represented with different frequency resolutions, related to the mechanical faults is extracted from the stator current of the induction motors operating over a wide range of speeds. A new integrated diagnostic system for electrical machine mechanical faults is then proposed using multiple Adaptive Neuro-fuzzy Inference Systems (ANFIS). This paper shows that using multiple ANFIS units significantly reduces the scale and complexity of the system and speeds up the training of the network. The diagnostic algorithm is validated on a three-phase induction motor drive system, and it is proven to be capable of detecting rotor bar breakage and air gap eccentricity faults with high accuracy. The algorithm is applicable to a variety of industrial applications where either continuous on-line monitoring or off-line fault diagnostics is required. (author)

  20. Abductive Inference using Array-Based Logic

    Frisvad, Jeppe Revall; Falster, Peter; Møller, Gert L.

    The notion of abduction has found its usage within a wide variety of AI fields. Computing abductive solutions has, however, shown to be highly intractable in logic programming. To avoid this intractability we present a new approach to logicbased abduction; through the geometrical view of data...... employed in array-based logic we embrace abduction in a simple structural operation. We argue that a theory of abduction on this form allows for an implementation which, at runtime, can perform abductive inference quite efficiently on arbitrary rules of logic representing knowledge of finite domains....

  1. Frequency Based Fault Detection in Wind Turbines

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  2. Adaptive PCA based fault diagnosis scheme in imperial smelting process.

    Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin

    2014-09-01

    In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

    Wang, H.; Jing, X. J.

    2017-07-01

    This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

  4. Data-based fault-tolerant control for affine nonlinear systems with actuator faults.

    Xie, Chun-Hua; Yang, Guang-Hong

    2016-09-01

    This paper investigates the fault-tolerant control (FTC) problem for unknown nonlinear systems with actuator faults including stuck, outage, bias and loss of effectiveness. The upper bounds of stuck faults, bias faults and loss of effectiveness faults are unknown. A new data-based FTC scheme is proposed. It consists of the online estimations of the bounds and a state-dependent function. The estimations are adjusted online to compensate automatically the actuator faults. The state-dependent function solved by using real system data helps to stabilize the system. Furthermore, all signals in the resulting closed-loop system are uniformly bounded and the states converge asymptotically to zero. Compared with the existing results, the proposed approach is data-based. Finally, two simulation examples are provided to show the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Fault Location Based on Synchronized Measurements: A Comprehensive Survey

    Al-Mohammed, A. H.; Abido, M. A.

    2014-01-01

    This paper presents a comprehensive survey on transmission and distribution fault location algorithms that utilize synchronized measurements. Algorithms based on two-end synchronized measurements and fault location algorithms on three-terminal and multiterminal lines are reviewed. Series capacitors equipped with metal oxide varistors (MOVs), when set on a transmission line, create certain problems for line fault locators and, therefore, fault location on series-compensated lines is discussed. The paper reports the work carried out on adaptive fault location algorithms aiming at achieving better fault location accuracy. Work associated with fault location on power system networks, although limited, is also summarized. Additionally, the nonstandard high-frequency-related fault location techniques based on wavelet transform are discussed. Finally, the paper highlights the area for future research. PMID:24701191

  6. Fault Location Based on Synchronized Measurements: A Comprehensive Survey

    A. H. Al-Mohammed

    2014-01-01

    Full Text Available This paper presents a comprehensive survey on transmission and distribution fault location algorithms that utilize synchronized measurements. Algorithms based on two-end synchronized measurements and fault location algorithms on three-terminal and multiterminal lines are reviewed. Series capacitors equipped with metal oxide varistors (MOVs, when set on a transmission line, create certain problems for line fault locators and, therefore, fault location on series-compensated lines is discussed. The paper reports the work carried out on adaptive fault location algorithms aiming at achieving better fault location accuracy. Work associated with fault location on power system networks, although limited, is also summarized. Additionally, the nonstandard high-frequency-related fault location techniques based on wavelet transform are discussed. Finally, the paper highlights the area for future research.

  7. Subsurface structures of the active reverse fault zones in Japan inferred from gravity anomalies.

    Matsumoto, N.; Sawada, A.; Hiramatsu, Y.; Okada, S.; Tanaka, T.; Honda, R.

    2016-12-01

    The object of our study is to examine subsurface features such as continuity, segmentation and faulting type, of the active reverse fault zones. We use the gravity data published by the Gravity Research Group in Southwest Japan (2001), the Geographical Survey Institute (2006), Yamamoto et al. (2011), Honda et al. (2012), and the Geological Survey of Japan, AIST (2013) in this study. We obtained the Bouguer anomalies through terrain corrections with 10 m DEM (Sawada et al. 2015) under the assumed density of 2670 kg/m3, a band-pass filtering, and removal of linear trend. Several derivatives and structural parameters calculated from a gravity gradient tensor are applied to highlight the features, such as a first horizontal derivatives (HD), a first vertical derivatives (VD), a normalized total horizontal derivative (TDX), a dip angle (β), and a dimensionality index (Di). We analyzed 43 reverse fault zones in northeast Japan and the northern part of southwest Japan among major active fault zones selected by Headquarters for Earthquake Research Promotion. As the results, the subsurface structural boundaries clearly appear along the faults at 21 faults zones. The weak correlations appear at 13 fault zones, and no correlations are recognized at 9 fault zones. For example, in the Itoigawa-Shizuoka tectonic line, the subsurface structure boundary seems to extend further north than the surface trace. Also, a left stepping structure of the fault around Hakuba is more clearly observed with HD. The subsurface structures, which detected as the higher values of HD, are distributed on the east side of the surface rupture in the north segments and on the west side in the south segments, indicating a change of the dip direction, the east dipping to the west dipping, from north to south. In the Yokote basin fault zone, the subsurface structural boundary are clearly detected with HD, VD and TDX along the fault zone in the north segment, but less clearly in the south segment. Also, Di

  8. Mixed linear-nonlinear fault slip inversion: Bayesian inference of model, weighting, and smoothing parameters

    Fukuda, J.; Johnson, K. M.

    2009-12-01

    Studies utilizing inversions of geodetic data for the spatial distribution of coseismic slip on faults typically present the result as a single fault plane and slip distribution. Commonly the geometry of the fault plane is assumed to be known a priori and the data are inverted for slip. However, sometimes there is not strong a priori information on the geometry of the fault that produced the earthquake and the data is not always strong enough to completely resolve the fault geometry. We develop a method to solve for the full posterior probability distribution of fault slip and fault geometry parameters in a Bayesian framework using Monte Carlo methods. The slip inversion problem is particularly challenging because it often involves multiple data sets with unknown relative weights (e.g. InSAR, GPS), model parameters that are related linearly (slip) and nonlinearly (fault geometry) through the theoretical model to surface observations, prior information on model parameters, and a regularization prior to stabilize the inversion. We present the theoretical framework and solution method for a Bayesian inversion that can handle all of these aspects of the problem. The method handles the mixed linear/nonlinear nature of the problem through combination of both analytical least-squares solutions and Monte Carlo methods. We first illustrate and validate the inversion scheme using synthetic data sets. We then apply the method to inversion of geodetic data from the 2003 M6.6 San Simeon, California earthquake. We show that the uncertainty in strike and dip of the fault plane is over 20 degrees. We characterize the uncertainty in the slip estimate with a volume around the mean fault solution in which the slip most likely occurred. Slip likely occurred somewhere in a volume that extends 5-10 km in either direction normal to the fault plane. We implement slip inversions with both traditional, kinematic smoothing constraints on slip and a simple physical condition of uniform stress

  9. Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

    Quan Zhou

    2017-07-01

    Full Text Available The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.

  10. Seismic hazard of the Enriquillog-Plantain Garden fault in Haiti inferred from palaeoseismology

    Prentice, C.S.; Mann, P.; Crone, A.J.; Gold, R.D.; Hudnut, K.W.; Briggs, R.W.; Koehler, R.D.; Jean, P.

    2010-01-01

    The Enriquillog-Plantain Garden fault zone is recognized as one of the primary plate-bounding fault systems in Haiti. The strike-slip fault runs adjacent to the city of Port-au-Prince and was initially thought to be the source of the 12 January 2010, M w 7.0 earthquake. Haiti experienced significant earthquakes in 1751 and 1770 (refsA, 3, 4, 5), but the role of the Enriquillog-Plantain Garden fault zone in these earthquakes is poorly known. We use satellite imagery, aerial photography, light detection and ranging (LIDAR) and field investigations to document Quaternary activity on the Enriquillog-Plantain Garden fault. We report late Quaternary, left-lateral offsets of up to 160m, and a set of small offsets ranging from 1.3 to 3.3m that we associate with one of the eighteenth century earthquakes. The size of the small offsets implies that the historical earthquake was larger than M w 7.0, but probably smaller than M w 7.6. We found no significant surface rupture associated with the 2010 earthquake. The lack of surface rupture, coupled with other seismologic, geologic and geodetic observations, suggests that little, if any, accumulated strain was released on the Enriquillog-Plantain Garden fault in the 2010 earthquake. These results confirm that the Enriquillog-Plantain Garden fault remains a significant seismic hazard. ?? 2010 Macmillan Publishers Limited. All rights reserved.

  11. Shallow to intermediate resistivity features of the Colfiorito Fault System inferred by DC and MT survey

    A. Siniscalchi

    2008-06-01

    Full Text Available Over the last decade electromagnetic (EM measurements have provided new constraints on the upper-crustal structure of the major fault zones in the world, both when they act as conduit and as a barrier, due to strong sensitivity of resistivity to fluids circulation and mineralization. On the track of a high impact magnetotelluric (MT study performed across the San Andreas Fault, high resolution EM data were collected in the Colfiorito epicentral area along profiles crossing some main fault lineaments. Being the study focussed both on shallow that on intermediate resistivity distribution in the brittle upper-crust, a MT profile was integrated by several electrical resistivity tomographies (ERT. The latter were successful in locating faults even where the structures are buried by a wide covering of Quaternary deposits and in the recognition of different electrical signatures of the faults. MT resistivity model crossing Mt. Prefoglio normal fault clearly imaged the typical thrust structures of the area and a high conductive zone spatially related to the fault. Seismicity seems to be located outside such conductive area, whose behaviour suggests a fluidised and altered zone incapable of supporting significant stress internally.

  12. BEAT: A Web-Based Boolean Expression Fault-Based Test Case Generation Tool

    Chen, T. Y.; Grant, D. D.; Lau, M. F.; Ng, S. P.; Vasa, V. R.

    2006-01-01

    BEAT is a Web-based system that generates fault-based test cases from Boolean expressions. It is based on the integration of our several fault-based test case selection strategies. The generated test cases are considered to be fault-based, because they are aiming at the detection of particular faults. For example, when the Boolean expression is in…

  13. Reliability of Coulomb stress changes inferred from correlated uncertainties of finite-fault source models

    Woessner, J.; Jonsson, Sigurjon; Sudhaus, H.; Baumann, C.

    2012-01-01

    Static stress transfer is one physical mechanism to explain triggered seismicity. Coseismic stress-change calculations strongly depend on the parameterization of the causative finite-fault source model. These models are uncertain due

  14. Wavelet Packet based Detection of Surface Faults on Compact Discs

    Odgaard, Peter Fogh; Stoustrup, Jakob; Wickerhauser, Mladen Victor

    2006-01-01

    based on these measurements. A precise detection of the surface fault is a prerequisite to a correct handling of the faults in order to protect the pick-up of the compact disc player from audible track losses. The actual fault handling which is addressed in other publications can be carried out......In this paper the detection of faults on the surface of a compact disc is addressed. Surface faults like scratches and fingerprints disturb the on-line measurement of the pick-up position relative to the track. This is critical since the pick-up is focused on and tracked at the information track...

  15. Exact, almost and delayed fault detection: An observer based approach

    Niemann, Hans Henrik; Saberi, Ali; Stoorvogel, Anton A.

    1999-01-01

    This paper consider the problem of fault detection and isolation in continuous- and discrete-time systems while using zero or almost zero threshold. A number of different fault detections and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability...... conditions are given for the formulated design problems together with methods for appropriate design of observer based fault detectors. The l-step delayed fault detection problem is also considered for discrete-time systems . Moreover, certain indirect fault detection methods such as unknown input observers...

  16. Generative Inferences Based on Learned Relations

    Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

    2017-01-01

    A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

  17. Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

    Moshen Kuai

    2018-03-01

    Full Text Available For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN Adaptive Neuro-fuzzy Inference System (ANFIS in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

  18. Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS.

    Kuai, Moshen; Cheng, Gang; Pang, Yusong; Li, Yong

    2018-03-05

    For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

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

    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....

  20. Inference-based procedural modeling of solids

    Biggers, Keith

    2011-11-01

    As virtual environments become larger and more complex, there is an increasing need for more automated construction algorithms to support the development process. We present an approach for modeling solids by combining prior examples with a simple sketch. Our algorithm uses an inference-based approach to incrementally fit patches together in a consistent fashion to define the boundary of an object. This algorithm samples and extracts surface patches from input models, and develops a Petri net structure that describes the relationship between patches along an imposed parameterization. Then, given a new parameterized line or curve, we use the Petri net to logically fit patches together in a manner consistent with the input model. This allows us to easily construct objects of varying sizes and configurations using arbitrary articulation, repetition, and interchanging of parts. The result of our process is a solid model representation of the constructed object that can be integrated into a simulation-based environment. © 2011 Elsevier Ltd. All rights reserved.

  1. Inferring fault rheology from low-frequency earthquakes on the San Andreas

    Beeler, Nicholas M.; Thomas, Amanda; Bürgmann, Roland; Shelly, David R.

    2013-01-01

    Families of recurring low-frequency earthquakes (LFEs) within nonvolcanic tremor (NVT) on the San Andreas fault in central California show strong sensitivity to shear stress induced by the daily tidal cycle. LFEs occur at all levels of the tidal shear stress and are in phase with the very small, ~400 Pa, stress amplitude. To quantitatively explain the correlation, we use a model from the existing literature that assumes the LFE sources are small, persistent regions that repeatedly fail during shear of a much larger scale, otherwise aseismically creeping fault zone. The LFE source patches see tectonic loading, creep of the surrounding fault which may be modulated by the tidal stress, and direct tidal loading. If the patches are small relative to the surrounding creeping fault then the stressing is dominated by fault creep, and if patch failure occurs at a threshold stress, then the resulting seismicity rate is proportional to the fault creep rate or fault zone strain rate. Using the seismicity rate as a proxy for strain rate and the tidal shear stress, we fit the data with possible fault rheologies that produce creep in laboratory experiments at temperatures of 400 to 600°C appropriate for the LFE source depth. The rheological properties of rock-forming minerals for dislocation creep and dislocation glide are not consistent with the observed fault creep because strong correlation between small stress perturbations and strain rate requires perturbation on the order of the ambient stress. The observed tidal modulation restricts ambient stress to be at most a few kilopascal, much lower than rock strength. A purely rate dependent friction is consistent with the observations only if the product of the friction rate dependence and effective normal stress is ~ 0.5 kPa. Extrapolating the friction rate strengthening dependence of phyllosilicates (talc) to depth would require the effective normal stress to be ~50 kPa, implying pore pressure is lithostatic. If the LFE

  2. Fault diagnosis based on controller modification

    Niemann, Hans Henrik

    2015-01-01

    Detection and isolation of parametric faults in closed-loop systems will be considered in this paper. A major problem is that a feedback controller will in general reduce the effects from variations in the systems including parametric faults on the controlled output from the system. Parametric...... faults can be detected and isolated using active methods, where an auxiliary input is applied. Using active methods for the diagnosis of parametric faults in closed-loop systems, the amplitude of the applied auxiliary input need to be increased to be able to detect and isolate the faults in a reasonable......-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify the feedback controller with a minor effect on the closed-loop performance in the fault-free case and at the same time optimize the detection and isolation in a faulty case. Controller modification in connection...

  3. Reliability of Coulomb stress changes inferred from correlated uncertainties of finite-fault source models

    Woessner, J.

    2012-07-14

    Static stress transfer is one physical mechanism to explain triggered seismicity. Coseismic stress-change calculations strongly depend on the parameterization of the causative finite-fault source model. These models are uncertain due to uncertainties in input data, model assumptions, and modeling procedures. However, fault model uncertainties have usually been ignored in stress-triggering studies and have not been propagated to assess the reliability of Coulomb failure stress change (ΔCFS) calculations. We show how these uncertainties can be used to provide confidence intervals for co-seismic ΔCFS-values. We demonstrate this for the MW = 5.9 June 2000 Kleifarvatn earthquake in southwest Iceland and systematically map these uncertainties. A set of 2500 candidate source models from the full posterior fault-parameter distribution was used to compute 2500 ΔCFS maps. We assess the reliability of the ΔCFS-values from the coefficient of variation (CV) and deem ΔCFS-values to be reliable where they are at least twice as large as the standard deviation (CV ≤ 0.5). Unreliable ΔCFS-values are found near the causative fault and between lobes of positive and negative stress change, where a small change in fault strike causes ΔCFS-values to change sign. The most reliable ΔCFS-values are found away from the source fault in the middle of positive and negative ΔCFS-lobes, a likely general pattern. Using the reliability criterion, our results support the static stress-triggering hypothesis. Nevertheless, our analysis also suggests that results from previous stress-triggering studies not considering source model uncertainties may have lead to a biased interpretation of the importance of static stress-triggering.

  4. Model-based fault diagnosis in PEM fuel cell systems

    Escobet, T; de Lira, S; Puig, V; Quevedo, J [Automatic Control Department (ESAII), Universitat Politecnica de Catalunya (UPC), Rambla Sant Nebridi 10, 08222 Terrassa (Spain); Feroldi, D; Riera, J; Serra, M [Institut de Robotica i Informatica Industrial (IRI), Consejo Superior de Investigaciones Cientificas (CSIC), Universitat Politecnica de Catalunya (UPC) Parc Tecnologic de Barcelona, Edifici U, Carrer Llorens i Artigas, 4-6, Planta 2, 08028 Barcelona (Spain)

    2009-07-01

    In this work, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults. (author)

  5. Multi-link faults localization and restoration based on fuzzy fault set for dynamic optical networks.

    Zhao, Yongli; Li, Xin; Li, Huadong; Wang, Xinbo; Zhang, Jie; Huang, Shanguo

    2013-01-28

    Based on a distributed method of bit-error-rate (BER) monitoring, a novel multi-link faults restoration algorithm is proposed for dynamic optical networks. The concept of fuzzy fault set (FFS) is first introduced for multi-link faults localization, which includes all possible optical equipment or fiber links with a membership describing the possibility of faults. Such a set is characterized by a membership function which assigns each object a grade of membership ranging from zero to one. OSPF protocol extension is designed for the BER information flooding in the network. The BER information can be correlated to link faults through FFS. Based on the BER information and FFS, multi-link faults localization mechanism and restoration algorithm are implemented and experimentally demonstrated on a GMPLS enabled optical network testbed with 40 wavelengths in each fiber link. Experimental results show that the novel localization mechanism has better performance compared with the extended limited perimeter vector matching (LVM) protocol and the restoration algorithm can improve the restoration success rate under multi-link faults scenario.

  6. A Model-Based Probabilistic Inversion Framework for Wire Fault Detection Using TDR

    Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

    2010-01-01

    Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable hand-held devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S-parameter based forward modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

  7. Low Velocity Zones along the San Jacinto Fault, Southern California, inferred from Local Earthquakes

    Li, Z.; Yang, H.; Peng, Z.; Ben-Zion, Y.; Vernon, F.

    2013-12-01

    Natural fault zones have regions of brittle damage leading to a low-velocity zone (LVZ) in the immediate vicinity of the main fault interface. The LVZ may amplify ground motion, modify rupture propagation, and impact derivation of earthquke properties. Here we image low-velocity fault zone structures along the San Jacinto Fault (SJF), southern California, using waveforms of local earthquakes that are recorded at several dense arrays across the SJFZ. We use generalized ray theory to compute synthetic travel times to track the direct and FZ-reflected waves bouncing from the FZ boundaries. This method can effectively reduce the trade-off between FZ width and velocity reduction relative to the host rock. Our preliminary results from travel time modeling show the clear signature of LVZs along the SJF, including the segment of the Anza seismic gap. At the southern part near the trifrication area, the LVZ of the Clark Valley branch (array JF) has a width of ~200 m with ~55% reduction in Vp and Vs. This is consistent with what have been suggested from previous studies. In comparison, we find that the velocity reduction relative to the host rock across the Anza seismic gap (array RA) is ~50% for both Vp and Vs, nearly as prominent as that on the southern branches. The width of the LVZ is ~230 m. In addition, the LVZ across the Anza gap appears to locate in the northeast side of the RA array, implying potential preferred propagation direction of past ruptures.

  8. Diagnosis of rotor fault using neuro-fuzzy inference system | Merabet ...

    The three-phase induction machines (IM) is large importance and are being widely used as electromechanical system device regarding for their robustness, reliability, and simple design with well developed technologies. This work presents a reliable method for diagnosis and detection of rotor broken bars faults in induction ...

  9. Vertical slip rates of active faults of southern Albania inferred from river terraces

    Oswaldo Guzmán

    2014-02-01

    Full Text Available Fluvial terraces of Shkumbin, Devoll, Osum and Vjosa rivers (southern Albania and northwestern Greece are studied in order to quantify the vertical slip rates of the large active faults of the Dinaric-Albanic-Hellenic Alpine fold belt. The spatial and temporal variations of the incision rates along these rivers were estimated from the geomorphological mapping of the Quaternary sediments, the geometry and the dating of the terraces. Eleven terraces levels were identified in Albania from 68 geochronological ages already published or acquired for this work. The five lower terraces of the four studied rivers are well dated (10 new and 23 already published ages. These terraces are younger than 30 ka and their remnants are numerous. Their restoration allows estimating the regional trend of incision rate and the identification of local shifts. We argue that these shifts are linked to the active tectonics when they coincide with the faults already mapped by previous authors. Vertical slip rates for eight active faults in southern Albania are thus estimated for the last 19 ka and vary from ~0.1 to ~2 mm/a. The Lushnje Tepelene Thrust, that extends more than 120 kilometers, has a throw rate that varies from 0.2 to 0.8 mm/a, whereas the active faults of the extensional domain are segmented but are very active, with throw rates reaching locally 2 mm/a.

  10. Late Quaternary activity along the Scorciabuoi Fault (Southern Italy as inferred from electrical resistivity tomographies

    A. Loperte

    2007-06-01

    Full Text Available The Scorciabuoi Fault is one of the major tectonic structures affecting the Southern Apennines, Italy. Across its central sector, we performed several electrical resistivity tomographies with different electrode spacing (5 and 10 m and using a multielectrode system with 32 electrodes. All tomographies were acquired with two different arrays, the dipole-dipole and the Wenner-Schlumberger. We also tested the different sensitivity of the two arrays with respect to the specific geological conditions and research goals. Detailed geological mapping and two boreholes were used to calibrate the electrical stratigraphy. In all but one tomography (purposely performed off the fault trace, we could recognise an abrupt subvertical lateral variation of the main sedimentary bodies showing the displacement and sharp thickening of the two youngest alluvial bodies in the hanging-wall block. These features are interpreted as evidence of synsedimentary activity of the Scorciabuoi Fault during Late Pleistocene and possibly as recently as Holocene and allow accurate location of the fault trace within the Sauro alluvial plain.

  11. Bond graph model-based fault diagnosis of hybrid systems

    Borutzky, Wolfgang

    2015-01-01

    This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

  12. Using surface creep rate to infer fraction locked for sections of the San Andreas fault system in northern California from alignment array and GPS data

    Lienkaemper, James J.; McFarland, Forrest S.; Simpson, Robert W.; Caskey, S. John

    2014-01-01

    Surface creep rate, observed along five branches of the dextral San Andreas fault system in northern California, varies considerably from one section to the next, indicating that so too may the depth at which the faults are locked. We model locking on 29 fault sections using each section’s mean long‐term creep rate and the consensus values of fault width and geologic slip rate. Surface creep rate observations from 111 short‐range alignment and trilateration arrays and 48 near‐fault, Global Positioning System station pairs are used to estimate depth of creep, assuming an elastic half‐space model and adjusting depth of creep iteratively by trial and error to match the creep observations along fault sections. Fault sections are delineated either by geometric discontinuities between them or by distinctly different creeping behaviors. We remove transient rate changes associated with five large (M≥5.5) regional earthquakes. Estimates of fraction locked, the ratio of moment accumulation rate to loading rate, on each section of the fault system provide a uniform means to inform source parameters relevant to seismic‐hazard assessment. From its mean creep rates, we infer the main branch (the San Andreas fault) ranges from only 20%±10% locked on its central creeping section to 99%–100% on the north coast. From mean accumulation rates, we infer that four urban faults appear to have accumulated enough seismic moment to produce major earthquakes: the northern Calaveras (M 6.8), Hayward (M 6.8), Rodgers Creek (M 7.1), and Green Valley (M 7.1). The latter three faults are nearing or past their mean recurrence interval.

  13. Algorithmic fault tree construction by component-based system modeling

    Majdara, Aref; Wakabayashi, Toshio

    2008-01-01

    Computer-aided fault tree generation can be easier, faster and less vulnerable to errors than the conventional manual fault tree construction. In this paper, a new approach for algorithmic fault tree generation is presented. The method mainly consists of a component-based system modeling procedure an a trace-back algorithm for fault tree synthesis. Components, as the building blocks of systems, are modeled using function tables and state transition tables. The proposed method can be used for a wide range of systems with various kinds of components, if an inclusive component database is developed. (author)

  14. Ontology-Based Method for Fault Diagnosis of Loaders.

    Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

    2018-02-28

    This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

  15. Refinements on the inferred causative faults of the great 2012 Indian Ocean earthquakes

    Revathy, P. M.; Rajendran, K.

    2014-12-01

    As the largest known intra-plate strike-slip events, the pair of 2012 earthquakes in the Wharton Basin is a rarity. Separated in time by 2 hours these events rouse interest also because of their short inter-event duration, complex rupture mechanism, and spatial-temporal proximity to the great 2004 Sumatra plate boundary earthquake. Reactivation of fossil ridge-transform pairs is a favoured mechanism for large oceanic plate earthquakes and their inherent geometry triggers earthquakes on conjugate fault systems, as observed previously in the Wharton Basin. The current debate is whether the ruptures occurred on the WNW-ESE paleo ridges or the NNE-SSW paleo transforms. Back-projection models give a complex rupture pattern that favours the WNW-ESE fault [1]. However, the static stress changes due to the 2004 Sumatra earthquake and 2005 Nias earthquake favour the N15°E fault [2]. We use the Teleseismic Body-Wave Inversion Program [3] and waveform data from Global Seismic Network, to obtain the best fit solutions using P and S-wave synthetic modelling. The preliminary P-wave analysis of both earthquakes gives source parameters that are consistent with the Harvard CMT solutions. The obtained slip distribution complies with the NNE-SSW transforms. Both these earthquakes triggered small tsunamis which appear as two distinctive pulses on 13 Indian Ocean tide gauges and buoys. Frequency spectra of the tsunami recordings from various azimuths provide additional constraint for the choice of the causative faults. References: [1] Yue, H., T. Lay, and K. D. Koper (2012), En echelon and orthogonal fault ruptures of the 11 April 2012 great intraplate earthquakes, Nature, 490, 245-249, doi:10.1038/nature11492 [2] Delescluse, M., N. Chamot-Rooke, R. Cattin, L. Fleitout, O. Trubienko and C. Vigny April 2012 intra-oceanic seismicity off Sumatra boosted by the Banda-Aceh megathrust, Nature, 490(2012), pp. 240-244, doi:10.1038/nature11520 [3] M. Kikuchi and H. Kanamori, Note on

  16. Model Based Fault Detection in a Centrifugal Pump Application

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  17. Active Fault Detection Based on a Statistical Test

    Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2016-01-01

    In this paper active fault detection of closed loop systems using dual Youla-Jabr-Bongiorno-Kucera(YJBK) parameters is presented. Until now all detector design for active fault detection using the dual YJBK parameters has been based on CUSUM detectors. Here a method for design of a matched filter...

  18. Late Pleistocene-Holocene Activity of the Strike-slip Xianshuihe Fault Zone, Tibetan Plateau, Inferred from Tectonic Landforms

    Lin, A.; Yan, B.

    2017-12-01

    Knowledges on the activity of the strike-slip fault zones on the Tibetan Plateau have been promoted greatly by the interpretation of remote sensing images (Molnar and Tapponnier, 1975; Tapponnier and Molnar, 1977). The active strike-slip Xianshuihe-Xiaojiang Fault System (XXFS), with the geometry of an arc projecting northeastwards, plays an important role in the crustal deformation of the Tibetan Plateau caused by the continental collision between the Indian and Eurasian plates. The Xianshuihe Fault Zone (XFZ) is located in the central segment of the XXFS and extends for 370 km, with a maximum sinistral offset of 60 km since 13‒5 Ma. In this study, we investigated the tectonic landforms and slip rate along the central segment of the left-lateral strike-slip XFZ. Field investigations and analysis of ttectonic landforms show that horizontal offset has been accumulated on the topographical markers of different scales that developed since the Last Glacial Maximum (LGM). The central segment of the XFZ is composed of three major faults: Yalahe, Selaha, and Zheduotang faults showing a right-stepping echelon pattern, that is characterized by systematical offset of drainages, alluvial fans and terrace risers with typical scissoring structures, indicating a structural feature of left-lateral strike-slip fault. Based on the offset glacial morphology and radiocarbon dating ages, we estimate the Late Pleistocene-Holocene slip rate to be 10 mm/yr for the central segment of the XFZ, which is consistent with that estimated from the GPS observations and geological evidence as reported previously. Across the central segment of the XFZ, the major Selaha and Zheduotang faults participate a slip rate of 5.8 mm/yr and 3.4 mm/yr, respectively. Detailed investigations of tectonic landforms are essential for the understanding the activity of active faults. Our findings suggest that the left-lateral slipping of the XFZ partitions the deformation of eastward extrusion and northeastward

  19. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    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.

  20. Contingency inferences driven by base rates: Valid by sampling

    Florian Kutzner

    2011-04-01

    Full Text Available Fiedler et al. (2009, reviewed evidence for the utilization of a contingency inference strategy termed pseudocontingencies (PCs. In PCs, the more frequent levels (and, by implication, the less frequent levels are assumed to be associated. PCs have been obtained using a wide range of task settings and dependent measures. Yet, the readiness with which decision makers rely on PCs is poorly understood. A computer simulation explored two potential sources of subjective validity of PCs. First, PCs are shown to perform above chance level when the task is to infer the sign of moderate to strong population contingencies from a sample of observations. Second, contingency inferences based on PCs and inferences based on cell frequencies are shown to partially agree across samples. Intriguingly, this criterion and convergent validity are by-products of random sampling error, highlighting the inductive nature of contingency inferences.

  1. Surrogate based approaches to parameter inference in ocean models

    Knio, Omar

    2016-01-06

    This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.

  2. Surrogate based approaches to parameter inference in ocean models

    Knio, Omar

    2016-01-01

    This talk discusses the inference of physical parameters using model surrogates. Attention is focused on the use of sampling schemes to build suitable representations of the dependence of the model response on uncertain input data. Non-intrusive spectral projections and regularized regressions are used for this purpose. A Bayesian inference formalism is then applied to update the uncertain inputs based on available measurements or observations. To perform the update, we consider two alternative approaches, based on the application of Markov Chain Monte Carlo methods or of adjoint-based optimization techniques. We outline the implementation of these techniques to infer dependence of wind drag, bottom drag, and internal mixing coefficients.

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

    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.

  4. Risk-based fault detection using Self-Organizing Map

    Yu, Hongyang; Khan, Faisal; Garaniya, Vikram

    2015-01-01

    The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system's operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system's states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault. - Highlights: • A new approach based on Self-Organizing Map is proposed to detect faults. • Integration of fault detection with risk assessment methodology. • Fault risk characterization into different levels to enable focused system monitoring

  5. Active fault diagnosis based on stochastic tests

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2008-01-01

    The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...

  6. Rule - based Fault Diagnosis Expert System for Wind Turbine

    Deng Xiao-Wen

    2017-01-01

    Full Text Available Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.

  7. Evaluation of Wind Farm Controller based Fault Detection and Isolation

    Odgaard, Peter Fogh; Shafiei, Seyed Ehsan

    2015-01-01

    detection and isolation and fault tolerant control has previously been proposed. Based on this model, and international competition on wind farm FDI was organized. The contributions were presented at the IFAC World Congress 2014. In this paper the top three contributions to this competition are shortly......In the process of lowering cost of energy of power generated by wind turbines, some focus has been drawn towards fault detection and isolation and as well as fault tolerant control of wind turbines with the purpose of increasing reliability and availability of the wind turbines. Most modern wind...

  8. Fuzzy delay model based fault simulator for crosstalk delay fault test ...

    In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design .... To find the quality of non-robust tests, a fuzzy delay ..... Dubois D and Prade H 1989 Processing Fuzzy temporal knowledge. IEEE Transactions ...

  9. Fuzzy delay model based fault simulator for crosstalk delay fault test ...

    In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design trends move towards nanometer technologies, more number of new parameters affects the delay of the component. Fuzzy delay models are ideal for modelling the uncertainty found in the design and manufacturing steps.

  10. Fault tolerant control with torque limitation based on fault mode for ten-phase permanent magnet synchronous motor

    Guo Hong

    2015-10-01

    Full Text Available This paper proposes a novel fault tolerant control with torque limitation based on the fault mode for the ten-phase permanent magnet synchronous motor (PMSM under various open-circuit and short-circuit fault conditions, which includes the optimal torque control and the torque limitation control based on the fault mode. The optimal torque control is adopted to guarantee the ripple-free electromagnetic torque operation for the ten-phase motor system under the post-fault condition. Furthermore, we systematically analyze the load capacity of the ten-phase motor system under different fault modes. And a torque limitation control approach based on the fault mode is proposed, which was not available earlier. This approach is able to ensure the safety operation of the faulted motor system in long operating time without causing the overheat fault. The simulation result confirms that the proposed fault tolerant control for the ten-phase motor system is able to guarantee the ripple-free electromagnetic torque and the safety operation in long operating time under the normal and fault conditions.

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

    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.

  12. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  13. Slip rates and spatially variable creep on faults of the northern San Andreas system inferred through Bayesian inversion of Global Positioning System data

    Murray, Jessica R.; Minson, Sarah E.; Svarc, Jerry L.

    2014-01-01

    Fault creep, depending on its rate and spatial extent, is thought to reduce earthquake hazard by releasing tectonic strain aseismically. We use Bayesian inversion and a newly expanded GPS data set to infer the deep slip rates below assigned locking depths on the San Andreas, Maacama, and Bartlett Springs Faults of Northern California and, for the latter two, the spatially variable interseismic creep rate above the locking depth. We estimate deep slip rates of 21.5 ± 0.5, 13.1 ± 0.8, and 7.5 ± 0.7 mm/yr below 16 km, 9 km, and 13 km on the San Andreas, Maacama, and Bartlett Springs Faults, respectively. We infer that on average the Bartlett Springs fault creeps from the Earth's surface to 13 km depth, and below 5 km the creep rate approaches the deep slip rate. This implies that microseismicity may extend below the locking depth; however, we cannot rule out the presence of locked patches in the seismogenic zone that could generate moderate earthquakes. Our estimated Maacama creep rate, while comparable to the inferred deep slip rate at the Earth's surface, decreases with depth, implying a slip deficit exists. The Maacama deep slip rate estimate, 13.1 mm/yr, exceeds long-term geologic slip rate estimates, perhaps due to distributed off-fault strain or the presence of multiple active fault strands. While our creep rate estimates are relatively insensitive to choice of model locking depth, insufficient independent information regarding locking depths is a source of epistemic uncertainty that impacts deep slip rate estimates.

  14. Protection algorithm for a wind turbine generator based on positive- and negative-sequence fault components

    Zheng, Tai-Ying; Cha, Seung-Tae; Crossley, Peter A.

    2011-01-01

    A protection relay for a wind turbine generator (WTG) based on positive- and negative-sequence fault components is proposed in the paper. The relay uses the magnitude of the positive-sequence component in the fault current to detect a fault on a parallel WTG, connected to the same power collection...... feeder, or a fault on an adjacent feeder; but for these faults, the relay remains stable and inoperative. A fault on the power collection feeder or a fault on the collection bus, both of which require an instantaneous tripping response, are distinguished from an inter-tie fault or a grid fault, which...... in the fault current is used to decide on either instantaneous or delayed operation. The operating performance of the relay is then verified using various fault scenarios modelled using EMTP-RV. The scenarios involve changes in the position and type of fault, and the faulted phases. Results confirm...

  15. Analysis of large fault trees based on functional decomposition

    Contini, Sergio; Matuzas, Vaidas

    2011-01-01

    With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis, a significant enhancement has been achieved with respect to previous approaches, both in terms of efficiency and accuracy of the overall outcome of the analysis. However, the exponential increase of the number of nodes with the complexity of the fault tree may prevent the construction of the BDD. In these cases, the only way to complete the analysis is to reduce the complexity of the BDD by applying the truncation technique, which nevertheless implies the problem of estimating the truncation error or upper and lower bounds of the top-event unavailability. This paper describes a new method to analyze large coherent fault trees which can be advantageously applied when the working memory is not sufficient to construct the BDD. It is based on the decomposition of the fault tree into simpler disjoint fault trees containing a lower number of variables. The analysis of each simple fault tree is performed by using all the computational resources. The results from the analysis of all simpler fault trees are re-combined to obtain the results for the original fault tree. Two decomposition methods are herewith described: the first aims at determining the minimal cut sets (MCS) and the upper and lower bounds of the top-event unavailability; the second can be applied to determine the exact value of the top-event unavailability. Potentialities, limitations and possible variations of these methods will be discussed with reference to the results of their application to some complex fault trees.

  16. Analysis of large fault trees based on functional decomposition

    Contini, Sergio, E-mail: sergio.contini@jrc.i [European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, 21020 Ispra (Italy); Matuzas, Vaidas [European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, 21020 Ispra (Italy)

    2011-03-15

    With the advent of the Binary Decision Diagrams (BDD) approach in fault tree analysis, a significant enhancement has been achieved with respect to previous approaches, both in terms of efficiency and accuracy of the overall outcome of the analysis. However, the exponential increase of the number of nodes with the complexity of the fault tree may prevent the construction of the BDD. In these cases, the only way to complete the analysis is to reduce the complexity of the BDD by applying the truncation technique, which nevertheless implies the problem of estimating the truncation error or upper and lower bounds of the top-event unavailability. This paper describes a new method to analyze large coherent fault trees which can be advantageously applied when the working memory is not sufficient to construct the BDD. It is based on the decomposition of the fault tree into simpler disjoint fault trees containing a lower number of variables. The analysis of each simple fault tree is performed by using all the computational resources. The results from the analysis of all simpler fault trees are re-combined to obtain the results for the original fault tree. Two decomposition methods are herewith described: the first aims at determining the minimal cut sets (MCS) and the upper and lower bounds of the top-event unavailability; the second can be applied to determine the exact value of the top-event unavailability. Potentialities, limitations and possible variations of these methods will be discussed with reference to the results of their application to some complex fault trees.

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

    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.

  18. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  19. A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

    Detong Kong

    2012-02-01

    Full Text Available Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

  20. Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

    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.

  1. Fault detection and reliability, knowledge based and other approaches

    Singh, M.G.; Hindi, K.S.; Tzafestas, S.G.

    1987-01-01

    These proceedings are split up into four major parts in order to reflect the most significant aspects of reliability and fault detection as viewed at present. The first part deals with knowledge-based systems and comprises eleven contributions from leading experts in the field. The emphasis here is primarily on the use of artificial intelligence, expert systems and other knowledge-based systems for fault detection and reliability. The second part is devoted to fault detection of technological systems and comprises thirteen contributions dealing with applications of fault detection techniques to various technological systems such as gas networks, electric power systems, nuclear reactors and assembly cells. The third part of the proceedings, which consists of seven contributions, treats robust, fault tolerant and intelligent controllers and covers methodological issues as well as several applications ranging from nuclear power plants to industrial robots to steel grinding. The fourth part treats fault tolerant digital techniques and comprises five contributions. Two papers, one on reactor noise analysis, the other on reactor control system design, are indexed separately. (author)

  2. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    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.

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

    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.

  4. Vehicle Fault Diagnose Based on Smart Sensor

    Zhining, Li; Peng, Wang; Jianmin, Mei; Jianwei, Li; Fei, Teng

    In the vehicle's traditional fault diagnose system, we usually use a computer system with a A/D card and with many sensors connected to it. The disadvantage of this system is that these sensor can hardly be shared with control system and other systems, there are too many connect lines and the electro magnetic compatibility(EMC) will be affected. In this paper, smart speed sensor, smart acoustic press sensor, smart oil press sensor, smart acceleration sensor and smart order tracking sensor were designed to solve this problem. With the CAN BUS these smart sensors, fault diagnose computer and other computer could be connected together to establish a network system which can monitor and control the vehicle's diesel and other system without any duplicate sensor. The hard and soft ware of the smart sensor system was introduced, the oil press, vibration and acoustic signal are resampled by constant angle increment to eliminate the influence of the rotate speed. After the resample, the signal in every working cycle could be averaged in angle domain and do other analysis like order spectrum.

  5. Late Cretaceous and Cenozoic dynamics of the Bohemian Massif inferred from the paleostress history of the Lusatian Fault Belt

    Coubal, Miroslav; Málek, Jiří; Adamovič, Jiří; Štěpančíková, Petra

    2015-01-01

    Roč. 87, July 1 (2015), s. 26-49 ISSN 0264-3707 R&D Projects: GA ČR GAP210/12/0573 Institutional support: RVO:67985831 ; RVO:67985891 Keywords : paleostress * fault kinematics * Lusatian Fault Belt * Elbe fault system * Bohemian Massif * Alpine foreland Subject RIV: DB - Geology ; Mineralogy Impact factor: 1.926, year: 2015

  6. Compression-based inference on graph data

    Bloem, P.; van den Bosch, A.; Heskes, T.; van Leeuwen, D.

    2013-01-01

    We investigate the use of compression-based learning on graph data. General purpose compressors operate on bitstrings or other sequential representations. A single graph can be represented sequentially in many ways, which may in uence the performance of sequential compressors. Using Normalized

  7. Statistical inference based on latent ability estimates

    Hoijtink, H.J.A.; Boomsma, A.

    The quality of approximations to first and second order moments (e.g., statistics like means, variances, regression coefficients) based on latent ability estimates is being discussed. The ability estimates are obtained using either the Rasch, oi the two-parameter logistic model. Straightforward use

  8. A New Acoustic Emission Sensor Based Gear Fault Detection Approach

    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.

  9. Individual based population inference using tagging data

    Pedersen, Martin Wæver; Thygesen, Uffe Høgsbro; Baktoft, Henrik

    A hierarchical framework for simultaneous analysis of multiple related individual datasets is presented. The approach is very similar to mixed effects modelling as known from statistical theory. The model used at the individual level is, in principle, irrelevant as long as a maximum likelihood...... estimate and its uncertainty (Hessian) can be computed. The individual model used in this text is a hidden Markov model. A simulation study concerning a two-dimensional biased random walk is examined to verify the consistency of the hierarchical estimation framework. In addition, a study based on acoustic...... telemetry data from pike illustrates how the framework can identify individuals that deviate from the remaining population....

  10. Access Control Based on Trail Inference

    ALBARELO, P. C.

    2015-06-01

    Full Text Available Professionals are constantly seeking qualification and consequently increasing their knowledge in their area of expertise. Thus, it is interesting to develop a computer system that knows its users and their work history. Using this information, even in the case of professional role change, the system could allow the renewed authorization for activities, based on previously authorized use. This article proposes a model for user access control that is embedded in a context-aware environment. The model applies the concept of trails to manage access control, recording activities usage in contexts and applying this history as a criterion to grant new accesses. Despite the fact that previous related research works consider contexts, none of them uses the concept of trails. Hence, the main contribution of this work is the use of a new access control criterion, namely, the history of previous accesses (trails. A prototype was implemented and applied in an evaluation based on scenarios. The results demonstrate the feasibility of the proposal, allowing for access control systems to use an alternative way to support access rights.

  11. Fuzzy model-based observers for fault detection in CSTR.

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Fault Diagnosis of Rolling Bearings Based on EWT and KDEC

    Mingtao Ge

    2017-12-01

    Full Text Available This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT and kernel density estimation classifier (KDEC, which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM classifier and back-propagation (BP neural network classifier.

  13. SPEEDY: An Eclipse-based IDE for invariant inference

    David R. Cok

    2014-04-01

    Full Text Available SPEEDY is an Eclipse-based IDE for exploring techniques that assist users in generating correct specifications, particularly including invariant inference algorithms and tools. It integrates with several back-end tools that propose invariants and will incorporate published algorithms for inferring object and loop invariants. Though the architecture is language-neutral, current SPEEDY targets C programs. Building and using SPEEDY has confirmed earlier experience demonstrating the importance of showing and editing specifications in the IDEs that developers customarily use, automating as much of the production and checking of specifications as possible, and showing counterexample information directly in the source code editing environment. As in previous work, automation of specification checking is provided by back-end SMT solvers. However, reducing the effort demanded of software developers using formal methods also requires a GUI design that guides users in writing, reviewing, and correcting specifications and automates specification inference.

  14. Likelihood-based inference for clustered line transect data

    Waagepetersen, Rasmus; Schweder, Tore

    2006-01-01

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  15. Likelihood-based inference for clustered line transect data

    Waagepetersen, Rasmus Plenge; Schweder, Tore

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  16. Likelihood based inference for partially observed renewal processes

    van Lieshout, Maria Nicolette Margaretha

    2016-01-01

    This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum

  17. Data-driven fault mechanics: Inferring fault hydro-mechanical properties from in situ observations of injection-induced aseismic slip

    Bhattacharya, P.; Viesca, R. C.

    2017-12-01

    In the absence of in situ field-scale observations of quantities such as fault slip, shear stress and pore pressure, observational constraints on models of fault slip have mostly been limited to laboratory and/or remote observations. Recent controlled fluid-injection experiments on well-instrumented faults fill this gap by simultaneously monitoring fault slip and pore pressure evolution in situ [Gugleilmi et al., 2015]. Such experiments can reveal interesting fault behavior, e.g., Gugleilmi et al. report fluid-activated aseismic slip followed only subsequently by the onset of micro-seismicity. We show that the Gugleilmi et al. dataset can be used to constrain the hydro-mechanical model parameters of a fluid-activated expanding shear rupture within a Bayesian framework. We assume that (1) pore-pressure diffuses radially outward (from the injection well) within a permeable pathway along the fault bounded by a narrow damage zone about the principal slip surface; (2) pore-pressure increase ativates slip on a pre-stressed planar fault due to reduction in frictional strength (expressed as a constant friction coefficient times the effective normal stress). Owing to efficient, parallel, numerical solutions to the axisymmetric fluid-diffusion and crack problems (under the imposed history of injection), we are able to jointly fit the observed history of pore-pressure and slip using an adaptive Monte Carlo technique. Our hydrological model provides an excellent fit to the pore-pressure data without requiring any statistically significant permeability enhancement due to the onset of slip. Further, for realistic elastic properties of the fault, the crack model fits both the onset of slip and its early time evolution reasonably well. However, our model requires unrealistic fault properties to fit the marked acceleration of slip observed later in the experiment (coinciding with the triggering of microseismicity). Therefore, besides producing meaningful and internally consistent

  18. MgB2-based superconductors for fault current limiters

    Sokolovsky, V.; Prikhna, T.; Meerovich, V.; Eisterer, M.; Goldacker, W.; Kozyrev, A.; Weber, H. W.; Shapovalov, A.; Sverdun, V.; Moshchil, V.

    2017-02-01

    A promising solution of the fault current problem in power systems is the application of fast-operating nonlinear superconducting fault current limiters (SFCLs) with the capability of rapidly increasing their impedance, and thus limiting high fault currents. We report the results of experiments with models of inductive (transformer type) SFCLs based on the ring-shaped bulk MgB2 prepared under high quasihydrostatic pressure (2 GPa) and by hot pressing technique (30 MPa). It was shown that the SFCLs meet the main requirements to fault current limiters: they possess low impedance in the nominal regime of the protected circuit and can fast increase their impedance limiting both the transient and the steady-state fault currents. The study of quenching currents of MgB2 rings (SFCL activation current) and AC losses in the rings shows that the quenching current density and critical current density determined from AC losses can be 10-20 times less than the critical current determined from the magnetization experiments.

  19. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    Kaijuan Yuan

    2016-01-01

    Full Text Available Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.

  20. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    Busbait, Monther I.

    2014-01-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum

  1. Model based Fault Detection and Isolation for Driving Motors of a Ground Vehicle

    Young-Joon Kim

    2016-04-01

    Full Text Available This paper proposes model based current sensor and position sensor fault detection and isolation algorithm for driving motor of In-wheel independent drive electric vehicle. From low level perspective, fault diagnosis conducted and analyzed to enhance robustness and stability. Composing state equation of interior permanent magnet synchronous motor (IPMSM, current sensor fault and position sensor fault diagnosed with parity equation. Validation and usefulness of algorithm confirmed based on IPMSM fault occurrence simulation data.

  2. Gear Fault Detection Based on Teager-Huang Transform

    Hui Li

    2010-01-01

    Full Text Available Gear fault detection based on Empirical Mode Decomposition (EMD and Teager Kaiser Energy Operator (TKEO technique is presented. This novel method is named as Teager-Huang transform (THT. EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs. TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. The experimental results provide effective evidence that Teager-Huang transform has better resolution than that of Hilbert-Huang transform. The Teager-Huang transform can effectively diagnose the fault of the gear, thus providing a viable processing tool for gearbox defect detection and diagnosis.

  3. Bayes-Based Fault Discrimination in Wide Area Backup Protection

    WANG, Z.

    2012-02-01

    Full Text Available Multivariate statistical analysis is an effective tool to finish the fault location for electric power system. In Bayesian discriminant analysis as a subbranch, by the research of several populations, one can calculate the conditional probability that some samples belong to these populations, and compare the corresponding probability. The sample will be classified as population with maximum probability. In this paper, based on Bayesian discriminant analysis principle, a great number of simulation examples have confirmed that the results of Bayesian fault discriminant in wide area backup protection are accurate and reliable.

  4. Improvement of testing and maintenance based on fault tree analysis

    Cepin, M.

    2000-01-01

    Testing and maintenance of safety equipment is an important issue, which significantly contributes to safe and efficient operation of a nuclear power plant. In this paper a method, which extends the classical fault tree with time, is presented. Its mathematical model is represented by a set of equations, which include time requirements defined in the house event matrix. House events matrix is a representation of house events switched on and off through the discrete points of time. It includes house events, which timely switch on and off parts of the fault tree in accordance with the status of the plant configuration. Time dependent top event probability is calculated by the fault tree evaluations. Arrangement of components outages is determined on base of minimization of mean system unavailability. The results show that application of the method may improve the time placement of testing and maintenance activities of safety equipment. (author)

  5. Dynamics of Earthquake Faulting in Subduction Zones: Inference from Pseudotachylytes and Ultracataclasites in an Ancient Accretionary Complex

    K. Ujiie

    2007-11-01

    Full Text Available The fault rocks in ancient accretionary complexes exhumed from seismogenic depths may provide an invaluable opportunity to examine the mechanisms and mechanics of seismic slip in subduction thrusts and splay faults. In order to understand the dynamics of earthquake faulting in subduction zones, we analyzed pseudotachylytes and ultracataclasites from the Shimanto accretionary complex in southwest Japan. doi:10.2204/iodp.sd.s01.21.2007

  6. Functional networks inference from rule-based machine learning models.

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

    2016-01-01

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  7. Note on fault-slip motion inferred from a study of micro-cataclastic particles from an underground shear rupture

    Ortlepp, W. D.

    1992-09-01

    Mining of a highly-stressed remnant in a deep South African gold mine was accompanied by considerable seismic activity and some significant rockbursts. The larger seismic events were registered some 60 km away at a WSSN station and several shear ruptures corresponding to these events were encountered during mining operations. A careful study based on detailed exploration of two of these ruptures proved them to be the source of two of the larger rockbursts. Certain striking features revealed by a scanning electron microscopic study of some of the fresh cataclastic ‘rock-flour’ forming part of the comminuted filling of these ruptures provide strong evidence of violent ’shock rebound’ phenomena in the faulting process. This interpretation could provide useful insight into earthquake source mechanisms and also has practical significance in the understanding of mine rockbursts.

  8. Memory-Based Simple Heuristics as Attribute Substitution: Competitive Tests of Binary Choice Inference Models

    Honda, Hidehito; Matsuka, Toshihiko; Ueda, Kazuhiro

    2017-01-01

    Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in…

  9. Strike-slip tectonics and Quaternary basin formation along the Vienna Basin fault system inferred from Bouguer gravity derivatives

    Salcher, B. C.; Meurers, B.; Smit, J.; Decker, K.; HöLzel, M.; Wagreich, M.

    2012-01-01

    The Vienna Basin at the transition between the Alpine and Carpathian belt hosts a number of large Pleistocene sub-basins forming along an active continental scale strike-slip fault (Vienna Basin strike-slip fault). We utilize first-order derivatives from industrial Bouguer gravity data to unravel

  10. Observer Based Fault Detection and Moisture Estimating in Coal Mill

    Odgaard, Peter Fogh; Mataji, Babak

    2008-01-01

    In this paper an observer-based method for detecting faults and estimating moisture content in the coal in coal mills is presented. Handling of faults and operation under special conditions, such as high moisture content in the coal, are of growing importance due to the increasing...... requirements to the general performance of power plants. Detection  of faults and moisture content estimation are consequently of high interest in the handling of the problems caused by faults and moisture content. The coal flow out of the mill is the obvious variable to monitor, when detecting non-intended drops in the coal...... flow out of the coal mill. However, this variable is not measurable. Another estimated variable is the moisture content, which is only "measurable" during steady-state operations of the coal mill. Instead, this paper suggests a method where these unknown variables are estimated based on a simple energy...

  11. Distributed bearing fault diagnosis based on vibration analysis

    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.

  12. SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

    Golafshan, Reza; Yuce Sanliturk, Kenan

    2016-03-01

    Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

  13. FSN-based fault modelling for fault detection and troubleshooting in CANDU stations

    Nasimi, E., E-mail: elnara.nasimi@brucepower.com [Bruce Power LLP., Tiverton, Ontario(Canada); Gabbar, H.A. [Univ. of Ontario Inst. of Tech., Oshawa, Ontario (Canada)

    2013-07-01

    An accurate fault modeling and troubleshooting methodology is required to aid in making risk-informed decisions related to design and operational activities of current and future generation of CANDU designs. This paper presents fault modeling approach using Fault Semantic Network (FSN) methodology with risk estimation. Its application is demonstrated using a case study of Bruce B zone-control level oscillations. (author)

  14. Efficient fault-ride-through control strategy of DFIG-based wind turbines during the grid faults

    Mohammadi, J.; Afsharnia, S.; Vaez-Zadeh, S.

    2014-01-01

    Highlights: • A comparative review of DFIGs fault-ride-through improvement approaches is presented. • An efficient control strategy is proposed to improve the FRT capability of DFIG. • The rotor overcurrent, DC-link overvoltage and torque oscillations are decreased. • The RSC, DC-link capacitor and mechanical parts are kept safe during the grid faults. • The DFIG remains connected to the grid during the symmetrical and asymmetrical faults. - Abstract: As the penetration of wind power in electrical power system increases, it is necessary that wind turbines remain connected to the grid and contribute to the system stability during and after the grid faults. This paper proposes an efficient control strategy to improve the fault ride through (FRT) capability of doubly fed induction generator (DFIG) during the symmetrical and asymmetrical grid faults. The proposed scheme consists of active and passive FRT compensators. The active compensator is carried out by determining the rotor current references to reduce the rotor over voltages. The passive compensator is based on rotor current limiter (RCL) that considerably reduces the rotor inrush currents at the instants of occurring and clearing the grid faults with deep sags. By applying the proposed strategy, negative effects of the grid faults in the DFIG system including the rotor over currents, electromagnetic torque oscillations and DC-link over voltage are decreased. The system simulation results confirm the effectiveness of the proposed control strategy

  15. Model-Based Methods for Fault Diagnosis: Some Guide-Lines

    Patton, R.J.; Chen, J.; Nielsen, S.B.

    1995-01-01

    This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....

  16. Bootstrap-Based Inference for Cube Root Consistent Estimators

    Cattaneo, Matias D.; Jansson, Michael; Nagasawa, Kenichi

    This note proposes a consistent bootstrap-based distributional approximation for cube root consistent estimators such as the maximum score estimator of Manski (1975) and the isotonic density estimator of Grenander (1956). In both cases, the standard nonparametric bootstrap is known...... to be inconsistent. Our method restores consistency of the nonparametric bootstrap by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy-to-implement resampling method for inference that is conceptually distinct...... from other available distributional approximations based on some form of modified bootstrap. We offer simulation evidence showcasing the performance of our inference method in finite samples. An extension of our methodology to general M-estimation problems is also discussed....

  17. Bootstrap-based Support of HGT Inferred by Maximum Parsimony

    Nakhleh Luay

    2010-05-01

    Full Text Available Abstract Background Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of phylogenetic networks, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An ad hoc solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold. Results In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples. Conclusions We have implemented our method in the NEPAL software tool (available publicly at http://bioinfo.cs.rice.edu/, and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.

  18. Bootstrap-based support of HGT inferred by maximum parsimony.

    Park, Hyun Jung; Jin, Guohua; Nakhleh, Luay

    2010-05-05

    Maximum parsimony is one of the most commonly used criteria for reconstructing phylogenetic trees. Recently, Nakhleh and co-workers extended this criterion to enable reconstruction of phylogenetic networks, and demonstrated its application to detecting reticulate evolutionary relationships. However, one of the major problems with this extension has been that it favors more complex evolutionary relationships over simpler ones, thus having the potential for overestimating the amount of reticulation in the data. An ad hoc solution to this problem that has been used entails inspecting the improvement in the parsimony length as more reticulation events are added to the model, and stopping when the improvement is below a certain threshold. In this paper, we address this problem in a more systematic way, by proposing a nonparametric bootstrap-based measure of support of inferred reticulation events, and using it to determine the number of those events, as well as their placements. A number of samples is generated from the given sequence alignment, and reticulation events are inferred based on each sample. Finally, the support of each reticulation event is quantified based on the inferences made over all samples. We have implemented our method in the NEPAL software tool (available publicly at http://bioinfo.cs.rice.edu/), and studied its performance on both biological and simulated data sets. While our studies show very promising results, they also highlight issues that are inherently challenging when applying the maximum parsimony criterion to detect reticulate evolution.

  19. Fault Diagnosis in Deaerator Using Fuzzy Logic

    S Srinivasan

    2007-01-01

    Full Text Available In this paper a fuzzy logic based fault diagnosis system for a deaerator in a power plant unit is presented. The system parameters are obtained using the linearised state space deaerator model. The fuzzy inference system is created and rule base are evaluated relating the parameters to the type and severity of the faults. These rules are fired for specific changes in system parameters and the faults are diagnosed.

  20. Bayesian inference for heterogeneous caprock permeability based on above zone pressure monitoring

    Namhata, Argha; Small, Mitchell J.; Dilmore, Robert M.; Nakles, David V.; King, Seth

    2017-02-01

    The presence of faults/ fractures or highly permeable zones in the primary sealing caprock of a CO2 storage reservoir can result in leakage of CO2. Monitoring of leakage requires the capability to detect and resolve the onset, location, and volume of leakage in a systematic and timely manner. Pressure-based monitoring possesses such capabilities. This study demonstrates a basis for monitoring network design based on the characterization of CO2 leakage scenarios through an assessment of the integrity and permeability of the caprock inferred from above zone pressure measurements. Four representative heterogeneous fractured seal types are characterized to demonstrate seal permeability ranging from highly permeable to impermeable. Based on Bayesian classification theory, the probability of each fractured caprock scenario given above zone pressure measurements with measurement error is inferred. The sensitivity to injection rate and caprock thickness is also evaluated and the probability of proper classification is calculated. The time required to distinguish between above zone pressure outcomes and the associated leakage scenarios is also computed.

  1. Algorithms for MDC-Based Multi-locus Phylogeny Inference

    Yu, Yun; Warnow, Tandy; Nakhleh, Luay

    One of the criteria for inferring a species tree from a collection of gene trees, when gene tree incongruence is assumed to be due to incomplete lineage sorting (ILS), is minimize deep coalescence, or MDC. Exact algorithms for inferring the species tree from rooted, binary trees under MDC were recently introduced. Nevertheless, in phylogenetic analyses of biological data sets, estimated gene trees may differ from true gene trees, be incompletely resolved, and not necessarily rooted. In this paper, we propose new MDC formulations for the cases where the gene trees are unrooted/binary, rooted/non-binary, and unrooted/non-binary. Further, we prove structural theorems that allow us to extend the algorithms for the rooted/binary gene tree case to these cases in a straightforward manner. Finally, we study the performance of these methods in coalescent-based computer simulations.

  2. EEG Based Inference of Spatio-Temporal Brain Dynamics

    Hansen, Sofie Therese

    Electroencephalography (EEG) provides a measure of brain activity and has improved our understanding of the brain immensely. However, there is still much to be learned and the full potential of EEG is yet to be realized. In this thesis we suggest to improve the information gain of EEG using three...... different approaches; 1) by recovery of the EEG sources, 2) by representing and inferring the propagation path of EEG sources, and 3) by combining EEG with functional magnetic resonance imaging (fMRI). The common goal of the methods, and thus of this thesis, is to improve the spatial dimension of EEG...... recovery ability. The forward problem describes the propagation of neuronal activity in the brain to the EEG electrodes on the scalp. The geometry and conductivity of the head layers are normally required to model this path. We propose a framework for inferring forward models which is based on the EEG...

  3. Likelihood-Based Inference of B Cell Clonal Families.

    Duncan K Ralph

    2016-10-01

    Full Text Available The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called "rearrangement" forming progenitor B cells, then a Darwinian process of lineage diversification and selection called "affinity maturation." The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem "clonal family inference." In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.

  4. Fault Analysis-based Logic Encryption (Preprint)

    2013-11-01

    publication of this paper. This material is based on work fund- ed by AFRL under contract No. FA8750-11-2-0274. Received and cleared for public release by...AFRL on November 19, 2012, case number 88ABW-2012-6072. Any opinions, findings and conclusions or recommendations expressed in this material are...those of the authors and do not necessarily reflect the views of AFRL or its contractors. 10 REFERENCES [1] KPMG . (2006) Managing the risks of

  5. Fault detection and fault tolerant control of a smart base isolation system with magneto-rheological damper

    Wang, Han; Song, Gangbing

    2011-01-01

    Fault detection and isolation (FDI) in real-time systems can provide early warnings for faulty sensors and actuator signals to prevent events that lead to catastrophic failures. The main objective of this paper is to develop FDI and fault tolerant control techniques for base isolation systems with magneto-rheological (MR) dampers. Thus, this paper presents a fixed-order FDI filter design procedure based on linear matrix inequalities (LMI). The necessary and sufficient conditions for the existence of a solution for detecting and isolating faults using the H ∞ formulation is provided in the proposed filter design. Furthermore, an FDI-filter-based fuzzy fault tolerant controller (FFTC) for a base isolation structure model was designed to preserve the pre-specified performance of the system in the presence of various unknown faults. Simulation and experimental results demonstrated that the designed filter can successfully detect and isolate faults from displacement sensors and accelerometers while maintaining excellent performance of the base isolation technology under faulty conditions

  6. Fault location in underground cables using ANFIS nets and discrete wavelet transform

    Shimaa Barakat

    2014-12-01

    Full Text Available This paper presents an accurate algorithm for locating faults in a medium voltage underground power cable using a combination of Adaptive Network-Based Fuzzy Inference System (ANFIS and discrete wavelet transform (DWT. The proposed method uses five ANFIS networks and consists of 2 stages, including fault type classification and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents. Other four ANFIS networks are utilized to pinpoint the faults (one for each fault type. Four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on the cable. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances.

  7. Generating inferences from knowledge structures based on general automata

    Koenig, E C

    1983-01-01

    The author shows that the model for knowledge structures for computers based on general automata accommodates procedures for establishing inferences. Algorithms are presented which generate inferences as output of a computer when its sentence input names appropriate knowledge elements contained in an associated knowledge structure already stored in the memory of the computer. The inferences are found to have either a single graph tuple or more than one graph tuple of associated knowledge. Six algorithms pertain to a single graph tuple and a seventh pertains to more than one graph tuple of associated knowledge. A named term is either the automaton, environment, auxiliary receptor, principal receptor, auxiliary effector, or principal effector. The algorithm pertaining to more than one graph tuple requires that the input sentence names the automaton, transformation response, and environment of one of the tuples of associated knowledge in a sequence of tuples. Interaction with the computer may be either in a conversation or examination mode. The algorithms are illustrated by an example. 13 references.

  8. Analytical Model-based Fault Detection and Isolation in Control Systems

    Vukic, Z.; Ozbolt, H.; Blanke, M.

    1998-01-01

    The paper gives an introduction and an overview of the field of fault detection and isolation for control systems. The summary of analytical (quantitative model-based) methodds and their implementation are presented. The focus is given to mthe analytical model-based fault-detection and fault...

  9. Fault Reconnaissance Agent for Sensor Networks

    Elhadi M. Shakshuki

    2010-01-01

    Full Text Available One of the key prerequisite for a scalable, effective and efficient sensor network is the utilization of low-cost, low-overhead and high-resilient fault-inference techniques. To this end, we propose an intelligent agent system with a problem solving capability to address the issue of fault inference in sensor network environments. The intelligent agent system is designed and implemented at base-station side. The core of the agent system – problem solver – implements a fault-detection inference engine which harnesses Expectation Maximization (EM algorithm to estimate fault probabilities of sensor nodes. To validate the correctness and effectiveness of the intelligent agent system, a set of experiments in a wireless sensor testbed are conducted. The experimental results show that our intelligent agent system is able to precisely estimate the fault probability of sensor nodes.

  10. A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels.

    Hu, Di; Sarosh, Ali; Dong, Yun-Feng

    2012-03-01

    Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Fault diagnosis of power transformer based on fault-tree analysis (FTA)

    Wang, Yongliang; Li, Xiaoqiang; Ma, Jianwei; Li, SuoYu

    2017-05-01

    Power transformers is an important equipment in power plants and substations, power distribution transmission link is made an important hub of power systems. Its performance directly affects the quality and health of the power system reliability and stability. This paper summarizes the five parts according to the fault type power transformers, then from the time dimension divided into three stages of power transformer fault, use DGA routine analysis and infrared diagnostics criterion set power transformer running state, finally, according to the needs of power transformer fault diagnosis, by the general to the section by stepwise refinement of dendritic tree constructed power transformer fault

  12. Nonlinear observer based fault detection and isolation for a momentum wheel

    Jensen, Hans-Christian Becker; Wisniewski, Rafal

    2001-01-01

    This article realizes nonlinear Fault Detection and Isolation for a momentum wheel. The Fault Detection and Isolation is based on a Failure Mode and Effect Analysis, which states which faults might occur and can be detected. The algorithms presented in this paper are based on a geometric approach...... toachieve nonlinear Fault Detection and Isolation. The proposed algorithms are tested in a simulation study and the pros and cons of the algorithm are discussed....

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

    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...

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

    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....

  15. Wilshire fault: Earthquakes in Hollywood?

    Hummon, Cheryl; Schneider, Craig L.; Yeats, Robert S.; Dolan, James F.; Sieh, Kerry E.; Huftile, Gary J.

    1994-04-01

    The Wilshire fault is a potentially seismogenic, blind thrust fault inferred to underlie and cause the Wilshire arch, a Quaternary fold in the Hollywood area, just west of downtown Los Angeles, California. Two inverse models, based on the Wilshire arch, allow us to estimate the location and slip rate of the Wilshire fault, which may be illuminated by a zone of microearthquakes. A fault-bend fold model indicates a reverse-slip rate of 1.5-1.9 mm/yr, whereas a three-dimensional elastic-dislocation model indicates a right-reverse slip rate of 2.6-3.2 mm/yr. The Wilshire fault is a previously unrecognized seismic hazard directly beneath Hollywood and Beverly Hills, distinct from the faults under the nearby Santa Monica Mountains.

  16. Inference-Based Surface Reconstruction of Cluttered Environments

    Biggers, K.

    2012-08-01

    We present an inference-based surface reconstruction algorithm that is capable of identifying objects of interest among a cluttered scene, and reconstructing solid model representations even in the presence of occluded surfaces. Our proposed approach incorporates a predictive modeling framework that uses a set of user-provided models for prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global construction process guided by rules for fitting high-quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several example data sets containing heavy clutter and occlusion. © 2012 IEEE.

  17. Fault diagnosis of air conditioning systems based on qualitative bond graph

    Ghiaus, C.

    1999-01-01

    The bond graph method represents a unified approach for modeling engineering systems. The main idea is that power transfer bonds the components of a system. The bond graph model is the same for both quantitative representation, in which parameters have numerical values, and qualitative approach, in which they are classified qualitatively. To infer the cause of faults using a qualitative method, a system of qualitative equations must be solved. However, the characteristics of qualitative operators require specific methods for solving systems of equations having qualitative variables. This paper proposes both a method for recursively solving the qualitative system of equations derived from bond graph, and a bond graph model of a direct-expansion, mechanical vapor-compression air conditioning system. Results from diagnosing two faults in a real air conditioning system are presented and discussed. Occasionally, more than one fault candidate is inferred for the same set of qualitative values derived from measurements. In these cases, additional information is required to localize the fault. Fault diagnosis is initiated by a fault detection mechanism which also classifies the quantitative measurements into qualitative values; the fault detection is not presented here. (author)

  18. Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

    Zhuqing Bi

    2017-01-01

    Full Text Available According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.

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

    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.

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

    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.

  1. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang

    2014-01-01

    The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324

  2. Fault Risk Assessment of Underwater Vehicle Steering System Based on Virtual Prototyping and Monte Carlo Simulation

    He Deyu

    2016-09-01

    Full Text Available Assessing the risks of steering system faults in underwater vehicles is a human-machine-environment (HME systematic safety field that studies faults in the steering system itself, the driver’s human reliability (HR and various environmental conditions. This paper proposed a fault risk assessment method for an underwater vehicle steering system based on virtual prototyping and Monte Carlo simulation. A virtual steering system prototype was established and validated to rectify a lack of historic fault data. Fault injection and simulation were conducted to acquire fault simulation data. A Monte Carlo simulation was adopted that integrated randomness due to the human operator and environment. Randomness and uncertainty of the human, machine and environment were integrated in the method to obtain a probabilistic risk indicator. To verify the proposed method, a case of stuck rudder fault (SRF risk assessment was studied. This method may provide a novel solution for fault risk assessment of a vehicle or other general HME system.

  3. EKF-based fault detection for guided missiles flight control system

    Feng, Gang; Yang, Zhiyong; Liu, Yongjin

    2017-03-01

    The guided missiles flight control system is essential for guidance accuracy and kill probability. It is complicated and fragile. Since actuator faults and sensor faults could seriously affect the security and reliability of the system, fault detection for missiles flight control system is of great significance. This paper deals with the problem of fault detection for the closed-loop nonlinear model of the guided missiles flight control system in the presence of disturbance. First, set up the fault model of flight control system, and then design the residual generation based on the extended Kalman filter (EKF) for the Eulerian-discrete fault model. After that, the Chi-square test was selected for the residual evaluation and the fault detention task for guided missiles closed-loop system was accomplished. Finally, simulation results are provided to illustrate the effectiveness of the approach proposed in the case of elevator fault separately.

  4. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    Hong Yin

    2014-01-01

    Full Text Available The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

  5. Track Circuit Fault Diagnosis Method based on Least Squares Support Vector

    Cao, Yan; Sun, Fengru

    2018-01-01

    In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.

  6. Product quality management based on CNC machine fault prognostics and diagnosis

    Kozlov, A. M.; Al-jonid, Kh M.; Kozlov, A. A.; Antar, Sh D.

    2018-03-01

    This paper presents a new fault classification model and an integrated approach to fault diagnosis which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, faults are categorized in two aspects, namely first and second degree faults. First degree faults are instantaneous in nature, and second degree faults are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of faults have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor’s equation. For fault diagnosis, this framework consists of two phases: the first one is focusing on fault prognosis, which is done online, and the second one is concerned with fault diagnosis which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or fault diagnosis based on the severity of a fault. The second phase only comes into action when an evolving fault goes beyond a critical threshold limit called a CBM limit for a command to be issued for fault diagnosis. During this phase, DBN and PF techniques are used as an intelligent fault diagnosis system to determine the severity, time and location of the fault. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.

  7. Model-based fault detection algorithm for photovoltaic system monitoring

    Harrou, Fouzi; Sun, Ying; Saidi, Ahmed

    2018-01-01

    Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a

  8. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    Harrou, Fouzi; Sun, Ying; Madakyaru, Muddu

    2017-01-01

    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

  9. The fault monitoring and diagnosis knowledge-based system for space power systems: AMPERES, phase 1

    Lee, S. C.

    1989-01-01

    The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.

  10. Observer Based Detection of Sensor Faults in Wind Turbines

    Odgaard, Peter Fogh; Stoustrup, Jakob; Nielsen, R.

    2009-01-01

    , if an unknown input observer the fault detection  scheme can be non dependent on the actual wind speed. The scheme  is validated on data from a more advanced and detailed simulation  model. The proposed scheme detects the sensor faults a few samples  after the beginning of the faults....

  11. Bayesian Inference for Signal-Based Seismic Monitoring

    Moore, D.

    2015-12-01

    Traditional seismic monitoring systems rely on discrete detections produced by station processing software, discarding significant information present in the original recorded signal. SIG-VISA (Signal-based Vertically Integrated Seismic Analysis) is a system for global seismic monitoring through Bayesian inference on seismic signals. By modeling signals directly, our forward model is able to incorporate a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. This allows inference in the model to recover the qualitative behavior of recent geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a global network of stations. We demonstrate recent progress in scaling up SIG-VISA to efficiently process the data stream of global signals recorded by the International Monitoring System (IMS), including comparisons against existing processing methods that show increased sensitivity from our signal-based model and in particular the ability to locate events (including aftershock sequences that can tax analyst processing) precisely from waveform correlation effects. We also provide a Bayesian analysis of an alleged low-magnitude event near the DPRK test site in May 2010 [1] [2], investigating whether such an event could plausibly be detected through automated processing in a signal-based monitoring system. [1] Zhang, Miao and Wen, Lianxing. "Seismological Evidence for a Low-Yield Nuclear Test on 12 May 2010 in North Korea". Seismological Research Letters, January/February 2015. [2] Richards, Paul. "A Seismic Event in North Korea on 12 May 2010". CTBTO SnT 2015 oral presentation, video at https://video-archive.ctbto.org/index.php/kmc/preview/partner_id/103/uiconf_id/4421629/entry_id/0_ymmtpps0/delivery/http

  12. Method of fault diagnosis in nuclear power plant base on genetic algorithm and knowledge base

    Zhou Yangping; Zhao Bingquan

    2000-01-01

    Via using the knowledge base, combining Genetic Algorithm and classical probability and contraposing the characteristic of the fault diagnosis of NPP. The authors put forward a method of fault diagnosis. In the process of fault diagnosis, this method contact the state of NPP with the colony in GA and transform the colony to get the individual that adapts to the condition. On the 950MW full size simulator in Beijing NPP simulation training center, experimentation shows it has comparative adaptability to the imperfection of expert knowledge, illusive signal and other instance

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

    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.

  14. A simulation training evaluation method for distribution network fault based on radar chart

    Yuhang Xu

    2018-01-01

    Full Text Available In order to solve the problem of automatic evaluation of dispatcher fault simulation training in distribution network, a simulation training evaluation method based on radar chart for distribution network fault is proposed. The fault handling information matrix is established to record the dispatcher fault handling operation sequence and operation information. The four situations of the dispatcher fault isolation operation are analyzed. The fault handling anti-misoperation rule set is established to describe the rules prohibiting dispatcher operation. Based on the idea of artificial intelligence reasoning, the feasibility of dispatcher fault handling is described by the feasibility index. The relevant factors and evaluation methods are discussed from the three aspects of the fault handling result feasibility, the anti-misoperation correctness and the operation process conciseness. The detailed calculation formula is given. Combining the independence and correlation between the three evaluation angles, a comprehensive evaluation method of distribution network fault simulation training based on radar chart is proposed. The method can comprehensively reflect the fault handling process of dispatchers, and comprehensively evaluate the fault handling process from various angles, which has good practical value.

  15. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  16. Adaptive neuro-fuzzy inference system based automatic generation control

    Hosseini, S.H.; Etemadi, A.H. [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran)

    2008-07-15

    Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified. (author)

  17. Fuzzy probability based fault tree analysis to propagate and quantify epistemic uncertainty

    Purba, Julwan Hendry; Sony Tjahyani, D.T.; Ekariansyah, Andi Sofrany; Tjahjono, Hendro

    2015-01-01

    Highlights: • Fuzzy probability based fault tree analysis is to evaluate epistemic uncertainty in fuzzy fault tree analysis. • Fuzzy probabilities represent likelihood occurrences of all events in a fault tree. • A fuzzy multiplication rule quantifies epistemic uncertainty of minimal cut sets. • A fuzzy complement rule estimate epistemic uncertainty of the top event. • The proposed FPFTA has successfully evaluated the U.S. Combustion Engineering RPS. - Abstract: A number of fuzzy fault tree analysis approaches, which integrate fuzzy concepts into the quantitative phase of conventional fault tree analysis, have been proposed to study reliabilities of engineering systems. Those new approaches apply expert judgments to overcome the limitation of the conventional fault tree analysis when basic events do not have probability distributions. Since expert judgments might come with epistemic uncertainty, it is important to quantify the overall uncertainties of the fuzzy fault tree analysis. Monte Carlo simulation is commonly used to quantify the overall uncertainties of conventional fault tree analysis. However, since Monte Carlo simulation is based on probability distribution, this technique is not appropriate for fuzzy fault tree analysis, which is based on fuzzy probabilities. The objective of this study is to develop a fuzzy probability based fault tree analysis to overcome the limitation of fuzzy fault tree analysis. To demonstrate the applicability of the proposed approach, a case study is performed and its results are then compared to the results analyzed by a conventional fault tree analysis. The results confirm that the proposed fuzzy probability based fault tree analysis is feasible to propagate and quantify epistemic uncertainties in fault tree analysis

  18. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    Busbait, Monther I.

    2014-05-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

  19. Diagnosis of soft faults in analog integrated circuits based on fractional correlation

    Deng Yong; Shi Yibing; Zhang Wei

    2012-01-01

    Aiming at the problem of diagnosing soft faults in analog integrated circuits, an approach based on fractional correlation is proposed. First, the Volterra series of the circuit under test (CUT) decomposed by the fractional wavelet packet are used to calculate the fractional correlation functions. Then, the calculated fractional correlation functions are used to form the fault signatures of the CUT. By comparing the fault signatures, the different soft faulty conditions of the CUT are identified and the faults are located. Simulations of benchmark circuits illustrate the proposed method and validate its effectiveness in diagnosing soft faults in analog integrated circuits. (semiconductor integrated circuits)

  20. Bearing Fault Classification Based on Conditional Random Field

    Guofeng Wang

    2013-01-01

    Full Text Available Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM and improve the diagnosis accuracy, conditional random field (CRF model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.

  1. A Cooperative Approach to Virtual Machine Based Fault Injection

    Naughton III, Thomas J [ORNL; Engelmann, Christian [ORNL; Vallee, Geoffroy R [ORNL; Aderholdt, William Ferrol [ORNL; Scott, Stephen L [Tennessee Technological University (TTU)

    2017-01-01

    Resilience investigations often employ fault injection (FI) tools to study the effects of simulated errors on a target system. It is important to keep the target system under test (SUT) isolated from the controlling environment in order to maintain control of the experiement. Virtual machines (VMs) have been used to aid these investigations due to the strong isolation properties of system-level virtualization. A key challenge in fault injection tools is to gain proper insight and context about the SUT. In VM-based FI tools, this challenge of target con- text is increased due to the separation between host and guest (VM). We discuss an approach to VM-based FI that leverages virtual machine introspection (VMI) methods to gain insight into the target s context running within the VM. The key to this environment is the ability to provide basic information to the FI system that can be used to create a map of the target environment. We describe a proof- of-concept implementation and a demonstration of its use to introduce simulated soft errors into an iterative solver benchmark running in user-space of a guest VM.

  2. Developing seismogenic source models based on geologic fault data

    Haller, Kathleen M.; Basili, Roberto

    2011-01-01

    Calculating seismic hazard usually requires input that includes seismicity associated with known faults, historical earthquake catalogs, geodesy, and models of ground shaking. This paper will address the input generally derived from geologic studies that augment the short historical catalog to predict ground shaking at time scales of tens, hundreds, or thousands of years (e.g., SSHAC 1997). A seismogenic source model, terminology we adopt here for a fault source model, includes explicit three-dimensional faults deemed capable of generating ground motions of engineering significance within a specified time frame of interest. In tectonically active regions of the world, such as near plate boundaries, multiple seismic cycles span a few hundred to a few thousand years. In contrast, in less active regions hundreds of kilometers from the nearest plate boundary, seismic cycles generally are thousands to tens of thousands of years long. Therefore, one should include sources having both longer recurrence intervals and possibly older times of most recent rupture in less active regions of the world rather than restricting the model to include only Holocene faults (i.e., those with evidence of large-magnitude earthquakes in the past 11,500 years) as is the practice in tectonically active regions with high deformation rates. During the past 15 years, our institutions independently developed databases to characterize seismogenic sources based on geologic data at a national scale. Our goal here is to compare the content of these two publicly available seismogenic source models compiled for the primary purpose of supporting seismic hazard calculations by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) and the U.S. Geological Survey (USGS); hereinafter we refer to the two seismogenic source models as INGV and USGS, respectively. This comparison is timely because new initiatives are emerging to characterize seismogenic sources at the continental scale (e.g., SHARE in the

  3. Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

    Lu, Feng; Huang, Jinquan; Xing, Yaodong

    2012-01-01

    Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

  4. Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

    Yaodong Xing

    2012-08-01

    Full Text Available Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can’t be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

  5. Fault diagnostics of dynamic system operation using a fault tree based method

    Hurdle, E.E.; Bartlett, L.M.; Andrews, J.D.

    2009-01-01

    For conventional systems, their availability can be considerably improved by reducing the time taken to restore the system to the working state when faults occur. Fault identification can be a significant proportion of the time taken in the repair process. Having diagnosed the problem the restoration of the system back to its fully functioning condition can then take place. This paper expands the capability of previous approaches to fault detection and identification using fault trees for application to dynamically changing systems. The technique has two phases. The first phase is modelling and preparation carried out offline. This gathers information on the effects that sub-system failure will have on the system performance. Causes of the sub-system failures are developed in the form of fault trees. The second phase is application. Sensors are installed on the system to provide information about current system performance from which the potential causes can be deduced. A simple system example is used to demonstrate the features of the method. To illustrate the potential for the method to deal with additional system complexity and redundancy, a section from an aircraft fuel system is used. A discussion of the results is provided.

  6. Integrated system fault diagnostics utilising digraph and fault tree-based approaches

    Bartlett, L.M.; Hurdle, E.E.; Kelly, E.M.

    2009-01-01

    With the growing intolerance to failures within systems, the issue of fault diagnosis has become ever prevalent. Information concerning these possible failures can help to minimise the disruption to the functionality of the system by allowing quick rectification. Traditional approaches to fault diagnosis within engineering systems have focused on sequential testing procedures and real-time mechanisms. Both methods have been predominantly limited to single fault causes. Latest approaches also consider the issue of multiple faults in reflection to the characteristics of modern day systems designed for high reliability. In addition, a diagnostic capability is required in real time and for changeable system functionality. This paper focuses on two approaches which have been developed to cater for the demands of diagnosis within current engineering systems, namely application of the fault tree analysis technique and the method of digraphs. Both use a comparative approach to consider differences between actual system behaviour and that expected. The procedural guidelines are discussed for each method, with an experimental aircraft fuel system used to test and demonstrate the features of the techniques. The effectiveness of the approaches is compared and their future potential highlighted

  7. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  8. Active superconducting DC fault current limiter based on flux compensation

    Shi Jing; Tang Yuejin; Wang, Chen; Zhou Yusheng; Li Jingdong; Ren Li; Chen Shijie

    2006-01-01

    With the extensive application of DC power systems, suppression of DC fault current is an important subject that guarantees system security. This paper presents an active superconducting DC fault current limiter (DC-SFCL) based on flux compensation. The DC-SFCL is composed of two superconducting windings wound on a single iron core, the primary winding is in series with DC power system, and the second winding is connected with AC power system through a PWM converter. In normal operating state, the flux in the iron core is compensated to zero, and the SFCL has no influence on DC power system. In the case of DC system accident, through regulating the active power exchange between the SFCL's second winding and the AC power system, the current on the DC side can be limited to different level complying with the system demand. Moreover, the PWM converter that interface the DC system and AC system can be controlled as a reactive power source to supply voltage support for the AC side, which has little influence on the performance of SFCL. Using MATLAB SIMULINK, the mathematic model of the DC-SFCL is created, simulation results validate the dynamics of system, and the performance of DC-SFCL is confirmed

  9. Vibration-based Fault Diagnostic of a Spur Gearbox

    Hartono Dennis

    2016-01-01

    Full Text Available This paper presents comparative studies of Fast Fourier Transform (FFT, Short Time Fourier Transform (STFT and Continuous Wavelet Transform (CWT as several advanced time-frequency analysis methods for diagnosing an early stage of spur gear tooth failure. An incipient fault of a chipped tooth was investigated in this work using vibration measurements from a spur gearbox test rig. Time Synchronous Averaging was implemented for the analysis to enhance the clarity of fault feature from the gear of interest. Based on the experimental results and analysis, it was shown that FFT method could identify the location of the faulty gear with sufficient accuracy. On the other hand, Short Time Fourier Transform method could not provide the angular location information of the faulty gear. It was found that the Continuous Wavelet Transform method offered the best representation of angle-frequency representation. It was not only able to distinguish the difference between the normal and faulty gearboxes from the joint angle-frequency results but could also provide an accurate angular location of the faulty gear tooth in the gearbox.

  10. A comparative study of sensor fault diagnosis methods based on observer for ECAS system

    Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

    2017-03-01

    The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

  11. Observer-Based Fault Estimation and Accomodation for Dynamic Systems

    Zhang, Ke; Shi, Peng

    2013-01-01

    Due to the increasing security and reliability demand of actual industrial process control systems, the study on fault diagnosis and fault tolerant control of dynamic systems has received considerable attention. Fault accommodation (FA) is one of effective methods that can be used to enhance system stability and reliability, so it has been widely and in-depth investigated and become a hot topic in recent years. Fault detection is used to monitor whether a fault occurs, which is the first step in FA. On the basis of fault detection, fault estimation (FE) is utilized to determine online the magnitude of the fault, which is a very important step because the additional controller is designed using the fault estimate. Compared with fault detection, the design difficulties of FE would increase a lot, so research on FE and accommodation is very challenging. Although there have been advancements reported on FE and accommodation for dynamic systems, the common methods at the present stage have design difficulties, whi...

  12. Knowledge-based fault diagnosis system for refuse collection vehicle

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-01-01

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle

  13. Knowledge-based fault diagnosis system for refuse collection vehicle

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y. [Centre of Advanced Research on Energy, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka (Malaysia)

    2015-05-15

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  14. Fault Detection, Isolation, and Accommodation for LTI Systems Based on GIMC Structure

    D. U. Campos-Delgado

    2008-01-01

    Full Text Available In this contribution, an active fault-tolerant scheme that achieves fault detection, isolation, and accommodation is developed for LTI systems. Faults and perturbations are considered as additive signals that modify the state or output equations. The accommodation scheme is based on the generalized internal model control architecture recently proposed for fault-tolerant control. In order to improve the performance after a fault, the compensation is considered in two steps according with a fault detection and isolation algorithm. After a fault scenario is detected, a general fault compensator is activated. Finally, once the fault is isolated, a specific compensator is introduced. In this setup, multiple faults could be treated simultaneously since their effect is additive. Design strategies for a nominal condition and under model uncertainty are presented in the paper. In addition, performance indices are also introduced to evaluate the resulting fault-tolerant scheme for detection, isolation, and accommodation. Hard thresholds are suggested for detection and isolation purposes, meanwhile, adaptive ones are considered under model uncertainty to reduce the conservativeness. A complete simulation evaluation is carried out for a DC motor setup.

  15. Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board

    Mr.D. V. Kodavade; Dr. Mrs.S.D.Apte

    2014-01-01

    With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations...

  16. Early Safety Assessment of Automotive Systems Using Sabotage Simulation-Based Fault Injection Framework

    Juez, Garazi; Amparan, Estíbaliz; Lattarulo, Ray; Ruíz, Alejandra; Perez, Joshue; Espinoza, Huascar

    2017-01-01

    As road vehicles increase their autonomy and the driver reduces his role in the control loop, novel challenges on dependability assessment arise. Model-based design combined with a simulation-based fault injection technique and a virtual vehicle poses as a promising solution for an early safety assessment of automotive systems. To start with, the design, where no safety was considered, is stimulated with a set of fault injection simulations (fault forecasting). By doing so, safety strategies ...

  17. Gear Fault Diagnosis Based on BP Neural Network

    Huang, Yongsheng; Huang, Ruoshi

    2018-03-01

    Gear transmission is more complex, widely used in machinery fields, which form of fault has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear fault in the gear fault diagnosis.

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

    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

  19. Adaptive Observer-Based Fault-Tolerant Control Design for Uncertain Systems

    Huaming Qian

    2015-01-01

    Full Text Available This study focuses on the design of the robust fault-tolerant control (FTC system based on adaptive observer for uncertain linear time invariant (LTI systems. In order to improve robustness, rapidity, and accuracy of traditional fault estimation algorithm, an adaptive fault estimation algorithm (AFEA using an augmented observer is presented. By utilizing a new fault estimator model, an improved AFEA based on linear matrix inequality (LMI technique is proposed to increase the performance. Furthermore, an observer-based state feedback fault-tolerant control strategy is designed, which guarantees the stability and performance of the faulty system. Moreover, the adaptive observer and the fault-tolerant controller are designed separately, whose performance can be considered, respectively. Finally, simulation results of an aircraft application are presented to illustrate the effectiveness of the proposed design methods.

  20. Model-Based Fault Management Engineering Tool Suite, Phase I

    National Aeronautics and Space Administration — NASA's successful development of next generation space vehicles, habitats, and robotic systems will rely on effective Fault Management Engineering. Our proposed...

  1. Fault-Tolerant Control of ANPC Three-Level Inverter Based on Order-Reduction Optimal Control Strategy under Multi-Device Open-Circuit Fault.

    Xu, Shi-Zhou; Wang, Chun-Jie; Lin, Fang-Li; Li, Shi-Xiang

    2017-10-31

    The multi-device open-circuit fault is a common fault of ANPC (Active Neutral-Point Clamped) three-level inverter and effect the operation stability of the whole system. To improve the operation stability, this paper summarized the main solutions currently firstly and analyzed all the possible states of multi-device open-circuit fault. Secondly, an order-reduction optimal control strategy was proposed under multi-device open-circuit fault to realize fault-tolerant control based on the topology and control requirement of ANPC three-level inverter and operation stability. This control strategy can solve the faults with different operation states, and can works in order-reduction state under specific open-circuit faults with specific combined devices, which sacrifices the control quality to obtain the stability priority control. Finally, the simulation and experiment proved the effectiveness of the proposed strategy.

  2. Cooperative Fault Tolerant Tracking Control for Multiagent Systems: An Intermediate Estimator-Based Approach.

    Zhu, Jun-Wei; Yang, Guang-Hong; Zhang, Wen-An; Yu, Li

    2017-10-17

    This paper studies the observer based fault tolerant tracking control problem for linear multiagent systems with multiple faults and mismatched disturbances. A novel distributed intermediate estimator based fault tolerant tracking protocol is presented. The leader's input is nonzero and unavailable to the followers. By applying a projection technique, the mismatched disturbances are separated into matched and unmatched components. For each node, a tracking error system is established, for which an intermediate estimator driven by the relative output measurements is constructed to estimate the sensor faults and a combined signal of the leader's input, process faults, and matched disturbance component. Based on the estimation, a fault tolerant tracking protocol is designed to eliminate the effects of the combined signal. Besides, the effect of unmatched disturbance component can be attenuated by directly adjusting some specified parameters. Finally, a simulation example of aircraft demonstrates the effectiveness of the designed tracking protocol.This paper studies the observer based fault tolerant tracking control problem for linear multiagent systems with multiple faults and mismatched disturbances. A novel distributed intermediate estimator based fault tolerant tracking protocol is presented. The leader's input is nonzero and unavailable to the followers. By applying a projection technique, the mismatched disturbances are separated into matched and unmatched components. For each node, a tracking error system is established, for which an intermediate estimator driven by the relative output measurements is constructed to estimate the sensor faults and a combined signal of the leader's input, process faults, and matched disturbance component. Based on the estimation, a fault tolerant tracking protocol is designed to eliminate the effects of the combined signal. Besides, the effect of unmatched disturbance component can be attenuated by directly adjusting some

  3. Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

    Yu Yuan

    2015-01-01

    Full Text Available The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

  4. Indirect Inference for Stochastic Differential Equations Based on Moment Expansions

    Ballesio, Marco; Tempone, Raul; Vilanova, Pedro

    2016-01-01

    We provide an indirect inference method to estimate the parameters of timehomogeneous scalar diffusion and jump diffusion processes. We obtain a system of ODEs that approximate the time evolution of the first two moments of the process

  5. Model-based monitoring of rotors with multiple coexisting faults

    Rossner, Markus

    2015-01-01

    Monitoring systems are applied to many rotors, but only few monitoring systems can separate coexisting errors and identify their quantity. This research project solves this problem using a combination of signal-based and model-based monitoring. The signal-based part performs a pre-selection of possible errors; these errors are further separated with model-based methods. This approach is demonstrated for the errors unbalance, bow, stator-fixed misalignment, rotor-fixed misalignment and roundness errors. For the model-based part, unambiguous error definitions and models are set up. The Ritz approach reduces the model order and therefore speeds up the diagnosis. Identification algorithms are developed for the different rotor faults. Hereto, reliable damage indicators and proper sub steps of the diagnosis have to be defined. For several monitoring problems, measuring both deflection and bearing force is very useful. The monitoring system is verified by experiments on an academic rotor test rig. The interpretation of the measurements requires much knowledge concerning the dynamics of the rotor. Due to the model-based approach, the system can separate errors with similar signal patterns and identify bow and roundness error online at operation speed. [de

  6. Fault Diagnosis of an Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

    Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc

    2015-01-01

    This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown...... turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from the structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed FD approach has been validated on a 5-MW wind...

  7. UIO-based Fault Diagnosis for Hydraulic Automatic Gauge Control System of Magnesium Sheet Mill

    Li-Ping FAN

    2014-02-01

    Full Text Available Hydraulic automatic gauge control system of magnesium sheet mill is a complex integrated control system, which including mechanical, hydraulic and electrical comprehensive information. The failure rate of AGC system always is high, and its fault reasons are always complex. Based on analyzing the fault of main components of the automatic gauge control system, unknown input observer is used to realize fault diagnosis and isolation. Simulation results show that the fault diagnosis method based on the unknown input observer for the hydraulic automatic gauge control system of magnesium sheet mill is effective.

  8. Aircraft Attitude Distributed Fault-tolerant Control Based on Dynamic Actuator

    Zhou Hong-Cheng

    2014-09-01

    Full Text Available For attitude control system, based on decentralized fault-tolerant control framework, actuators damage and stuck fault detection and identification unit are designed for the flight control system. And observer-based auxiliary system unit is also designed. The auxiliary system implies control surface damage faults and disturbances information. Firstly, we give the attitude control system under actuator stuck, lose of effectiveness, and control surface damages faults. Secondly, a multi-observer is designed for actuator fault detection and identification using a decision-making mechanism to determine current actuator failure modes. Then, an adaptive sliding mode observer is designed for implicit control surface damages and interference information. The reconfigurable controller can achieve fault tolerant using the information of adaptive sliding mode observer. Finally, the simulation results show the effectiveness of the proposed method.

  9. Model-based fault detection and isolation of a PWR nuclear power plant using neural networks

    Far, R.R.; Davilu, H.; Lucas, C.

    2008-01-01

    The proper and timely fault detection and isolation of industrial plant is of premier importance to guarantee the safe and reliable operation of industrial plants. The paper presents application of a neural networks-based scheme for fault detection and isolation, for the pressurizer of a PWR nuclear power plant. The scheme is constituted by 2 components: residual generation and fault isolation. The first component generates residuals via the discrepancy between measurements coming from the plant and a nominal model. The neutral network estimator is trained with healthy data collected from a full-scale simulator. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. These patterns are stored in an associative memory based on a recurrent neutral network. The proposed fault diagnosis tool is evaluated on-line via a full-scale simulator detected and isolate the main faults appearing in the pressurizer of a PWR. (orig.)

  10. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  11. A fault-tolerant strategy based on SMC for current-controlled converters

    Azer, Peter M.; Marei, Mostafa I.; Sattar, Ahmed A.

    2018-05-01

    The sliding mode control (SMC) is used to control variable structure systems such as power electronics converters. This paper presents a fault-tolerant strategy based on the SMC for current-controlled AC-DC converters. The proposed SMC is based on three sliding surfaces for the three legs of the AC-DC converter. Two sliding surfaces are assigned to control the phase currents since the input three-phase currents are balanced. Hence, the third sliding surface is considered as an extra degree of freedom which is utilised to control the neutral voltage. This action is utilised to enhance the performance of the converter during open-switch faults. The proposed fault-tolerant strategy is based on allocating the sliding surface of the faulty leg to control the neutral voltage. Consequently, the current waveform is improved. The behaviour of the current-controlled converter during different types of open-switch faults is analysed. Double switch faults include three cases: two upper switch fault; upper and lower switch fault at different legs; and two switches of the same leg. The dynamic performance of the proposed system is evaluated during healthy and open-switch fault operations. Simulation results exhibit the various merits of the proposed SMC-based fault-tolerant strategy.

  12. An algebra-based method for inferring gene regulatory networks.

    Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

    2014-03-26

    The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

  13. A Framework-Based Approach for Fault-Tolerant Service Robots

    Heejune Ahn

    2012-11-01

    Full Text Available Recently the component-based approach has become a major trend in intelligent service robot development due to its reusability and productivity. The framework in a component-based system should provide essential services for application components. However, to our knowledge the existing robot frameworks do not yet support fault tolerance service. Moreover, it is often believed that faults can be handled only at the application level. In this paper, by extending the robot framework with the fault tolerance function, we argue that the framework-based fault tolerance approach is feasible and even has many benefits, including that: 1 the system integrators can build fault tolerance applications from non-fault-aware components; 2 the constraints of the components and the operating environment can be considered at the time of integration, which – cannot be anticipated eaily at the time of component development; 3 consistency in system reliability can be obtained even in spite of diverse application component sources. In the proposed construction, we build XML rule files defining the rules for probing and determining the fault conditions of each component, contamination cases from a faulty component, and the possible recovery and safety methods. The rule files are established by a system integrator and the fault manager in the framework controls the fault tolerance process according to the rules. We demonstrate that the fault-tolerant framework can incorporate widely accepted fault tolerance techniques. The effectiveness and real-time performance of the framework-based approach and its techniques are examined by testing an autonomous mobile robot in typical fault scenarios.

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

    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 ...

  15. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    Schlechtingen, Meik; Santos, Ilmar

    2011-01-01

    This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal...

  16. Rotor current transient analysis of DFIG-based wind turbines during symmetrical voltage faults

    Ling, Yu; Cai, Xu; Wang, Ningbo

    2013-01-01

    Highlights: • We theoretically analyze the rotor fault current of DFIG based on space vector. • The presented analysis is simple, easy to understand. • The analysis highlights the accuracy of the expression of the rotor fault currents. • The expression can be widely used to analyze the different levels of voltage symmetrical fault. • Simulation results show the accuracy of the expression of the rotor currents. - Abstract: The impact of grid voltage fault on doubly fed induction generators (DFIGs), especially rotor currents, has received much attention. So, in this paper, the rotor currents of based-DFIG wind turbines are considered in a generalized way, which can be widely used to analyze the cases under different levels of voltage symmetrical faults. A direct method based on space vector is proposed to obtain an accurate expression of rotor currents as a function of time for symmetrical voltage faults in the power system. The presented theoretical analysis is simple and easy to understand and especially highlights the accuracy of the expression. Finally, the comparable simulations evaluate this analysis and show that the expression of the rotor currents is sufficient to calculate the maximum fault current, DC and AC components, and especially helps to understand the causes of the problem and as a result, contributes to adapt reasonable approaches to enhance the fault ride through (FRT) capability of DFIG wind turbines during a voltage fault

  17. FUZZY FAULT DETECTION FOR PERMANENT MAGNET SYNCHRONOUS GENERATOR

    N. Selvaganesan

    2011-07-01

    Full Text Available Faults in engineering systems are difficult to avoid and may result in serious consequences. Effective fault detection and diagnosis can improve system reliability and avoid expensive maintenance. In this paper fuzzy system based fault detection scheme for permanent magnet synchronous generator is proposed. The sequence current components like positive and negative sequence currents are used as fault indicators and given as inputs to fuzzy fault detector. Also, the fuzzy inference system is created and rule base is evaluated, relating the sequence current component to the type of faults. These rules are fired for specific changes in sequence current component and the faults are detected. The feasibility of the proposed scheme for permanent magnet synchronous generator is demonstrated for different types of fault under various operating conditions using MATLAB/Simulink.

  18. ASCS online fault detection and isolation based on an improved MPCA

    Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

    2014-09-01

    Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

  19. Fault Detection for Large-Scale Railway Maintenance Equipment Base on Wireless Sensor Networks

    Junfu Yu

    2014-04-01

    Full Text Available Focusing on the fault detection application for large-scale railway maintenance equipment with the specialties of low-cost, energy efficiency, collecting data of the function units. This paper proposed energy efficiency, convenient installation fault detection application using Sigsbee wireless sensor networks, which Sigsbee is the most widely used protocol based on IEEE 802.15.4. This paper proposed a systematic application from hardware design using STM32F103 chips as processer, to software system. Fault detection application is the basic part of the fault diagnose system, wireless sensor nodes of the fault detection application with different kinds of sensors for verities function units communication by Sigsbee to collecting and sending basic working status data to the home gateway, then data will be sent to the fault diagnose system.

  20. Fault Tolerant Control for Civil Structures Based on LMI Approach

    Chunxu Qu

    2013-01-01

    Full Text Available The control system may lose the performance to suppress the structural vibration due to the faults in sensors or actuators. This paper designs the filter to perform the fault detection and isolation (FDI and then reforms the control strategy to achieve the fault tolerant control (FTC. The dynamic equation of the structure with active mass damper (AMD is first formulated. Then, an estimated system is built to transform the FDI filter design problem to the static gain optimization problem. The gain is designed to minimize the gap between the estimated system and the practical system, which can be calculated by linear matrix inequality (LMI approach. The FDI filter is finally used to isolate the sensor faults and reform the FTC strategy. The efficiency of FDI and FTC is validated by the numerical simulation of a three-story structure with AMD system with the consideration of sensor faults. The results show that the proposed FDI filter can detect the sensor faults and FTC controller can effectively tolerate the faults and suppress the structural vibration.

  1. Communication-based fault handling scheme for ungrounded distribution systems

    Yang, X.; Lim, S.I.; Lee, S.J.; Choi, M.S.

    2006-01-01

    The requirement for high quality and highly reliable power supplies has been increasing as a result of increasing demand for power. At the time of a fault occurrence in a distribution system, some protection method would be dedicated to fault section isolation and service restoration. However, if there are many outage areas when the protection method is performed, it is an inconvenience to the customer. A conventional method to determine a fault section in ungrounded systems requires many successive outage invocations. This paper proposed an efficient fault section isolation method and service restoration method for single line-to-ground fault in an ungrounded distribution system that was faster than the conventional one using the information exchange between connected feeders. The proposed algorithm could be performed without any power supply interruption and could decrease the number of switching operations, so that customers would not experience outages very frequently. The method involved the use of an intelligent communication method and a sequential switching control scheme. The proposed algorithm was also applied in both a single-tie and multi-tie distribution system. This proposed algorithm has been verified through fault simulations in a simple model of ungrounded multi-tie distribution system. The method proposed in this paper was proven to offer more efficient fault identification and much less outage time than the conventional method. The proposed method could contribute to a system design since it is valid in multi-tie systems. 5 refs., 2 tabs., 8 figs

  2. Dynamic Output Feedback Based Active Decentralized Fault-Tolerant Control for Reconfigurable Manipulator with Concurrent Failures

    Yuanchun Li

    2015-01-01

    Full Text Available The goal of this paper is to describe an active decentralized fault-tolerant control (ADFTC strategy based on dynamic output feedback for reconfigurable manipulators with concurrent actuator and sensor failures. Consider each joint module of the reconfigurable manipulator as a subsystem, and treat the fault as the unknown input of the subsystem. Firstly, by virtue of linear matrix inequality (LMI technique, the decentralized proportional-integral observer (DPIO is designed to estimate and compensate the sensor fault online; hereafter, the compensated system model could be derived. Then, the actuator fault is estimated similarly by another DPIO using LMI as well, and the sufficient condition of the existence of H∞ fault-tolerant controller in the dynamic output feedback is presented for the compensated system model. Furthermore, the dynamic output feedback controller is presented based on the estimation of actuator fault to realize active fault-tolerant control. Finally, two 3-DOF reconfigurable manipulators with different configurations are employed to verify the effectiveness of the proposed scheme in simulation. The main advantages of the proposed scheme lie in that it can handle the concurrent faults act on the actuator and sensor on the same joint module, as well as there is no requirement of fault detection and isolation process; moreover, it is more feasible to the modularity of the reconfigurable manipulator.

  3. Fault zone identification in the eastern part of the Persian Gulf based on combined seismic attributes

    Mirkamali, M. S.; Keshavarz FK, N.; Bakhtiari, M. R.

    2013-02-01

    Faults, as main pathways for fluids, play a critical role in creating regions of high porosity and permeability, in cutting cap rock and in the migration of hydrocarbons into the reservoir. Therefore, accurate identification of fault zones is very important in maximizing production from petroleum traps. Image processing and modern visualization techniques are provided for better mapping of objects of interest. In this study, the application of fault mapping in the identification of fault zones within the Mishan and Aghajari formations above the Guri base unconformity surface in the eastern part of Persian Gulf is investigated. Seismic single- and multi-trace attribute analyses are employed separately to determine faults in a vertical section, but different kinds of geological objects cannot be identified using individual attributes only. A mapping model is utilized to improve the identification of the faults, giving more accurate results. This method is based on combinations of all individual relevant attributes using a neural network system to create combined attributes, which gives an optimal view of the object of interest. Firstly, a set of relevant attributes were separately calculated on the vertical section. Then, at interpreted positions, some example training locations were manually selected in each fault and non-fault class by an interpreter. A neural network was trained on combinations of the attributes extracted at the example training locations to generate an optimized fault cube. Finally, the results of the fault and nonfault probability cube were estimated, which the neural network applied to the entire data set. The fault probability cube was obtained with higher mapping accuracy and greater contrast, and with fewer disturbances in comparison with individual attributes. The computed results of this study can support better understanding of the data, providing fault zone mapping with reliable results.

  4. Fault zone identification in the eastern part of the Persian Gulf based on combined seismic attributes

    Mirkamali, M S; Keshavarz FK, N; Bakhtiari, M R

    2013-01-01

    Faults, as main pathways for fluids, play a critical role in creating regions of high porosity and permeability, in cutting cap rock and in the migration of hydrocarbons into the reservoir. Therefore, accurate identification of fault zones is very important in maximizing production from petroleum traps. Image processing and modern visualization techniques are provided for better mapping of objects of interest. In this study, the application of fault mapping in the identification of fault zones within the Mishan and Aghajari formations above the Guri base unconformity surface in the eastern part of Persian Gulf is investigated. Seismic single- and multi-trace attribute analyses are employed separately to determine faults in a vertical section, but different kinds of geological objects cannot be identified using individual attributes only. A mapping model is utilized to improve the identification of the faults, giving more accurate results. This method is based on combinations of all individual relevant attributes using a neural network system to create combined attributes, which gives an optimal view of the object of interest. Firstly, a set of relevant attributes were separately calculated on the vertical section. Then, at interpreted positions, some example training locations were manually selected in each fault and non-fault class by an interpreter. A neural network was trained on combinations of the attributes extracted at the example training locations to generate an optimized fault cube. Finally, the results of the fault and nonfault probability cube were estimated, which the neural network applied to the entire data set. The fault probability cube was obtained with higher mapping accuracy and greater contrast, and with fewer disturbances in comparison with individual attributes. The computed results of this study can support better understanding of the data, providing fault zone mapping with reliable results. (paper)

  5. Experiences of pathways, outcomes and choice after severe traumatic brain injury under no-fault versus fault-based motor accident insurance.

    Harrington, Rosamund; Foster, Michele; Fleming, Jennifer

    2015-01-01

    To explore experiences of pathways, outcomes and choice after motor vehicle accident (MVA) acquired severe traumatic brain injury (sTBI) under fault-based vs no-fault motor accident insurance (MAI). In-depth qualitative interviews with 10 adults with sTBI and 17 family members examined experiences of pathways, outcomes and choice and how these were shaped by both compensable status and interactions with service providers and service funders under a no-fault and a fault-based MAI scheme. Participants were sampled to provide variation in compensable status, injury severity, time post-injury and metropolitan vs regional residency. Interviews were recorded, transcribed and thematically analysed to identify dominant themes under each scheme. Dominant themes emerging under the no-fault scheme included: (a) rehabilitation-focused pathways; (b) a sense of security; and (c) bounded choices. Dominant themes under the fault-based scheme included: (a) resource-rationed pathways; (b) pressured lives; and (c) unknown choices. Participants under the no-fault scheme experienced superior access to specialist rehabilitation services, greater surety of support and more choice over how rehabilitation and life-time care needs were met. This study provides valuable insights into individual experiences under fault-based vs no-fault MAI. Implications for an injury insurance scheme design to optimize pathways, outcomes and choice after sTBI are discussed.

  6. Fault trend prediction of device based on support vector regression

    Song Meicun; Cai Qi

    2011-01-01

    The research condition of fault trend prediction and the basic theory of support vector regression (SVR) were introduced. SVR was applied to the fault trend prediction of roller bearing, and compared with other methods (BP neural network, gray model, and gray-AR model). The results show that BP network tends to overlearn and gets into local minimum so that the predictive result is unstable. It also shows that the predictive result of SVR is stabilization, and SVR is superior to BP neural network, gray model and gray-AR model in predictive precision. SVR is a kind of effective method of fault trend prediction. (authors)

  7. Indirect Inference for Stochastic Differential Equations Based on Moment Expansions

    Ballesio, Marco

    2016-01-06

    We provide an indirect inference method to estimate the parameters of timehomogeneous scalar diffusion and jump diffusion processes. We obtain a system of ODEs that approximate the time evolution of the first two moments of the process by the approximation of the stochastic model applying a second order Taylor expansion of the SDE s infinitesimal generator in the Dynkin s formula. This method allows a simple and efficient procedure to infer the parameters of such stochastic processes given the data by the maximization of the likelihood of an approximating Gaussian process described by the two moments equations. Finally, we perform numerical experiments for two datasets arising from organic and inorganic fouling deposition phenomena.

  8. Factors for simultaneous rupture assessment of active fault. Part 1. Fault geometry and slip-distribution based on tectonic geomorphological and paleoseismological investigations

    Sasaki, Toshinori; Ueta, Keiichi

    2012-01-01

    It is important to evaluate the magnitude of an earthquake caused by multiple active faults, taking into account the simultaneous effects. The simultaneity of adjacent active faults is often decided on the basis of geometric distances except for the cases in which paleoseismic records of these faults are well known. We have been studying the step area between the Nukumi fault and the Neodani fault, which appeared as consecutive ruptures in the 1891 Nobi earthquake, since 2009. The purpose of this study is to establish innovation in valuation technique of the simultaneity of adjacent active faults in addition to the techniques based on the paleoseismic record and the geometric distance. The present work is intended to clarify the distribution of tectonic geomorphology along the Nukumi fault and the Neodani fault by high-resolution interpretations of airborne LiDAR DEM and aerial photograph, and the field survey of outcrops and location survey. As a result of topographic survey, we found consecutive tectonic topography which is left lateral displacement of ridge and valley lines and reverse scarplets along these faults in dense vegetation area. We have found several new outcrops in this area where the surface ruptures of the 1891 Nobi earthquake have not been known. At the several outcrops, humic layer whose age is from 14th century to 19th century by 14C age dating was deformed by the active fault. We conclude that the surface rupture of Nukumi fault in the 1891 Nobi earthquake is continuous to 12km southeast of Nukumi village. In other words, these findings indicate that there is 10-12km parallel overlap zone between the surface rupture of the southeastern end of Nukumi fault and the northwestern end of Neodani fault. (author)

  9. Model-based fault detection algorithm for photovoltaic system monitoring

    Harrou, Fouzi

    2018-02-12

    Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  10. Fault tolerance based on serial communication of FPGA

    Peng Jing; Fang Zongliang; Xu Quanzhou; Hu Jiewei; Ma Guizhen

    2012-01-01

    There maybe appear mistake in serial communication. This paper was described the intellectual detector of γ dose ratemeter communication with FPGA. The software of FPGA designed the code about fault tolerance, prevented mistake effectively. (authors)

  11. Model based fault diagnosis in a centrifugal pump application using structural analysis

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  12. Model Based Fault Diagnosis in a Centrifugal Pump Application using Structural Analysis

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  13. Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review.

    Huang, Wentao; Sun, Hongjian; Wang, Weijie

    2017-06-03

    Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

  14. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    Busbait, Monther I.; Moshkov, Mikhail

    2016-01-01

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound

  15. Transient Monitoring Function–Based Fault Detection for Inverter-Interfaced Microgrids

    Sadeghkhani, Iman; Esmail Hamedani Golshan, Mohamad; Mehrizi-Sani, Ali

    2018-01-01

    One of the major challenges in protection of the inverter-interfaced islanded microgrids is their limited fault current level. This degrades the performance of traditional overcurrent protection schemes. This paper proposes a fault detection strategy based on monitoring the transient response......-domain simulation case studies using the CIGRE benchmark low voltage microgrid network....

  16. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

  17. A fault diagnosis system for PV power station based on global partitioned gradually approximation method

    Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

    2016-08-01

    As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

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

    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.

  19. Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

    Yu, Jianbo

    2015-12-01

    Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

  20. Fault-tolerant control for current sensors of doubly fed induction generators based on an improved fault detection method

    Li, Hui; Yang, Chao; Hu, Yaogang

    2014-01-01

    Fault-tolerant control of current sensors is studied in this paper to improve the reliability of a doubly fed induction generator (DFIG). A fault-tolerant control system of current sensors is presented for the DFIG, which consists of a new current observer and an improved current sensor fault...... detection algorithm, and fault-tolerant control system are investigated by simulation. The results indicate that the outputs of the observer and the sensor are highly coherent. The fault detection algorithm can efficiently detect both soft and hard faults in current sensors, and the fault-tolerant control...

  1. Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

    Odgaard, Peter Fogh; Lin, Bao; Jørgensen, Sten Bay

    2006-01-01

    In order to improve the reliability of power plants it is important to detect fault as fast as possible. Doing this it is interesting to find the most efficient method. Since modeling of large scale systems is time consuming it is interesting to compare a model-based method with data driven ones....

  2. Fault-tolerant measurement-based quantum computing with continuous-variable cluster states.

    Menicucci, Nicolas C

    2014-03-28

    A long-standing open question about Gaussian continuous-variable cluster states is whether they enable fault-tolerant measurement-based quantum computation. The answer is yes. Initial squeezing in the cluster above a threshold value of 20.5 dB ensures that errors from finite squeezing acting on encoded qubits are below the fault-tolerance threshold of known qubit-based error-correcting codes. By concatenating with one of these codes and using ancilla-based error correction, fault-tolerant measurement-based quantum computation of theoretically indefinite length is possible with finitely squeezed cluster states.

  3. A Method of Rotating Machinery Fault Diagnosis Based on the Close Degree of Information Entropy

    GENG Jun-bao; HUANG Shu-hong; JIN Jia-shan; CHEN Fei; LIU Wei

    2006-01-01

    This paper presents a method of rotating machinery fault diagnosis based on the close degree of information entropy. In the view of the information entropy, we introduce four information entropy features of the rotating machinery, which describe the vibration condition of the machinery. The four features are, respectively, denominated as singular spectrum entropy, power spectrum entropy, wavelet space state feature entropy and wavelet power spectrum entropy. The value scopes of the four information entropy features of the rotating machinery in some typical fault conditions are gained by experiments, which can be acted as the standard features of fault diagnosis. According to the principle of the shorter distance between the more similar models, the decision-making method based on the close degree of information entropy is put forward to deal with the recognition of fault patterns. We demonstrate the effectiveness of this approach in an instance involving the fault pattern recognition of some rotating machinery.

  4. A knowledge-based approach to the evaluation of fault trees

    Hwang, Yann-Jong; Chow, Louis R.; Huang, Henry C.

    1996-01-01

    A list of critical components is useful for determining the potential problems of a complex system. However, to find this list through evaluating the fault trees is expensive and time consuming. This paper intends to propose an integrated software program which consists of a fault tree constructor, a knowledge base, and an efficient algorithm for evaluating minimal cut sets of a large fault tree. The proposed algorithm uses the approaches of top-down heuristic searching and the probability-based truncation. That makes the evaluation of fault trees obviously efficient and provides critical components for solving the potential problems in complex systems. Finally, some practical fault trees are included to illustrate the results

  5. Learning and case-based reasoning for faults diagnosis-aiding in nuclear power plants

    Nicolini, C.

    1998-01-01

    The aim of this thesis is the design of a faults diagnosis-aiding system in a nuclear facility of the Cea. Actually the existing system allows the optimization of the production processes in regular operating conditions. Meanwhile during accidental events, the alarms, managed by threshold, are bringing no relevant information. To increase the reliability and the safety, the human operator needs a faults diagnosis-aiding system. The developed system, SECAPI, combines problem solving techniques and automatic learning techniques, that allow the diagnosis and the the simulation of various faults happening on nuclear facilities. Its reasoning principle uses case-based and rules-based techniques. SECAPI owns a learning module which reads out knowledge connected with faults. It can then simulate various faults, using the inductive logical computing. SECAPI has been applied on a radioactive tritium treatment operating channel, at the Cea with good results. (A.L.B.)

  6. Study on reliability analysis based on multilevel flow models and fault tree method

    Chen Qiang; Yang Ming

    2014-01-01

    Multilevel flow models (MFM) and fault tree method describe the system knowledge in different forms, so the two methods express an equivalent logic of the system reliability under the same boundary conditions and assumptions. Based on this and combined with the characteristics of MFM, a method mapping MFM to fault tree was put forward, thus providing a way to establish fault tree rapidly and realizing qualitative reliability analysis based on MFM. Taking the safety injection system of pressurized water reactor nuclear power plant as an example, its MFM was established and its reliability was analyzed qualitatively. The analysis result shows that the logic of mapping MFM to fault tree is correct. The MFM is easily understood, created and modified. Compared with the traditional fault tree analysis, the workload is greatly reduced and the modeling time is saved. (authors)

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

    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.

  8. Line-to-Line Fault Analysis and Location in a VSC-Based Low-Voltage DC Distribution Network

    Shi-Min Xue

    2018-03-01

    Full Text Available A DC cable short-circuit fault is the most severe fault type that occurs in DC distribution networks, having a negative impact on transmission equipment and the stability of system operation. When a short-circuit fault occurs in a DC distribution network based on a voltage source converter (VSC, an in-depth analysis and characterization of the fault is of great significance to establish relay protection, devise fault current limiters and realize fault location. However, research on short-circuit faults in VSC-based low-voltage DC (LVDC systems, which are greatly different from high-voltage DC (HVDC systems, is currently stagnant. The existing research in this area is not conclusive, with further study required to explain findings in HVDC systems that do not fit with simulated results or lack thorough theoretical analyses. In this paper, faults are divided into transient- and steady-state faults, and detailed formulas are provided. A more thorough and practical theoretical analysis with fewer errors can be used to develop protection schemes and short-circuit fault locations based on transient- and steady-state analytic formulas. Compared to the classical methods, the fault analyses in this paper provide more accurate computed results of fault current. Thus, the fault location method can rapidly evaluate the distance between the fault and converter. The analyses of error increase and an improved handshaking method coordinating with the proposed location method are presented.

  9. Gear-box fault detection using time-frequency based methods

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2015-01-01

    Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors...... in the gear-box resonance frequency can be detected. Two different time–frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen–Loeve basis. Both of them detect the gear-box fault with an acceptable detection delay of maximum 100s, which...... is neglectable compared with the fault developing time....

  10. Nuclear Power Plants Fault Diagnosis Method Based on Data Fusion

    Xie Chunli; Liu Yongkuo; Xia Hong

    2009-01-01

    The data fusion is a method suit for complex system fault diagnosis such as nuclear power plants, which is multisource information processing technology. This paper uses data fusion information hierarchical thinking and divides nuclear power plants fault diagnosis into three levels. Data level adopts data mining method to handle data and reduction attributes. Feature level uses three parallel neural networks to deal with attributes of data level reduction and the outputs of three networks are as the basic probability assignment of Dempster-Shafer (D-S) evidence theory. The improved D-S evidence theory synthesizes the outputs of neural networks in decision level, which conquer the traditional D-S evidence theory limitation which can't dispose conflict information. The diagnosis method was tested using correlation data of literature. The test results indicate that the data fusion diagnosis system can diagnose nuclear power plants faults accurately and the method has application value. (authors)

  11. A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

    Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

    2018-03-01

    In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

  12. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

    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.

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

    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.

  14. Observer and data-driven model based fault detection in Power Plant Coal Mills

    Fogh Odgaard, Peter; Lin, Bao; Jørgensen, Sten Bay

    2008-01-01

    model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual...... between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault......This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles...

  15. Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

    Zhou Gang; Ge Shengqi; Yang Li

    2010-01-01

    A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

  16. Fault tree synthesis for software design analysis of PLC based safety-critical systems

    Koo, S. R.; Cho, C. H.; Seong, P. H.

    2006-01-01

    As a software verification and validation should be performed for the development of PLC based safety-critical systems, a software safety analysis is also considered in line with entire software life cycle. In this paper, we propose a technique of software safety analysis in the design phase. Among various software hazard analysis techniques, fault tree analysis is most widely used for the safety analysis of nuclear power plant systems. Fault tree analysis also has the most intuitive notation and makes both qualitative and quantitative analyses possible. To analyze the design phase more effectively, we propose a technique of fault tree synthesis, along with a universal fault tree template for the architecture modules of nuclear software. Consequently, we can analyze the safety of software on the basis of fault tree synthesis. (authors)

  17. Fault detection of Tennessee Eastman process based on topological features and SVM

    Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

    2018-03-01

    Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

  18. Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm.

    Wang, Tianyang; Chu, Fulei; Han, Qinkai

    2017-03-01

    Identifying the differences between the spectra or envelope spectra of a faulty signal and a healthy baseline signal is an efficient planetary gearbox local fault detection strategy. However, causes other than local faults can also generate the characteristic frequency of a ring gear fault; this may further affect the detection of a local fault. To address this issue, a new filtering algorithm based on the meshing resonance phenomenon is proposed. In detail, the raw signal is first decomposed into different frequency bands and levels. Then, a new meshing index and an MRgram are constructed to determine which bands belong to the meshing resonance frequency band. Furthermore, an optimal filter band is selected from this MRgram. Finally, the ring gear fault can be detected according to the envelope spectrum of the band-pass filtering result. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Research on the Diagnosis of Rotor Coupling Fault Based on Wavelet Packet and Local Fisher Discriminant

    Guangbin Wang

    2014-09-01

    Full Text Available this article is for the coupling fault diagnosis of rotor system, and does in-depth analysis of the rotor unbalance and misalignment, and the fault formed by the coupling of these two. Through research, Rotor Coupling was found filled with rich features. In this paper, Wavelet packet de- noising ideas being introduced to the local Fisher discriminant analysis (LFDA, a new method of fault diagnosis based on Wavelet Packet and Local Fisher Discriminant is proposed. The technology of information fusion is applied to the data processing with coupling faults. By comparing and analyzing the algorithms effect of LE, LPP, FDA, LFDA and IOLFA through experiment, it shows that LE and LPP are unable to identify the fault, while FDA, LFDA has better identification, and Wavelet Packet and Local Fisher discriminant has the best effect.

  20. Modelling of Surface Fault Structures Based on Ground Magnetic Survey

    Michels, A.; McEnroe, S. A.

    2017-12-01

    The island of Leka confines the exposure of the Leka Ophiolite Complex (LOC) which contains mantle and crustal rocks and provides a rare opportunity to study the magnetic properties and response of these formations. The LOC is comprised of five rock units: (1) harzburgite that is strongly deformed, shifting into an increasingly olivine-rich dunite (2) ultramafic cumulates with layers of olivine, chromite, clinopyroxene and orthopyroxene. These cumulates are overlain by (3) metagabbros, which are cut by (4) metabasaltic dykes and (5) pillow lavas (Furnes et al. 1988). Over the course of three field seasons a detailed ground-magnetic survey was made over the island covering all units of the LOC and collecting samples from 109 sites for magnetic measurements. NRM, susceptibility, density and hysteresis properties were measured. In total 66% of samples with a Q value > 1, suggests that the magnetic anomalies should include both induced and remanent components in the model.This Ophiolite originated from a suprasubduction zone near the coast of Laurentia (497±2 Ma), was obducted onto Laurentia (≈460 Ma) and then transferred to Baltica during the Caledonide Orogeny (≈430 Ma). The LOC was faulted, deformed and serpentinized during these events. The gabbro and ultramafic rocks are separated by a normal fault. The dominant magnetic anomaly that crosses the island correlates with this normal fault. There are a series of smaller scale faults that are parallel to this and some correspond to local highs that can be highlighted by a tilt derivative of the magnetic data. These fault boundaries which are well delineated by the distinct magnetic anomalies in both ground and aeromagnetic survey data are likely caused by increased amount of serpentinization of the ultramafic rocks in the fault areas.

  1. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    Harrou, Fouzi

    2017-09-18

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

  2. Dynamic Evolution Of Off-Fault Medium During An Earthquake: A Micromechanics Based Model

    Thomas, Marion Y.; Bhat, Harsha S.

    2018-05-01

    Geophysical observations show a dramatic drop of seismic wave speeds in the shallow off-fault medium following earthquake ruptures. Seismic ruptures generate, or reactivate, damage around faults that alter the constitutive response of the surrounding medium, which in turn modifies the earthquake itself, the seismic radiation, and the near-fault ground motion. We present a micromechanics based constitutive model that accounts for dynamic evolution of elastic moduli at high-strain rates. We consider 2D in-plane models, with a 1D right lateral fault featuring slip-weakening friction law. The two scenarios studied here assume uniform initial off-fault damage and an observationally motivated exponential decay of initial damage with fault normal distance. Both scenarios produce dynamic damage that is consistent with geological observations. A small difference in initial damage actively impacts the final damage pattern. The second numerical experiment, in particular, highlights the complex feedback that exists between the evolving medium and the seismic event. We show that there is a unique off-fault damage pattern associated with supershear transition of an earthquake rupture that could be potentially seen as a geological signature of this transition. These scenarios presented here underline the importance of incorporating the complex structure of fault zone systems in dynamic models of earthquakes.

  3. Dynamic Evolution Of Off-Fault Medium During An Earthquake: A Micromechanics Based Model

    Thomas, M. Y.; Bhat, H. S.

    2017-12-01

    Geophysical observations show a dramatic drop of seismic wave speeds in the shallow off-fault medium following earthquake ruptures. Seismic ruptures generate, or reactivate, damage around faults that alter the constitutive response of the surrounding medium, which in turn modifies the earthquake itself, the seismic radiation, and the near-fault ground motion. We present a micromechanics based constitutive model that accounts for dynamic evolution of elastic moduli at high-strain rates. We consider 2D in-plane models, with a 1D right lateral fault featuring slip-weakening friction law. The two scenarios studied here assume uniform initial off-fault damage and an observationally motivated exponential decay of initial damage with fault normal distance. Both scenarios produce dynamic damage that is consistent with geological observations. A small difference in initial damage actively impacts the final damage pattern. The second numerical experiment, in particular, highlights the complex feedback that exists between the evolving medium and the seismic event. We show that there is a unique off-fault damage pattern associated with supershear transition of an earthquake rupture that could be potentially seen as a geological signature of this transition. These scenarios presented here underline the importance of incorporating the complex structure of fault zone systems in dynamic models of earthquakes.

  4. Fault Diagnosis Method of Polymerization Kettle Equipment Based on Rough Sets and BP Neural Network

    Shu-zhi Gao

    2013-01-01

    Full Text Available Polyvinyl chloride (PVC polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system.

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

    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.

  6. Fault diagnosis of direct-drive wind turbine based on support vector machine

    An, X L; Jiang, D X; Li, S H; Chen, J

    2011-01-01

    A fault diagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.

  7. Improved protection system for phase faults on marine vessels based on ratio between negative sequence and positive sequence of the fault current

    Ciontea, Catalin-Iosif; Hong, Qiteng; Booth, Campbell

    2018-01-01

    algorithm is implemented in a programmable digital relay embedded in a hardware-in-the-loop (HIL) test set-up that emulates a typical maritime feeder using a real-time digital simulator. The HIL set-up allows testing of the new protection method under a wide range of faults and network conditions......This study presents a new method to protect the radial feeders on marine vessels. The proposed protection method is effective against phase–phase (PP) faults and is based on evaluation of the ratio between the negative sequence and positive sequence of the fault currents. It is shown...... that the magnitude of the introduced ratio increases significantly during the PP fault, hence indicating the fault presence in an electric network. Here, the theoretical background of the new method of protection is firstly discussed, based on which the new protection algorithm is described afterwards. The proposed...

  8. Design & Evaluation of a Protection Algorithm for a Wind Turbine Generator based on the fault-generated Symmetrical Components

    Zheng, T. Y.; Cha, Seung-Tae; Lee, B. E.

    2011-01-01

    A protection relay for a wind turbine generator (WTG) based on the fault-generated symmetrical components is proposed in the paper. At stage 1, the relay uses the magnitude of the positive-sequence component in the fault current to distinguish faults on a parallel WTG, connected to the same feeder......, or on an adjacent feeder from those on the connected feeder, on the collection bus, at an inter-tie or at a grid. For the former faults, the relay should remain stable and inoperative whilst the instantaneous or delayed tripping is required for the latter faults. At stage 2, the fault type is first evaluated using...... the relationships of the fault-generated symmetrical components. Then, the magnitude of the positive-sequence component in the fault current is used again to decide on either instantaneous or delayed operation. The operating performance of the relay is then verified using various fault scenarios modelled using...

  9. Optimal Design of Rectification Circuit in Electronic Circuit Fault Self-repair Based on EHW and RBT

    ZHANG Junbin; CAI Jinyan; MENG Yafeng

    2018-01-01

    Reliability of traditional electronic circuit is improved mainly by redundant fault-tolerant technol-ogy with large hardware resource consumption and limited fault self-repair capability. In complicated environment, electronic circuit faults appear easily. If on-site immedi-ate repair is not implemented, normal running of elec-tronic system will be directly affected. In order to solve these problems, Evolvable hardware (EHW) technology is widely used. The conventional EHW has some bottlenecks. The optimal design of Rectification circuit (RTC) is fur-ther researched on the basis of the previously proposed fault self-repair based on EHW and Reparation balance technology (RBT). Fault sets are selected by fault danger degree and fault coverage rate. The optimal designed RTC can completely repair faults in the fault set. Simulation re-sults prove that it has higher self-repair capability and less hardware resource.

  10. Statistical inference for extended or shortened phase II studies based on Simon's two-stage designs.

    Zhao, Junjun; Yu, Menggang; Feng, Xi-Ping

    2015-06-07

    Simon's two-stage designs are popular choices for conducting phase II clinical trials, especially in the oncology trials to reduce the number of patients placed on ineffective experimental therapies. Recently Koyama and Chen (2008) discussed how to conduct proper inference for such studies because they found that inference procedures used with Simon's designs almost always ignore the actual sampling plan used. In particular, they proposed an inference method for studies when the actual second stage sample sizes differ from planned ones. We consider an alternative inference method based on likelihood ratio. In particular, we order permissible sample paths under Simon's two-stage designs using their corresponding conditional likelihood. In this way, we can calculate p-values using the common definition: the probability of obtaining a test statistic value at least as extreme as that observed under the null hypothesis. In addition to providing inference for a couple of scenarios where Koyama and Chen's method can be difficult to apply, the resulting estimate based on our method appears to have certain advantage in terms of inference properties in many numerical simulations. It generally led to smaller biases and narrower confidence intervals while maintaining similar coverages. We also illustrated the two methods in a real data setting. Inference procedures used with Simon's designs almost always ignore the actual sampling plan. Reported P-values, point estimates and confidence intervals for the response rate are not usually adjusted for the design's adaptiveness. Proper statistical inference procedures should be used.

  11. Induced Voltages Ratio-Based Algorithm for Fault Detection, and Faulted Phase and Winding Identification of a Three-Winding Power Transformer

    Byung Eun Lee

    2014-09-01

    Full Text Available This paper proposes an algorithm for fault detection, faulted phase and winding identification of a three-winding power transformer based on the induced voltages in the electrical power system. The ratio of the induced voltages of the primary-secondary, primary-tertiary and secondary-tertiary windings is the same as the corresponding turns ratio during normal operating conditions, magnetic inrush, and over-excitation. It differs from the turns ratio during an internal fault. For a single phase and a three-phase power transformer with wye-connected windings, the induced voltages of each pair of windings are estimated. For a three-phase power transformer with delta-connected windings, the induced voltage differences are estimated to use the line currents, because the delta winding currents are practically unavailable. Six detectors are suggested for fault detection. An additional three detectors and a rule for faulted phase and winding identification are presented as well. The proposed algorithm can not only detect an internal fault, but also identify the faulted phase and winding of a three-winding power transformer. The various test results with Electromagnetic Transients Program (EMTP-generated data show that the proposed algorithm successfully discriminates internal faults from normal operating conditions including magnetic inrush and over-excitation. This paper concludes by implementing the algorithm into a prototype relay based on a digital signal processor.

  12. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    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.

  13. Model-Based Fault Detection and Isolation of a Liquid-Cooled Frequency Converter on a Wind Turbine

    Li, Peng; Odgaard, Peter Fogh; Stoustrup, Jakob

    2012-01-01

    advanced fault detection and isolation schemes. In this paper, an observer-based fault detection and isolation method for the cooling system in a liquid-cooled frequency converter on a wind turbine which is built up in a scalar version in the laboratory is presented. A dynamic model of the scale cooling...... system is derived based on energy balance equation. A fault analysis is conducted to determine the severity and occurrence rate of possible component faults and their end effects in the cooling system. A method using unknown input observer is developed in order to detect and isolate the faults based...... on the developed dynamical model. The designed fault detection and isolation algorithm is applied on a set of measured experiment data in which different faults are artificially introduced to the scaled cooling system. The experimental results conclude that the different faults are successfully detected...

  14. Spectrum-based Fault Localization in Embedded Software

    Abreu, R.

    2009-01-01

    Locating software components that are responsible for observed failures is a time-intensive and expensive phase in the software development cycle. Automatic fault localization techniques aid developers/testers in pinpointing the root cause of software failures, as such reducing the debugging effort.

  15. Stochastic Change Detection based on an Active Fault Diagnosis Approach

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2007-01-01

    The focus in this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow to obtain a fast change detection/isolation by considering the output or an err...

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

    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

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

    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.

  18. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

    Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

    2018-06-01

    This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  19. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    Busbait, Monther I.

    2016-03-24

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in networks. For bases containing networks with at most 10 edges, we find sharp coefficients for linear bounds.

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

    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)

  1. Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

    Qiaoning Yang

    2015-10-01

    Full Text Available In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.

  2. Fault Identification Algorithm Based on Zone-Division Wide Area Protection System

    Xiaojun Liu

    2014-04-01

    Full Text Available As the power grid becomes more magnified and complicated, wide-area protection system in the practical engineering application is more and more restricted by the communication level. Based on the concept of limitedness of wide-area protection system, the grid with complex structure is divided orderly in this paper, and fault identification and protection action are executed in each divided zone to reduce the pressure of the communication system. In protection zone, a new wide-area protection algorithm based on positive sequence fault components directional comparison principle is proposed. The special associated intelligent electronic devices (IEDs zones which contain buses and transmission lines are created according to the installation location of the IEDs. When a fault occurs, with the help of the fault information collecting and sharing from associated zones with the fault discrimination principle defined in this paper, the IEDs can identify the fault location and remove the fault according to the predetermined action strategy. The algorithm will not be impacted by the load changes and transition resistance and also has good adaptability in open phase running power system. It can be used as a main protection, and it also can be taken into account for the back-up protection function. The results of cases study show that, the division method of the wide-area protection system and the proposed algorithm are effective.

  3. A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

    Xue, Xiaoming; Zhou, Jianzhong

    2017-01-01

    To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Fooled by First Impressions? Reexamining the Diagnostic Value of Appearance-Based Inferences

    2010-01-01

    Abstract We often form opinions about the characteristics of others from single, static samples of their appearance --the very first thing we see when, or even before, we meet them. These inferences occur spontaneously, rapidly, and can impact decisions in a variety of important domains. A crucial question, then, is whether appearance-based inferences are accurate. Using a naturalistic data set of more than 1 million appearance-based judgments obtained from a popular website (Study...

  5. Fault Detection and Diagnosis System in Process industry Based on Big Data and WeChat

    Sun Zengqiang

    2017-01-01

    Full Text Available The fault detection and diagnosis information in process industry can be received, anytime and anywhere, based on bigdata and WeChat with mobile phone, which got rid of constraints that can only check Distributed Control System (DCS in the central control room or look over in office. Then, fault detection, diagnosis information sharing can be provided, and what’s more, fault detection alarm range, code and inform time can be personalized. The pressure of managers who worked on process industry can be release with the mobile information system.

  6. Fault diagnosis of main coolant pump in the nuclear power station based on the principal component analysis

    Feng Junting; Xu Mi; Wang Guizeng

    2003-01-01

    The fault diagnosis method based on principal component analysis is studied. The fault character direction storeroom of fifteen parameters abnormity is built in the simulation for the main coolant pump of nuclear power station. The measuring data are analyzed, and the results show that it is feasible for the fault diagnosis system of main coolant pump in the nuclear power station

  7. Design of uav robust autopilot based on adaptive neuro-fuzzy inference system

    Mohand Achour Touat

    2008-04-01

    Full Text Available  This paper is devoted to the application of adaptive neuro-fuzzy inference systems to the robust control of the UAV longitudinal motion. The adaptive neore-fuzzy inference system model needs to be trained by input/output data. This data were obtained from the modeling of a ”crisp” robust control system. The synthesis of this system is based on the separation theorem, which defines the structure and parameters of LQG-optimal controller, and further - robust optimization of this controller, based on the genetic algorithm. Such design procedure can define the rule base and parameters of fuzzyfication and defuzzyfication algorithms of the adaptive neore-fuzzy inference system controller, which ensure the robust properties of the control system. Simulation of the closed loop control system of UAV longitudinal motion with adaptive neore-fuzzy inference system controller demonstrates high efficiency of proposed design procedure.

  8. Condition-based fault tree analysis (CBFTA): A new method for improved fault tree analysis (FTA), reliability and safety calculations

    Shalev, Dan M.; Tiran, Joseph

    2007-01-01

    Condition-based maintenance methods have changed systems reliability in general and individual systems in particular. Yet, this change does not affect system reliability analysis. System fault tree analysis (FTA) is performed during the design phase. It uses components failure rates derived from available sources as handbooks, etc. Condition-based fault tree analysis (CBFTA) starts with the known FTA. Condition monitoring (CM) methods applied to systems (e.g. vibration analysis, oil analysis, electric current analysis, bearing CM, electric motor CM, and so forth) are used to determine updated failure rate values of sensitive components. The CBFTA method accepts updated failure rates and applies them to the FTA. The CBFTA recalculates periodically the top event (TE) failure rate (λ TE ) thus determining the probability of system failure and the probability of successful system operation-i.e. the system's reliability. FTA is a tool for enhancing system reliability during the design stages. But, it has disadvantages, mainly it does not relate to a specific system undergoing maintenance. CBFTA is tool for updating reliability values of a specific system and for calculating the residual life according to the system's monitored conditions. Using CBFTA, the original FTA is ameliorated to a practical tool for use during the system's field life phase, not just during system design phase. This paper describes the CBFTA method and its advantages are demonstrated by an example

  9. Spectral negentropy based sidebands and demodulation analysis for planet bearing fault diagnosis

    Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

    2017-12-01

    Planet bearing vibration signals are highly complex due to intricate kinematics (involving both revolution and spinning) and strong multiple modulations (including not only the fault induced amplitude modulation and frequency modulation, but also additional amplitude modulations due to load zone passing, time-varying vibration transfer path, and time-varying angle between the gear pair mesh lines of action and fault impact force vector), leading to difficulty in fault feature extraction. Rolling element bearing fault diagnosis essentially relies on detection of fault induced repetitive impulses carried by resonance vibration, but they are usually contaminated by noise and therefor are hard to be detected. This further adds complexity to planet bearing diagnostics. Spectral negentropy is able to reveal the frequency distribution of repetitive transients, thus providing an approach to identify the optimal frequency band of a filter for separating repetitive impulses. In this paper, we find the informative frequency band (including the center frequency and bandwidth) of bearing fault induced repetitive impulses using the spectral negentropy based infogram. In Fourier spectrum, we identify planet bearing faults according to sideband characteristics around the center frequency. For demodulation analysis, we filter out the sensitive component based on the informative frequency band revealed by the infogram. In amplitude demodulated spectrum (squared envelope spectrum) of the sensitive component, we diagnose planet bearing faults by matching the present peaks with the theoretical fault characteristic frequencies. We further decompose the sensitive component into mono-component intrinsic mode functions (IMFs) to estimate their instantaneous frequencies, and select a sensitive IMF with an instantaneous frequency fluctuating around the center frequency for frequency demodulation analysis. In the frequency demodulated spectrum (Fourier spectrum of instantaneous frequency) of

  10. Fault-tolerance techniques for SRAM-based FPGAs

    Kastensmidt, Fernanda Lima; Reis, Ricardo

    2006-01-01

    Fault-tolerance in integrated circuits is no longer the exclusive concern of space designers or highly-reliable applications engineers. Today, designers of many next-generation products must cope with reduced margin noises. The continuous evolution of fabrication technology of semiconductor components – shrinking transistor geometry, power supply, speed, and logic density – has significantly reduced the reliability of very deep submicron integrated circuits, in face of various internal and external sources of noise. Field Programmable Gate Arrays (FPGAs), customizable by SRAM cells, are the latest advance in the integrated circuit evolution: millions of memory cells to implement the logic, embedded memories, routing, and embedded microprocessors cores. These re-programmable systems-on-chip platforms must be fault-tolerant to cope with current requirements.

  11. Fault tolerant microcomputer based alarm annunciator for Dhruva reactor

    Chandra, A.K.

    1988-01-01

    The Dhruva alarm annunciator displays the status of 624 alarm points on an array of display windows using the standard ringback sequence. Recognizing the need for a very high availability, the system is implemented as a fault tolerant configuration. The annunciator is partitioned into three identical units; each unit is implemented using two microcomputers wired in a hot standby mode. In the event of one computer malfunctioning, the standby computer takes over control in a bouncefree transfer. The use of microprocessors has helped built-in flexibility in the system. The system also provides built-in capability to resolve the sequence of occurrence of events and conveys this information to another system for display on a CRT. This report describes the system features, fault tolerant organisation used and the hardware and software developed for the annunciation function. (author). 8 figs

  12. Rupture Dynamics and Seismic Radiation on Rough Faults for Simulation-Based PSHA

    Mai, P. M.; Galis, M.; Thingbaijam, K. K. S.; Vyas, J. C.; Dunham, E. M.

    2017-12-01

    Simulation-based ground-motion predictions may augment PSHA studies in data-poor regions or provide additional shaking estimations, incl. seismic waveforms, for critical facilities. Validation and calibration of such simulation approaches, based on observations and GMPE's, is important for engineering applications, while seismologists push to include the precise physics of the earthquake rupture process and seismic wave propagation in 3D heterogeneous Earth. Geological faults comprise both large-scale segmentation and small-scale roughness that determine the dynamics of the earthquake rupture process and its radiated seismic wavefield. We investigate how different parameterizations of fractal fault roughness affect the rupture evolution and resulting near-fault ground motions. Rupture incoherence induced by fault roughness generates realistic ω-2 decay for high-frequency displacement amplitude spectra. Waveform characteristics and GMPE-based comparisons corroborate that these rough-fault rupture simulations generate realistic synthetic seismogram for subsequent engineering application. Since dynamic rupture simulations are computationally expensive, we develop kinematic approximations that emulate the observed dynamics. Simplifying the rough-fault geometry, we find that perturbations in local moment tensor orientation are important, while perturbations in local source location are not. Thus, a planar fault can be assumed if the local strike, dip, and rake are maintained. The dynamic rake angle variations are anti-correlated with local dip angles. Based on a dynamically consistent Yoffe source-time function, we show that the seismic wavefield of the approximated kinematic rupture well reproduces the seismic radiation of the full dynamic source process. Our findings provide an innovative pseudo-dynamic source characterization that captures fault roughness effects on rupture dynamics. Including the correlations between kinematic source parameters, we present a new

  13. Design of passive fault-tolerant controllers of a quadrotor based on sliding mode theory

    Merheb Abdel-Razzak

    2015-09-01

    Full Text Available Abstract In this paper, sliding mode control is used to develop two passive fault tolerant controllers for an AscTec Pelican UAV quadrotor. In the first approach, a regular sliding mode controller (SMC augmented with an integrator uses the robustness property of variable structure control to tolerate partial actuator faults. The second approach is a cascaded sliding mode controller with an inner and outer SMC loops. In this configuration, faults are tolerated in the fast inner loop controlling the velocity system. Tuning the controllers to find the optimal values of the sliding mode controller gains is made using the ecological systems algorithm (ESA, a biologically inspired stochastic search algorithm based on the natural equilibrium of animal species. The controllers are tested using SIMULINK in the presence of two different types of actuator faults, partial loss of motor power affecting all the motors at once, and partial loss of motor speed. Results of the quadrotor following a continuous path demonstrated the effectiveness of the controllers, which are able to tolerate a significant number of actuator faults despite the lack of hardware redundancy in the quadrotor system. Tuning the controller using a faulty system improves further its ability to afford more severe faults. Simulation results show that passive schemes reserve their important role in fault tolerant control and are complementary to active techniques

  14. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

    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.

  15. Support vector machine based fault classification and location of a long transmission line

    Papia Ray

    2016-09-01

    Full Text Available This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400 kV, 300 km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21% and least fault distance estimation error (0.29%.

  16. Hydraulic Pump Fault Diagnosis Control Research Based on PARD-BP Algorithm

    LV Dongmei

    2014-12-01

    Full Text Available Combining working principle and failure mechanism of RZU2000HM hydraulic press, with its present fault cases being collected, the working principle of the oil pressure and faults phenomenon of the hydraulic power unit –swash-plate axial piston pump were studied with some emphasis, whose faults will directly affect the dynamic performance of the oil pressure and flow. In order to make hydraulic power unit work reliably, PARD-BP (Pruning Algorithm based Random Degree neural network fault algorithm was introduced, with swash-plate axial piston pump’s vibration fault sample data regarded as input, and fault mode matrix regarded as target output, so that PARD-BP algorithm could be trained. In the end, the vibration results were verified by the vibration modal test, and it was shown that the biggest upward peaks of vacuum pump in X-direction, Y-direction and Z- direction have fallen by 30.49 %, 21.13 % and 18.73 % respectively, so that the reliability of the fact that PARD-BP algorithm could be used for the online fault detection and diagnosis of the hydraulic pump was verified.

  17. A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis.

    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).

  18. Sensor fault diagnosis of aero-engine based on divided flight status.

    Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

    2017-11-01

    Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

  19. Sensor fault diagnosis of aero-engine based on divided flight status

    Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu

    2017-11-01

    Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.

  20. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

  1. Guideline for Bayesian Net based Software Fault Estimation Method for Reactor Protection System

    Eom, Heung Seop; Park, Gee Yong; Jang, Seung Cheol

    2011-01-01

    The purpose of this paper is to provide a preliminary guideline for the estimation of software faults in a safety-critical software, for example, reactor protection system's software. As the fault estimation method is based on Bayesian Net which intensively uses subjective probability and informal data, it is necessary to define formal procedure of the method to minimize the variability of the results. The guideline describes assumptions, limitations and uncertainties, and the product of the fault estimation method. The procedure for conducting a software fault-estimation method is then outlined, highlighting the major tasks involved. The contents of the guideline are based on our own experience and a review of research guidelines developed for a PSA

  2. Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance

    Detang Zeng

    2018-01-01

    Full Text Available It is essential to monitor and to diagnose faults in rotating machinery with a high thrust–weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.

  3. Performance Analysis of a Voltage Source Converter (VSC based HVDC Transmission System under Faulted Conditions

    Amiri RABIE

    2009-12-01

    Full Text Available Voltage Source Converter (VSC based HVDC transmission technology hasbeen selected as the basis for several recent projects due to its controllability,compact modular design, ease of system interface, and low environmentalimpact. This paper investigates the dynamic performance of a 200MW,±100kV VSC-HVDC transmission system under some faulted conditionsusing MATLAB/Simulink. Simulation results confirm the satisfactoryperformance of the proposed system under active and reactive powervariations and fault conditions.

  4. Development of a component centered fault monitoring and diagnosis knowledge based system for space power system

    Lee, S. C.; Lollar, Louis F.

    1988-01-01

    The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.

  5. Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific

    Sraj, Ihab; Zedler, Sarah E.; Knio, Omar; Jackson, Charles S.; Hoteit, Ibrahim

    2016-01-01

    The authors present a polynomial chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference

  6. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

    2016-02-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  7. [Early warning for various internal faults of GIS based on ultraviolet spectroscopy].

    Zhao, Yu; Wang, Xian-pei; Hu, Hong-hong; Dai, Dang-dang; Long, Jia-chuan; Tian, Meng; Zhu, Guo-wei; Huang, Yun-guang

    2015-02-01

    As the basis of accurate diagnosis, fault early-warning of gas insulation switchgear (GIS) focuses on the time-effectiveness and the applicability. It would be significant to research the method of unified early-warning for partial discharge (PD) and overheated faults in GIS. In the present paper, SO2 is proposed as the common and typical by-product. The unified monitoring could be achieved through ultraviolet spectroscopy (UV) detection of SO2. The derivative method and Savitzky-Golay filtering are employed for baseline correction and smoothing. The wavelength range of 290-310 nm is selected for quantitative detection of SO2. Through UV method, the spectral interference of SF6 and other complex by-products, e.g., SOF2 and SOF2, can be avoided and the features of trace SO2 in GIS can be extracted. The detection system is featured by compacted structure, low maintenance and satisfactory suitability in filed surveillance. By conducting SF6 decomposition experiments, including two types of PD faults and the overheated faults between 200-400 degrees C, the feasibility of proposed UV method has been verified. Fourier transform infrared spectroscopy and gas chromatography methods can be used for subsequent fault diagnosis. The different decomposition features in two kinds of faults are confirmed and the diagnosis strategy has been briefly analyzed. The main by-products under PD are SOF2 and SO2F2. The generated SO2 is significantly less than SOF2. More carbonous by-products will be generated when PD involves epoxy. By contrast, when the material of heater is stainless steel, SF6 decomposes at about 300 "C and the main by-products in overheated faults are SO2 and SO2F2. When heated over 350 degrees C, SO2 is generated much faster. SOz content stably increases when the GIS fault lasts. The faults types could be preliminarily identified based on the generation features of SO2.

  8. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    Zhang, Yan; Tang, Baoping; Chen, Rengxiang; Liu, Ziran

    2016-01-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  9. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition.

    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.

  10. FUZZY INFERENCE BASED LEAK ESTIMATION IN WATER PIPELINES SYSTEM

    N. Lavanya

    2015-01-01

    Full Text Available Pipeline networks are the most widely used mode for transporting fluids and gases around the world. Leakage in this pipeline causes harmful effects when the flowing fluid/gas is hazardous. Hence the detection of leak becomes essential to avoid/minimize such undesirable effects. This paper presents the leak detection by spectral analysis methods in a laboratory pipeline system. Transient in the pressure signal in the pipeline is created by opening and closing the exit valve. These pressure variations are captured and power spectrum is obtained by using Fast Fourier Transform (FFT method and Filter Diagonalization Method (FDM. The leaks at various positions are simulated and located using these methods and the results are compared. In order to determine the quantity of leak a 2 × 1 fuzzy inference system is created using the upstream and downstream pressure as input and the leak size as the output. Thus a complete leak detection, localization and quantification are done by using only the pressure variations in the pipeline.

  11. Fault Diagnosis of Car Engine by Using a Novel GA-Based Extension Recognition Method

    Meng-Hui Wang

    2014-01-01

    Full Text Available Due to the passenger’s security, the recognized hidden faults in car engines are the most important work for a maintenance engineer, so they can regulate the engines to be safe and improve the reliability of automobile systems. In this paper, we will present a novel fault recognition method based on the genetic algorithm (GA and the extension theory and also apply this method to the fault recognition of a practical car engine. The proposed recognition method has been tested on the Nissan Cefiro 2.0 engine and has also been compared to other traditional classification methods. Experimental results are of great effect regarding the hidden fault recognition of car engines, and the proposed method can also be applied to other industrial apparatus.

  12. Research on bearing fault diagnosis of large machinery based on mathematical morphology

    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.

  13. Design of on-board Bluetooth wireless network system based on fault-tolerant technology

    You, Zheng; Zhang, Xiangqi; Yu, Shijie; Tian, Hexiang

    2007-11-01

    In this paper, the Bluetooth wireless data transmission technology is applied in on-board computer system, to realize wireless data transmission between peripherals of the micro-satellite integrating electronic system, and in view of the high demand of reliability of a micro-satellite, a design of Bluetooth wireless network based on fault-tolerant technology is introduced. The reliability of two fault-tolerant systems is estimated firstly using Markov model, then the structural design of this fault-tolerant system is introduced; several protocols are established to make the system operate correctly, some related problems are listed and analyzed, with emphasis on Fault Auto-diagnosis System, Active-standby switch design and Data-Integrity process.

  14. Identification method of non-reflective faults based on index distribution of optical fibers.

    Lee, Wonkyoung; Myong, Seung Il; Lee, Jyung Chan; Lee, Sangsoo

    2014-01-13

    This paper investigates an identification method of non-reflective faults based on index distribution of optical fibers. The method identifies not only reflective faults but also non-reflective faults caused by tilted fiber-cut, lateral connector-misalignment, fiber-bend, and temperature variation. We analyze the reason why wavelength dependence of the fiber-bend is opposite to that of the lateral connector-misalignment, and the effect of loss due to temperature variation on OTDR waveforms through simulation and experimental results. This method can be realized by only upgrade of fault-analysis software without the hardware change, it is, therefore, competitive and cost-effective in passive optical networks.

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

    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.

  16. Adaptive Fault Tolerance for Many-Core Based Space-Borne Computing

    James, Mark; Springer, Paul; Zima, Hans

    2010-01-01

    This paper describes an approach to providing software fault tolerance for future deep-space robotic NASA missions, which will require a high degree of autonomy supported by an enhanced on-board computational capability. Such systems have become possible as a result of the emerging many-core technology, which is expected to offer 1024-core chips by 2015. We discuss the challenges and opportunities of this new technology, focusing on introspection-based adaptive fault tolerance that takes into account the specific requirements of applications, guided by a fault model. Introspection supports runtime monitoring of the program execution with the goal of identifying, locating, and analyzing errors. Fault tolerance assertions for the introspection system can be provided by the user, domain-specific knowledge, or via the results of static or dynamic program analysis. This work is part of an on-going project at the Jet Propulsion Laboratory in Pasadena, California.

  17. Identification of active fault using analysis of derivatives with vertical second based on gravity anomaly data (Case study: Seulimeum fault in Sumatera fault system)

    Hududillah, Teuku Hafid; Simanjuntak, Andrean V. H.; Husni, Muhammad

    2017-07-01

    Gravity is a non-destructive geophysical technique that has numerous application in engineering and environmental field like locating a fault zone. The purpose of this study is to spot the Seulimeum fault system in Iejue, Aceh Besar (Indonesia) by using a gravity technique and correlate the result with geologic map and conjointly to grasp a trend pattern of fault system. An estimation of subsurface geological structure of Seulimeum fault has been done by using gravity field anomaly data. Gravity anomaly data which used in this study is from Topex that is processed up to Free Air Correction. The step in the Next data processing is applying Bouger correction and Terrin Correction to obtain complete Bouger anomaly that is topographically dependent. Subsurface modeling is done using the Gav2DC for windows software. The result showed a low residual gravity value at a north half compared to south a part of study space that indicated a pattern of fault zone. Gravity residual was successfully correlate with the geologic map that show the existence of the Seulimeum fault in this study space. The study of earthquake records can be used for differentiating the active and non active fault elements, this gives an indication that the delineated fault elements are active.

  18. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs

    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

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

    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.

  20. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    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.

  1. Fault trees based on past accidents. Factorial analysis of events

    Vaillant, M.

    1977-01-01

    The method of the fault tree is already useful in the qualitative step before any reliability calculation. The construction of the tree becomes even simpler when we just want to describe how the events happened. Differently from screenplays that introduce several possibilities by means of the conjunction OR, you only have here the conjunction AND, which will not be written at all. This method is presented by INRS (1) for the study of industrial injuries; it may also be applied to material damages. (orig.) [de

  2. Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

    Steffen Haus

    2013-01-01

    Full Text Available Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed.A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA based on a Fast Fourier Transform (FFT. Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications.In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal.Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted with respect to the motor design. Physical interpretation are given in case the results differ

  3. Fault detection and isolation in systems with parametric faults

    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...

  4. Improved Data-based Fault Detection Strategy and Application to Distillation Columns

    Madakyaru, Muddu

    2017-01-31

    Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

  5. Improved Data-based Fault Detection Strategy and Application to Distillation Columns

    Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying

    2017-01-01

    Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

  6. Cultural effects on the association between election outcomes and face-based trait inferences.

    Chujun Lin

    Full Text Available How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants' inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the

  7. Cultural effects on the association between election outcomes and face-based trait inferences.

    Lin, Chujun; Adolphs, Ralph; Alvarez, R Michael

    2017-01-01

    How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants' inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures) correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the difficult question of

  8. Cultural effects on the association between election outcomes and face-based trait inferences

    Adolphs, Ralph; Alvarez, R. Michael

    2017-01-01

    How competent a politician looks, as assessed in the laboratory, is correlated with whether the politician wins in real elections. This finding has led many to investigate whether the association between candidate appearances and election outcomes transcends cultures. However, these studies have largely focused on European countries and Caucasian candidates. To the best of our knowledge, there are only four cross-cultural studies that have directly investigated how face-based trait inferences correlate with election outcomes across Caucasian and Asian cultures. These prior studies have provided some initial evidence regarding cultural differences, but methodological problems and inconsistent findings have complicated our understanding of how culture mediates the effects of candidate appearances on election outcomes. Additionally, these four past studies have focused on positive traits, with a relative neglect of negative traits, resulting in an incomplete picture of how culture may impact a broader range of trait inferences. To study Caucasian-Asian cultural effects with a more balanced experimental design, and to explore a more complete profile of traits, here we compared how Caucasian and Korean participants’ inferences of positive and negative traits correlated with U.S. and Korean election outcomes. Contrary to previous reports, we found that inferences of competence (made by participants from both cultures) correlated with both U.S. and Korean election outcomes. Inferences of open-mindedness and threat, two traits neglected in previous cross-cultural studies, were correlated with Korean but not U.S. election outcomes. This differential effect was found in trait judgments made by both Caucasian and Korean participants. Interestingly, the faster the participants made face-based trait inferences, the more strongly those inferences were correlated with real election outcomes. These findings provide new insights into cultural effects and the difficult question of

  9. Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

    Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

    2016-07-01

    Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  10. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

    Gang Ma

    2015-01-01

    Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

  11. Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

    Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil

    2016-01-01

    Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

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

    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.

  13. Optimum IMFs Selection Based Envelope Analysis of Bearing Fault Diagnosis in Plunger Pump

    Wenliao Du

    2016-01-01

    Full Text Available As the plunger pump always works in a complicated environment and the hydraulic cycle has an intrinsic fluid-structure interaction character, the fault information is submerged in the noise and the disturbance impact signals. For the fault diagnosis of the bearings in plunger pump, an optimum intrinsic mode functions (IMFs selection based envelope analysis was proposed. Firstly, the Wigner-Ville distribution was calculated for the acquired vibration signals, and the resonance frequency brought on by fault was obtained. Secondly, the empirical mode decomposition (EMD was employed for the vibration signal, and the optimum IMFs and the filter bandwidth were selected according to the Wigner-Ville distribution. Finally, the envelope analysis was utilized for the selected IMFs filtered by the band pass filter, and the fault type was recognized by compared with the bearing character frequencies. For the two modes, inner race fault and compound fault in the inner race and roller of rolling element bearing in plunger pump, the experiments show that a promising result is achieved.

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

    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.

  15. Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

    Junjie Lu

    2018-01-01

    Full Text Available Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughly understanding the components’ performance, which is described by component characteristic maps and the fault of each component can be regarded as the change of characteristic maps. In this paper, a novel method based on a Levenberg–Marquardt (LM algorithm is proposed to enhance the fidelity of the performance estimation and the credibility of the fault diagnosis for the turbofan engine. The presented method utilizes the LM algorithm to figure out the operating point in the characteristic maps, preparing for performance estimation and fault diagnosis. The accuracy of the proposed method is evaluated for estimating performance parameters in the transient case with Rayleigh process noise and Gaussian measurement noise. The comparison among the extended Kalman filter (EKF method, the particle filter (PF method and the proposed method is implemented in the abrupt fault case and the gradual degeneration case and it has been shown that the proposed method has the capability to lead to more accurate result for performance estimation and fault diagnosis of turbofan engine than current popular EKF and PF diagnosis methods.

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

    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. New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

    Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

    2018-01-24

    Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

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

    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.

  19. Adaptive extended-state observer-based fault tolerant attitude control for spacecraft with reaction wheels

    Ran, Dechao; Chen, Xiaoqian; de Ruiter, Anton; Xiao, Bing

    2018-04-01

    This study presents an adaptive second-order sliding control scheme to solve the attitude fault tolerant control problem of spacecraft subject to system uncertainties, external disturbances and reaction wheel faults. A novel fast terminal sliding mode is preliminarily designed to guarantee that finite-time convergence of the attitude errors can be achieved globally. Based on this novel sliding mode, an adaptive second-order observer is then designed to reconstruct the system uncertainties and the actuator faults. One feature of the proposed observer is that the design of the observer does not necessitate any priori information of the upper bounds of the system uncertainties and the actuator faults. In view of the reconstructed information supplied by the designed observer, a second-order sliding mode controller is developed to accomplish attitude maneuvers with great robustness and precise tracking accuracy. Theoretical stability analysis proves that the designed fault tolerant control scheme can achieve finite-time stability of the closed-loop system, even in the presence of reaction wheel faults and system uncertainties. Numerical simulations are also presented to demonstrate the effectiveness and superiority of the proposed control scheme over existing methodologies.

  20. Data-driven fault detection for industrial processes canonical correlation analysis and projection based methods

    Chen, Zhiwen

    2017-01-01

    Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementat...

  1. Improving the extraction of complex regulatory events from scientific text by using ontology-based inference.

    Kim, Jung-Jae; Rebholz-Schuhmann, Dietrich

    2011-10-06

    The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge. We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision) and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task. Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature.

  2. Improving the extraction of complex regulatory events from scientific text by using ontology-based inference

    Kim Jung-jae

    2011-10-01

    Full Text Available Abstract Background The extraction of complex events from biomedical text is a challenging task and requires in-depth semantic analysis. Previous approaches associate lexical and syntactic resources with ontologies for the semantic analysis, but fall short in testing the benefits from the use of domain knowledge. Results We developed a system that deduces implicit events from explicitly expressed events by using inference rules that encode domain knowledge. We evaluated the system with the inference module on three tasks: First, when tested against a corpus with manually annotated events, the inference module of our system contributes 53.2% of correct extractions, but does not cause any incorrect results. Second, the system overall reproduces 33.1% of the transcription regulatory events contained in RegulonDB (up to 85.0% precision and the inference module is required for 93.8% of the reproduced events. Third, we applied the system with minimum adaptations to the identification of cell activity regulation events, confirming that the inference improves the performance of the system also on this task. Conclusions Our research shows that the inference based on domain knowledge plays a significant role in extracting complex events from text. This approach has great potential in recognizing the complex concepts of such biomedical ontologies as Gene Ontology in the literature.

  3. Adjustable Parameter-Based Distributed Fault Estimation Observer Design for Multiagent Systems With Directed Graphs.

    Zhang, Ke; Jiang, Bin; Shi, Peng

    2017-02-01

    In this paper, a novel adjustable parameter (AP)-based distributed fault estimation observer (DFEO) is proposed for multiagent systems (MASs) with the directed communication topology. First, a relative output estimation error is defined based on the communication topology of MASs. Then a DFEO with AP is constructed with the purpose of improving the accuracy of fault estimation. Based on H ∞ and H 2 with pole placement, multiconstrained design is given to calculate the gain of DFEO. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed DFEO design with AP.

  4. Optimal design of RTCs in digital circuit fault self-repair based on global signal optimization

    Zhang Junbin; Cai Jinyan; Meng Yafeng

    2016-01-01

    Since digital circuits have been widely and thoroughly applied in various fields, electronic systems are increasingly more complicated and require greater reliability. Faults may occur in elec-tronic systems in complicated environments. If immediate field repairs are not made on the faults, elec-tronic systems will not run normally, and this will lead to serious losses. The traditional method for improving system reliability based on redundant fault-tolerant technique has been unable to meet the requirements. Therefore, on the basis of (evolvable hardware)-based and (reparation balance technology)-based electronic circuit fault self-repair strategy proposed in our preliminary work, the optimal design of rectification circuits (RTCs) in electronic circuit fault self-repair based on global sig-nal optimization is deeply researched in this paper. First of all, the basic theory of RTC optimal design based on global signal optimization is proposed. Secondly, relevant considerations and suitable ranges are analyzed. Then, the basic flow of RTC optimal design is researched. Eventually, a typical circuit is selected for simulation verification, and detailed simulated analysis is made on five circumstances that occur during RTC evolution. The simulation results prove that compared with the conventional design method based RTC, the global signal optimization design method based RTC is lower in hardware cost, faster in circuit evolution, higher in convergent precision, and higher in circuit evolution success rate. Therefore, the global signal optimization based RTC optimal design method applied in the elec-tronic circuit fault self-repair technology is proven to be feasible, effective, and advantageous.

  5. Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

    Runxia Guo

    2016-01-01

    Full Text Available The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

  6. Modeling and knowledge acquisition processes using case-based inference

    Ameneh Khadivar

    2017-03-01

    Full Text Available The method of acquisition and presentation of the organizational Process Knowledge has considered by many KM researches. In this research a model for process knowledge acquisition and presentation has been presented by using the approach of Case Base Reasoning. The validation of the presented model was evaluated by conducting an expert panel. Then a software has been developed based on the presented model and implemented in Eghtesad Novin Bank of Iran. In this company, based on the stages of the presented model, first the knowledge intensive processes has been identified, then the Process Knowledge was stored in a knowledge base in the format of problem/solution/consequent .The retrieval of the knowledge was done based on the similarity of the nearest neighbor algorithm. For validating of the implemented system, results of the system has compared by the results of the decision making of the expert of the process.

  7. Inferring Trust Relationships in Web-Based Social Networks

    Golbeck, Jennifer; Hendler, James

    2006-01-01

    The growth of web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences...

  8. Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

    Harrou, Fouzi

    2017-03-18

    Fault detection has a vital role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. This paper proposes an innovative multivariate fault detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, to enhance further the robustness of these methods to measurement noise, and reduce the false alarms due to modeling errors, wavelet-based multiscale filtering of residuals is used before the application of the HD-based monitoring scheme. The performances of the developed NLPLS-HD fault detection technique is illustrated using simulated plug flow reactor data. The results show that the proposed method provides favorable performance for detection of faults compared to the conventional NLPLS method.

  9. Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

    Ding, Steven X

    2013-01-01

    Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...

  10. Fault feature analysis of cracked gear based on LOD and analytical-FE method

    Wu, Jiateng; Yang, Yu; Yang, Xingkai; Cheng, Junsheng

    2018-01-01

    At present, there are two main ideas for gear fault diagnosis. One is the model-based gear dynamic analysis; the other is signal-based gear vibration diagnosis. In this paper, a method for fault feature analysis of gear crack is presented, which combines the advantages of dynamic modeling and signal processing. Firstly, a new time-frequency analysis method called local oscillatory-characteristic decomposition (LOD) is proposed, which has the attractive feature of extracting fault characteristic efficiently and accurately. Secondly, an analytical-finite element (analytical-FE) method which is called assist-stress intensity factor (assist-SIF) gear contact model, is put forward to calculate the time-varying mesh stiffness (TVMS) under different crack states. Based on the dynamic model of the gear system with 6 degrees of freedom, the dynamic simulation response was obtained for different tooth crack depths. For the dynamic model, the corresponding relation between the characteristic parameters and the degree of the tooth crack is established under a specific condition. On the basis of the methods mentioned above, a novel gear tooth root crack diagnosis method which combines the LOD with the analytical-FE is proposed. Furthermore, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are contrasted with the LOD by gear crack fault vibration signals. The analysis results indicate that the proposed method performs effectively and feasibility for the tooth crack stiffness calculation and the gear tooth crack fault diagnosis.

  11. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data.

    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.

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

    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.

  13. Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data

    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

  14. Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.

    Jia, Bin; Wang, Xiaodong

    2013-12-17

    : The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

  15. Statistical inference for remote sensing-based estimates of net deforestation

    Ronald E. McRoberts; Brian F. Walters

    2012-01-01

    Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because...

  16. Improved fault ride through capability of DFIG based wind turbines using synchronous reference frame control based dynamic voltage restorer.

    Rini Ann Jerin, A; Kaliannan, Palanisamy; Subramaniam, Umashankar

    2017-09-01

    Fault ride through (FRT) capability in wind turbines to maintain the grid stability during faults has become mandatory with the increasing grid penetration of wind energy. Doubly fed induction generator based wind turbine (DFIG-WT) is the most popularly utilized type of generator but highly susceptible to the voltage disturbances in grid. Dynamic voltage restorer (DVR) based external FRT capability improvement is considered. Since DVR is capable of providing fast voltage sag mitigation during faults and can maintain the nominal operating conditions for DFIG-WT. The effectiveness of the DVR using Synchronous reference frame (SRF) control is investigated for FRT capability in DFIG-WT during both balanced and unbalanced fault conditions. The operation of DVR is confirmed using time-domain simulation in MATLAB/Simulink using 1.5MW DFIG-WT. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm

    Wei Huang

    2013-01-01

    Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.

  18. FaultBuster: data driven fault detection and diagnosis for industrial systems

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    . Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while...

  19. Model-based fault detection for proton exchange membrane fuel cell ...

    In this paper, an intelligent model-based fault detection (FD) is developed for proton exchange membrane fuel cell (PEMFC) dynamic systems using an independent radial basis function (RBF) networks. The novelty is that this RBF networks is used to model the PEMFC dynamic systems and residuals are generated based ...

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

    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.

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

    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.

  2. Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest.

    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.

  3. Online model-based fault detection for grid connected PV systems monitoring

    Harrou, Fouzi

    2017-12-14

    This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  4. Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics

    Ahmed TOUMI

    2009-12-01

    Full Text Available Self-Organizing Maps (SOM is an excellent method of analyzingmultidimensional data. The SOM based classification is attractive, due to itsunsupervised learning and topology preserving properties. In this paper, theperformance of the self-organizing methods is investigated in induction motorrotor fault detection and severity evaluation. The SOM is based on motor currentsignature analysis (MCSA. The agglomerative hierarchical algorithms using theWard’s method is applied to automatically dividing the map into interestinginterpretable groups of map units that correspond to clusters in the input data. Theresults obtained with this approach make it possible to detect a rotor bar fault justdirectly from the visualization results. The system is also able to estimate theextent of rotor faults.

  5. Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train

    Hehong Zhang

    2015-01-01

    Full Text Available A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.

  6. Online model-based fault detection for grid connected PV systems monitoring

    Harrou, Fouzi; Sun, Ying; Saidi, Ahmed

    2017-01-01

    This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.

  7. Fault diagnosis for tilting-pad journal bearing based on SVD and LMD

    Zhang Xiaotao

    2016-01-01

    Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.

  8. A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

    Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

    2017-01-01

    In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  9. Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

    Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

    2006-01-01

    High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

  10. New developments of a knowledge based system (VEG) for inferring vegetation characteristics

    Kimes, D. S.; Harrison, P. A.; Harrison, P. R.

    1992-01-01

    An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).

  11. A Fault Diagnosis Model of Surface to Air Missile Equipment Based on Wavelet Transformation and Support Vector Machine

    Zhheng Ni

    2016-01-01

    Full Text Available At present, the fault signals of surface to air missile equipment are hard to collect and the accuracy of fault diagnosis is very low. To solve the above problems, based on the superiority of wavelet transformation on processing non-stationary signals and the advantage of SVM on pattern classification, this paper proposes a fault diagnosis model and takes the typical analog circuit diagnosis of one power distribution system as an example to verify the fault diagnosis model based on Wavelet Transformation and SVM. The simulation results show that the model is able to achieve fault diagnosis based on a small amount of training samples, which improves the accuracy of fault diagnosis.

  12. A Novel Wide-Area Backup Protection Based on Fault Component Current Distribution and Improved Evidence Theory

    Zhe Zhang

    2014-01-01

    Full Text Available In order to solve the problems of the existing wide-area backup protection (WABP algorithms, the paper proposes a novel WABP algorithm based on the distribution characteristics of fault component current and improved Dempster/Shafer (D-S evidence theory. When a fault occurs, slave substations transmit to master station the amplitudes of fault component currents of transmission lines which are the closest to fault element. Then master substation identifies suspicious faulty lines according to the distribution characteristics of fault component current. After that, the master substation will identify the actual faulty line with improved D-S evidence theory based on the action states of traditional protections and direction components of these suspicious faulty lines. The simulation examples based on IEEE 10-generator-39-bus system show that the proposed WABP algorithm has an excellent performance. The algorithm has low requirement of sampling synchronization, small wide-area communication flow, and high fault tolerance.

  13. A Novel Wide-Area Backup Protection Based on Fault Component Current Distribution and Improved Evidence Theory

    Zhang, Zhe; Kong, Xiangping; Yin, Xianggen; Yang, Zengli; Wang, Lijun

    2014-01-01

    In order to solve the problems of the existing wide-area backup protection (WABP) algorithms, the paper proposes a novel WABP algorithm based on the distribution characteristics of fault component current and improved Dempster/Shafer (D-S) evidence theory. When a fault occurs, slave substations transmit to master station the amplitudes of fault component currents of transmission lines which are the closest to fault element. Then master substation identifies suspicious faulty lines according to the distribution characteristics of fault component current. After that, the master substation will identify the actual faulty line with improved D-S evidence theory based on the action states of traditional protections and direction components of these suspicious faulty lines. The simulation examples based on IEEE 10-generator-39-bus system show that the proposed WABP algorithm has an excellent performance. The algorithm has low requirement of sampling synchronization, small wide-area communication flow, and high fault tolerance. PMID:25050399

  14. Data-based Non-Markovian Model Inference

    Ghil, Michael

    2015-04-01

    This talk concentrates on obtaining stable and efficient data-based models for simulation and prediction in the geosciences and life sciences. The proposed model derivation relies on using a multivariate time series of partial observations from a large-dimensional system, and the resulting low-order models are compared with the optimal closures predicted by the non-Markovian Mori-Zwanzig formalism of statistical physics. Multilayer stochastic models (MSMs) are introduced as both a very broad generalization and a time-continuous limit of existing multilevel, regression-based approaches to data-based closure, in particular of empirical model reduction (EMR). We show that the multilayer structure of MSMs can provide a natural Markov approximation to the generalized Langevin equation (GLE) of the Mori-Zwanzig formalism. A simple correlation-based stopping criterion for an EMR-MSM model is derived to assess how well it approximates the GLE solution. Sufficient conditions are given for the nonlinear cross-interactions between the constitutive layers of a given MSM to guarantee the existence of a global random attractor. This existence ensures that no blow-up can occur for a very broad class of MSM applications. The EMR-MSM methodology is first applied to a conceptual, nonlinear, stochastic climate model of coupled slow and fast variables, in which only slow variables are observed. The resulting reduced model with energy-conserving nonlinearities captures the main statistical features of the slow variables, even when there is no formal scale separation and the fast variables are quite energetic. Second, an MSM is shown to successfully reproduce the statistics of a partially observed, generalized Lokta-Volterra model of population dynamics in its chaotic regime. The positivity constraint on the solutions' components replaces here the quadratic-energy-preserving constraint of fluid-flow problems and it successfully prevents blow-up. This work is based on a close

  15. Sensor-based activity recognition using extended belief rule-based inference methodology.

    Calzada, A; Liu, J; Nugent, C D; Wang, H; Martinez, L

    2014-01-01

    The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for sensor-based activity recognition. Although RIMER+ has been designed as a generic decision model that could be applied in a wide range of situations, this paper discusses how this methodology can be adapted to recognize human activities using binary sensors within smart environments. The evaluation of RIMER+ against other state-of-the-art classifiers in terms of accuracy, efficiency and applicability was found to be significantly relevant, specially in situations of input data incompleteness, and it demonstrates the potential of this methodology and underpins the basis to develop further research on the topic.

  16. Minimizing student’s faults in determining the design of experiment through inquiry-based learning

    Nilakusmawati, D. P. E.; Susilawati, M.

    2017-10-01

    The purpose of this study were to describe the used of inquiry method in an effort to minimize student’s fault in designing an experiment and to determine the effectiveness of the implementation of the inquiry method in minimizing student’s faults in designing experiments on subjects experimental design. This type of research is action research participants, with a model of action research design. The data source were students of the fifth semester who took a subject of experimental design at Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University. Data was collected through tests, interviews, and observations. The hypothesis was tested by t-test. The result showed that the implementation of inquiry methods to minimize of students fault in designing experiments, analyzing experimental data, and interpret them in cycle 1 students can reduce fault by an average of 10.5%. While implementation in Cycle 2, students managed to reduce fault by an average of 8.78%. Based on t-test results can be concluded that the inquiry method effectively used to minimize of student’s fault in designing experiments, analyzing experimental data, and interpreting them. The nature of the teaching materials on subject of Experimental Design that demand the ability of students to think in a systematic, logical, and critical in analyzing the data and interpret the test cases makes the implementation of this inquiry become the proper method. In addition, utilization learning tool, in this case the teaching materials and the students worksheet is one of the factors that makes this inquiry method effectively minimizes of student’s fault when designing experiments.

  17. Likelihood-Based Inference in Nonlinear Error-Correction Models

    Kristensen, Dennis; Rahbæk, Anders

    We consider a class of vector nonlinear error correction models where the transfer function (or loadings) of the stationary relation- ships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties...... and a linear trend in general. Gaussian likelihood-based estimators are considered for the long- run cointegration parameters, and the short-run parameters. Asymp- totic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normaity can be found. A simulation study...

  18. Sensitivity analysis practices: Strategies for model-based inference

    Saltelli, Andrea; Ratto, Marco; Tarantola, Stefano; Campolongo, Francesca

    2006-01-01

    Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA

  19. Sensitivity analysis practices: Strategies for model-based inference

    Saltelli, Andrea [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (Vatican City State, Holy See,) (Italy)]. E-mail: andrea.saltelli@jrc.it; Ratto, Marco [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Tarantola, Stefano [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy); Campolongo, Francesca [Institute for the Protection and Security of the Citizen (IPSC), European Commission, Joint Research Centre, TP 361, 21020 Ispra (VA) (Italy)

    2006-10-15

    Fourteen years after Science's review of sensitivity analysis (SA) methods in 1989 (System analysis at molecular scale, by H. Rabitz) we search Science Online to identify and then review all recent articles having 'sensitivity analysis' as a keyword. In spite of the considerable developments which have taken place in this discipline, of the good practices which have emerged, and of existing guidelines for SA issued on both sides of the Atlantic, we could not find in our review other than very primitive SA tools, based on 'one-factor-at-a-time' (OAT) approaches. In the context of model corroboration or falsification, we demonstrate that this use of OAT methods is illicit and unjustified, unless the model under analysis is proved to be linear. We show that available good practices, such as variance based measures and others, are able to overcome OAT shortcomings and easy to implement. These methods also allow the concept of factors importance to be defined rigorously, thus making the factors importance ranking univocal. We analyse the requirements of SA in the context of modelling, and present best available practices on the basis of an elementary model. We also point the reader to available recipes for a rigorous SA.

  20. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

  1. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    Jian Ma

    Full Text Available The aircraft environmental control system (ECS is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

  2. V and V based Fault Estimation Method for Safety-Critical Software using BNs

    Eom, Heung Seop; Park, Gee Yong; Jang, Seung Cheol; Kang, Hyun Gook

    2011-01-01

    Quantitative software reliability measurement approaches have severe limitations in demonstrating the proper level of reliability for safety-critical software. These limitations can be overcome by using some other means of assessment. One of the promising candidates is based on the quality of the software development. Particularly in the nuclear industry, regulatory bodies in most countries do not accept the concept of quantitative goals as a sole means of meeting their regulations for the reliability of digital computers in NPPs, and use deterministic criteria for both hardware and software. The point of deterministic criteria is to assess the whole development process and its related activities during the software development life cycle for the acceptance of safety-critical software, and software V and V plays an important role in this process. In this light, we studied a V and V based fault estimation method using Bayesian Nets (BNs) to assess the reliability of safety-critical software, especially reactor protection system software in a NPP. The BNs in the study were made for an estimation of software faults and were based on the V and V frame, which governs the development of safety-critical software in the nuclear field. A case study was carried out for a reactor protection system that was developed as a part of the Korea Nuclear Instrumentation and Control System. The insight from the case study is that some important factors affecting the fault number of the target software include the residual faults in the system specification, maximum number of faults introduced in the development phase, ratio between process/function characteristic, uncertainty sizing, and fault elimination rate by inspection activities

  3. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

    Hao Sun

    2014-01-01

    Full Text Available The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO is proposed. Geronimo, Hardin, and Massopust (GHM multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

  4. Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles

    Peng Liu

    2018-01-01

    Full Text Available The battery is a key component and the major fault source in electric vehicles (EVs. Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.

  5. Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

    Xinghui Zhang

    2015-01-01

    Full Text Available Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

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

    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.

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

    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

  8. Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method

    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.

  9. An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

    Xiaofeng Lv

    2018-01-01

    Full Text Available Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR and fault isolation rate (FIR. From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.

  10. Fault Diagnosis of a Reconfigurable Crawling–Rolling Robot Based on Support Vector Machines

    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.

  11. Fault tolerant control for uncertain systems with parametric faults

    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....

  12. Effect of age on variability in the production of text-based global inferences.

    Williams, Lynne J; Dunlop, Joseph P; Abdi, Hervé

    2012-01-01

    As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence). However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence). The current study investigated whether age increased the variability in text based global inference generation--a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years), middle-old (70 to 76 years) and old-old (77 to 94 years) adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging.

  13. Effect of age on variability in the production of text-based global inferences.

    Lynne J Williams

    Full Text Available As we age, our differences in cognitive skills become more visible, an effect especially true for memory and problem solving skills (i.e., fluid intelligence. However, by contrast with fluid intelligence, few studies have examined variability in measures that rely on one's world knowledge (i.e., crystallized intelligence. The current study investigated whether age increased the variability in text based global inference generation--a measure of crystallized intelligence. Global inference generation requires the integration of textual information and world knowledge and can be expressed as a gist or lesson. Variability in generating two global inferences for a single text was examined in young-old (62 to 69 years, middle-old (70 to 76 years and old-old (77 to 94 years adults. The older two groups showed greater variability, with the middle elderly group being most variable. These findings suggest that variability may be a characteristic of both fluid and crystallized intelligence in aging.

  14. Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

    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.

  15. Rolling bearing fault diagnosis based on information fusion using Dempster-Shafer evidence theory

    Pei, Di; Yue, Jianhai; Jiao, Jing

    2017-10-01

    This paper presents a fault diagnosis method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing vibration data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing fault diagnosis accuracy compared with traditional signal analysis methods.

  16. Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

    Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling

    2016-07-01

    To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. Vibration signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the fault. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering fault templates are used to perform the image matching.

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

    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.

  18. Voltage Based Detection Method for High Impedance Fault in a Distribution System

    Thomas, Mini Shaji; Bhaskar, Namrata; Prakash, Anupama

    2016-09-01

    High-impedance faults (HIFs) on distribution feeders cannot be detected by conventional protection schemes, as HIFs are characterized by their low fault current level and waveform distortion due to the nonlinearity of the ground return path. This paper proposes a method to identify the HIFs in distribution system and isolate the faulty section, to reduce downtime. This method is based on voltage measurements along the distribution feeder and utilizes the sequence components of the voltages. Three models of high impedance faults have been considered and source side and load side breaking of the conductor have been studied in this work to capture a wide range of scenarios. The effect of neutral grounding of the source side transformer is also accounted in this study. The results show that the algorithm detects the HIFs accurately and rapidly. Thus, the faulty section can be isolated and service can be restored to the rest of the consumers.

  19. Advanced neural network-based computational schemes for robust fault diagnosis

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  20. A Novel Fault Line Selection Method Based on Improved Oscillator System of Power Distribution Network

    Xiaowei Wang

    2014-01-01

    Full Text Available A novel method of fault line selection based on IOS is presented. Firstly, the IOS is established by using math model, which adopted TZSC signal to replace built-in signal of duffing chaotic oscillator by selecting appropriate parameters. Then, each line’s TZSC decomposed by db10 wavelet packet to get CFB with the maximum energy principle, and CFB was solved by IOS. Finally, maximum chaotic distance and average chaotic distance on the phase trajectory are used to judge fault line. Simulation results show that the proposed method can accurately judge fault line and healthy line in strong noisy background. Besides, the nondetection zones of proposed method are elaborated.

  1. Fault diagnosis

    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

  2. The Effect of Simulation-Based Learning on Prospective Teachers' Inference Skills in Teaching Probability

    Koparan, Timur; Yilmaz, Gül Kaleli

    2015-01-01

    The effect of simulation-based probability teaching on the prospective teachers' inference skills has been examined with this research. In line with this purpose, it has been aimed to examine the design, implementation and efficiency of a learning environment for experimental probability. Activities were built on modeling, simulation and the…

  3. Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training

    Baschera, Gian-Marco; Gross, Markus

    2010-01-01

    We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…

  4. The Development of Adaptive Decision Making: Recognition-Based Inference in Children and Adolescents

    Horn, Sebastian S.; Ruggeri, Azzurra; Pachur, Thorsten

    2016-01-01

    Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment,…

  5. Preverbal Infants Infer Third-Party Social Relationships Based on Language

    Liberman, Zoe; Woodward, Amanda L.; Kinzler, Katherine D.

    2017-01-01

    Language provides rich social information about its speakers. For instance, adults and children make inferences about a speaker's social identity, geographic origins, and group membership based on her language and accent. Although infants prefer speakers of familiar languages (Kinzler, Dupoux, & Spelke, 2007), little is known about the…

  6. Fault diagnosis model for power transformers based on information fusion

    Dong, Ming; Yan, Zhang; Yang, Li; Judd, Martin D.

    2005-07-01

    Methods used to assess the insulation status of power transformers before they deteriorate to a critical state include dissolved gas analysis (DGA), partial discharge (PD) detection and transfer function techniques, etc. All of these approaches require experience in order to correctly interpret the observations. Artificial intelligence (AI) is increasingly used to improve interpretation of the individual datasets. However, a satisfactory diagnosis may not be obtained if only one technique is used. For example, the exact location of PD cannot be predicted if only DGA is performed. However, using diverse methods may result in different diagnosis solutions, a problem that is addressed in this paper through the introduction of a fuzzy information infusion model. An inference scheme is proposed that yields consistent conclusions and manages the inherent uncertainty in the various methods. With the aid of information fusion, a framework is established that allows different diagnostic tools to be combined in a systematic way. The application of information fusion technique for insulation diagnostics of transformers is proved promising by means of examples.

  7. Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

    Hongzhi Hu

    2015-01-01

    Full Text Available This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in three-dimensional (3D complex space is proposed, which achieves a far better fault detection ratio (FDR against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT method. By adding redundant bypassing-components in the circuit under test (CUT, this method achieves excellent fault isolation ratio (FIR in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.

  8. Software fault detection and recovery in critical real-time systems: An approach based on loose coupling

    Alho, Pekka; Mattila, Jouni

    2014-01-01

    Highlights: •We analyze fault tolerance in mission-critical real-time systems. •Decoupled architectural model can be used to implement fault tolerance. •Prototype implementation for remote handling control system and service manager. •Recovery from transient faults by restarting services. -- Abstract: Remote handling (RH) systems are used to inspect, make changes to, and maintain components in the ITER machine and as such are an example of mission-critical system. Failure in a critical system may cause damage, significant financial losses and loss of experiment runtime, making dependability one of their most important properties. However, even if the software for RH control systems has been developed using best practices, the system might still fail due to undetected faults (bugs), hardware failures, etc. Critical systems therefore need capability to tolerate faults and resume operation after their occurrence. However, design of effective fault detection and recovery mechanisms poses a challenge due to timeliness requirements, growth in scale, and complex interactions. In this paper we evaluate effectiveness of service-oriented architectural approach to fault tolerance in mission-critical real-time systems. We use a prototype implementation for service management with an experimental RH control system and industrial manipulator. The fault tolerance is based on using the high level of decoupling between services to recover from transient faults by service restarts. In case the recovery process is not successful, the system can still be used if the fault was not in a critical software module

  9. Software fault detection and recovery in critical real-time systems: An approach based on loose coupling

    Alho, Pekka, E-mail: pekka.alho@tut.fi; Mattila, Jouni

    2014-10-15

    Highlights: •We analyze fault tolerance in mission-critical real-time systems. •Decoupled architectural model can be used to implement fault tolerance. •Prototype implementation for remote handling control system and service manager. •Recovery from transient faults by restarting services. -- Abstract: Remote handling (RH) systems are used to inspect, make changes to, and maintain components in the ITER machine and as such are an example of mission-critical system. Failure in a critical system may cause damage, significant financial losses and loss of experiment runtime, making dependability one of their most important properties. However, even if the software for RH control systems has been developed using best practices, the system might still fail due to undetected faults (bugs), hardware failures, etc. Critical systems therefore need capability to tolerate faults and resume operation after their occurrence. However, design of effective fault detection and recovery mechanisms poses a challenge due to timeliness requirements, growth in scale, and complex interactions. In this paper we evaluate effectiveness of service-oriented architectural approach to fault tolerance in mission-critical real-time systems. We use a prototype implementation for service management with an experimental RH control system and industrial manipulator. The fault tolerance is based on using the high level of decoupling between services to recover from transient faults by service restarts. In case the recovery process is not successful, the system can still be used if the fault was not in a critical software module.

  10. Geodetic Finite-Fault-based Earthquake Early Warning Performance for Great Earthquakes Worldwide

    Ruhl, C. J.; Melgar, D.; Grapenthin, R.; Allen, R. M.

    2017-12-01

    GNSS-based earthquake early warning (EEW) algorithms estimate fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because large events are infrequent, algorithms are not regularly exercised and insufficiently tested on few available datasets. The Geodetic Alarm System (G-larmS) is a GNSS-based finite-fault algorithm developed as part of the ShakeAlert EEW system in the western US. Performance evaluations using synthetic earthquakes offshore Cascadia showed that G-larmS satisfactorily recovers magnitude and fault length, providing useful alerts 30-40 s after origin time and timely warnings of ground motion for onshore urban areas. An end-to-end test of the ShakeAlert system demonstrated the need for GNSS data to accurately estimate ground motions in real-time. We replay real data from several subduction-zone earthquakes worldwide to demonstrate the value of GNSS-based EEW for the largest, most damaging events. We compare predicted ground acceleration (PGA) from first-alert-solutions with those recorded in major urban areas. In addition, where applicable, we compare observed tsunami heights to those predicted from the G-larmS solutions. We show that finite-fault inversion based on GNSS-data is essential to achieving the goals of EEW.

  11. Fast architecture-level synthesis of fault-tolerant flow-based microfluidic biochips

    Huang, Wei Lun; Gupta, Ankur; Roy, Sudip

    2017-01-01

    Microfluidic-based lab-on-a-chips have emerged as a popular technology for implementation of different biochemical test protocols used in medical diagnostics. However, in the manufacturing process or during operation of such chips, some faults may occur that leads to damage of the chip, which...

  12. Architecture Synthesis for Cost-Constrained Fault-Tolerant Flow-based Biochips

    Eskesen, Morten Chabert; Pop, Paul; Potluri, Seetal

    2016-01-01

    . This increase in fabrication complexity has led to an increase in defect rates during the manufacturing, thereby motivating the need to improve the yield, by designing these biochips such that they are fault tolerant. We propose an approach based on a Greedy Randomized Adaptive Search Procedure (GRASP...

  13. Sliding observer-based demagnetisation fault-tolerant control in permanent magnet synchronous motors

    Changfan Zhang

    2017-04-01

    Full Text Available This study proposes a fault-tolerant control method for permanent magnet synchronous motors (PMSMs based on the active flux linkage concept, which addresses permanent magnet (PM demagnetisation faults in PMSMs. First, a mathematical model for a PMSM is established based on active flux linkage, and then the effect of PM demagnetisation on the PMSM is analysed. Second, the stator current in the static coordinate is set as the state variable, an observer is designed based on a sliding-mode variable structure, and an equation for active flux linkage is established for dynamic estimation based on the equivalent control principle of sliding-mode variable structure. Finally, the active flux linkage for the next moment is predicted according to the operating conditions of the motor and the observed values of the current active flux linkage. The deadbeat control strategy is applied to eliminate errors in the active flux linkage and realise the objective of fault-tolerant control. A timely and effective control for demagnetisation faults is achieved using the proposed method, which validity and feasibility are verified by the simulation and experiment results.

  14. Fault current contribution from VSC-based wind turbines to the grid

    Valentini, Massimo; Akhmatov, Vladislav; Iov, Florin

    2008-01-01

    current injections during the fault. In this paper an equivalent VSC-based wind turbine model for short-circuit calculations at steady-state conditions is developed and presented. The model is implemented in DigSILENT PowerFactory using the DPL-Programming Language. The developed wind turbine model...

  15. Continuous Record Laplace-based Inference about the Break Date in Structural Change Models

    Casini, Alessandro; Perron, Pierre

    2018-01-01

    Building upon the continuous record asymptotic framework recently introduced by Casini and Perron (2017a) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change. The procedure relies on a Laplace-type estimator defined by an integration-based rather than an optimization-based method. A transformation of the leastsquares criterion function is evaluated in order to ...

  16. A 3D modeling approach to complex faults with multi-source data

    Wu, Qiang; Xu, Hua; Zou, Xukai; Lei, Hongzhuan

    2015-04-01

    Fault modeling is a very important step in making an accurate and reliable 3D geological model. Typical existing methods demand enough fault data to be able to construct complex fault models, however, it is well known that the available fault data are generally sparse and undersampled. In this paper, we propose a workflow of fault modeling, which can integrate multi-source data to construct fault models. For the faults that are not modeled with these data, especially small-scale or approximately parallel with the sections, we propose the fault deduction method to infer the hanging wall and footwall lines after displacement calculation. Moreover, using the fault cutting algorithm can supplement the available fault points on the location where faults cut each other. Increasing fault points in poor sample areas can not only efficiently construct fault models, but also reduce manual intervention. By using a fault-based interpolation and remeshing the horizons, an accurate 3D geological model can be constructed. The method can naturally simulate geological structures no matter whether the available geological data are sufficient or not. A concrete example of using the method in Tangshan, China, shows that the method can be applied to broad and complex geological areas.

  17. Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

    Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

    2017-11-01

    Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

  18. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  19. A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

    Xiao-hui He

    2016-01-01

    Full Text Available To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM. Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

  20. Fault diagnosis in spur gears based on genetic algorithm and random forest

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  1. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

    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.

  2. NN-Es Fault Diagnosis Method in Nuclear Power Equipment Based on Concept Lattice

    Liu Yongkuo; Xie Chunli; Xia Hong

    2010-01-01

    In order to improve the fault diagnosis accuracy of nuclear power plant,neural network and expert systems were combined to give full play to their advantages. In this paper, the concept lattice was applied to get the object properties, extracting the core attributes, dispensable attributes and relative necessary attributes from a large number raw data of fault symptoms.Based on these attributes, neural networks with different levels of importance were designed to improve the learning speed and diagnosis accuracy, and the diagnosis results of the neural networks were verified by using rule-based reasoning expert system. To verify the accuracy of this method, some simulation experiments about the typical faults of nuclear power plant were conducted. And the simulation results show that it is feasible to diagnose nuclear power plant faults with the confederation diagnosis methods combined the neural networks based on the concept lattice theory and expert system, with the distinctive features such as the efficiency of neural network learning, less calculation and reliability of diagnosis results and so on. (authors)

  3. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

    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.

  4. Parameter-free bearing fault detection based on maximum likelihood estimation and differentiation

    Bozchalooi, I Soltani; Liang, Ming

    2009-01-01

    Bearing faults can lead to malfunction and ultimately complete stall of many machines. The conventional high-frequency resonance (HFR) method has been commonly used for bearing fault detection. However, it is often very difficult to obtain and calibrate bandpass filter parameters, i.e. the center frequency and bandwidth, the key to the success of the HFR method. This inevitably undermines the usefulness of the conventional HFR technique. To avoid such difficulties, we propose parameter-free, versatile yet straightforward techniques to detect bearing faults. We focus on two types of measured signals frequently encountered in practice: (1) a mixture of impulsive faulty bearing vibrations and intrinsic background noise and (2) impulsive faulty bearing vibrations blended with intrinsic background noise and vibration interferences. To design a proper signal processing technique for each case, we analyze the effects of intrinsic background noise and vibration interferences on amplitude demodulation. For the first case, a maximum likelihood-based fault detection method is proposed to accommodate the Rician distribution of the amplitude-demodulated signal mixture. For the second case, we first illustrate that the high-amplitude low-frequency vibration interferences can make the amplitude demodulation ineffective. Then we propose a differentiation method to enhance the fault detectability. It is shown that the iterative application of a differentiation step can boost the relative strength of the impulsive faulty bearing signal component with respect to the vibration interferences. This preserves the effectiveness of amplitude demodulation and hence leads to more accurate fault detection. The proposed approaches are evaluated on simulated signals and experimental data acquired from faulty bearings

  5. Improving software requirements specification for safety-related systems using the fault tree developed by an object-based method

    Cepin, M.; Mavko, B.

    1998-01-01

    A modification of the fault tree analysis is presented. The new fault tree integrates structural and behavioral models of a system. Information on the system structure is captured in the name of each gate and basic event of the fault tree. Information on the system behavior is captured in their description. Behavior is expressed using the axiomatic notation based on first order predicate logic. The new fault tree is a useful model for analysis and improvement of software requirements specification. The benefit of such improvements is reduced probability of failures in specification, which in turn results in increased reliability of the software.(author)

  6. A new Bayesian Inference-based Phase Associator for Earthquake Early Warning

    Meier, Men-Andrin; Heaton, Thomas; Clinton, John; Wiemer, Stefan

    2013-04-01

    State of the art network-based Earthquake Early Warning (EEW) systems can provide warnings for large magnitude 7+ earthquakes. Although regions in the direct vicinity of the epicenter will not receive warnings prior to damaging shaking, real-time event characterization is available before the destructive S-wave arrival across much of the strongly affected region. In contrast, in the case of the more frequent medium size events, such as the devastating 1994 Mw6.7 Northridge, California, earthquake, providing timely warning to the smaller damage zone is more difficult. For such events the "blind zone" of current systems (e.g. the CISN ShakeAlert system in California) is similar in size to the area over which severe damage occurs. We propose a faster and more robust Bayesian inference-based event associator, that in contrast to the current standard associators (e.g. Earthworm Binder), is tailored to EEW and exploits information other than only phase arrival times. In particular, the associator potentially allows for reliable automated event association with as little as two observations, which, compared to the ShakeAlert system, would speed up the real-time characterizations by about ten seconds and thus reduce the blind zone area by up to 80%. We compile an extensive data set of regional and teleseismic earthquake and noise waveforms spanning a wide range of earthquake magnitudes and tectonic regimes. We pass these waveforms through a causal real-time filterbank with passband filters between 0.1 and 50Hz, and, updating every second from the event detection, extract the maximum amplitudes in each frequency band. Using this dataset, we define distributions of amplitude maxima in each passband as a function of epicentral distance and magnitude. For the real-time data, we pass incoming broadband and strong motion waveforms through the same filterbank and extract an evolving set of maximum amplitudes in each passband. We use the maximum amplitude distributions to check

  7. An information transfer based novel framework for fault root cause tracing of complex electromechanical systems in the processing industry

    Wang, Rongxi; Gao, Xu; Gao, Jianmin; Gao, Zhiyong; Kang, Jiani

    2018-02-01

    As one of the most important approaches for analyzing the mechanism of fault pervasion, fault root cause tracing is a powerful and useful tool for detecting the fundamental causes of faults so as to prevent any further propagation and amplification. Focused on the problems arising from the lack of systematic and comprehensive integration, an information transfer-based novel data-driven framework for fault root cause tracing of complex electromechanical systems in the processing industry was proposed, taking into consideration the experience and qualitative analysis of conventional fault root cause tracing methods. Firstly, an improved symbolic transfer entropy method was presented to construct a directed-weighted information model for a specific complex electromechanical system based on the information flow. Secondly, considering the feedback mechanisms in the complex electromechanical systems, a method for determining the threshold values of weights was developed to explore the disciplines of fault propagation. Lastly, an iterative method was introduced to identify the fault development process. The fault root cause was traced by analyzing the changes in information transfer between the nodes along with the fault propagation pathway. An actual fault root cause tracing application of a complex electromechanical system is used to verify the effectiveness of the proposed framework. A unique fault root cause is obtained regardless of the choice of the initial variable. Thus, the proposed framework can be flexibly and effectively used in fault root cause tracing for complex electromechanical systems in the processing industry, and formulate the foundation of system vulnerability analysis and condition prediction, as well as other engineering applications.

  8. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

    Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wencheng

    2017-01-01

    Highlights: •An improved Simulink based modeling method is proposed for PV modules and arrays. •Key points of I-V curves and PV model parameters are used as the feature variables. •Kernel extreme learning machine (KELM) is explored for PV arrays fault diagnosis. •The parameters of KELM algorithm are optimized by the Nelder-Mead simplex method. •The optimized KELM fault diagnosis model achieves high accuracy and reliability. -- Abstract: Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are

  9. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

    Mauricio Holguín-Londoño

    2016-01-01

    Full Text Available Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM. This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

  10. Detection of Early Faults in Rotating Machinery Based on Wavelet Analysis

    Meng Hee Lim

    2013-01-01

    Full Text Available This paper explores the application of wavelet analysis for the detection of early changes in rotor dynamics caused by common machinery faults, namely, rotor unbalance and minor blade rubbing conditions. In this paper, the time synchronised wavelet analysis method was formulated and its effectiveness to detect machinery faults at the early stage was evaluated based on signal simulation and experimental study. The proposed method provides a more standardised approach to visualise the current state of rotor dynamics of a rotating machinery by taking into account the effects of time shift, wavelet edge distortion, and system noise suppression. The experimental results showed that this method is able to reveal subtle changes of the vibration signal characteristics in both the frequency content distribution and the amplitude distortion caused by minor rotor unbalance and blade rubbing conditions. Besides, this method also appeared to be an effective tool to diagnose and to discriminate the different types of machinery faults based on the unique pattern of the wavelet contours. This study shows that the proposed wavelet analysis method is promising to reveal machinery faults at early stage as compared to vibration spectrum analysis.

  11. Methodology for selection of attributes and operating conditions for SVM-Based fault locator's

    Debbie Johan Arredondo Arteaga

    2017-01-01

    Full Text Available Context: Energy distribution companies must employ strategies to meet their timely and high quality service, and fault-locating techniques represent and agile alternative for restoring the electric service in the power distribution due to the size of distribution services (generally large and the usual interruptions in the service. However, these techniques are not robust enough and present some limitations in both computational cost and the mathematical description of the models they use. Method: This paper performs an analysis based on a Support Vector Machine for the evaluation of the proper conditions to adjust and validate a fault locator for distribution systems; so that it is possible to determine the minimum number of operating conditions that allow to achieve a good performance with a low computational effort. Results: We tested the proposed methodology in a prototypical distribution circuit, located in a rural area of Colombia. This circuit has a voltage of 34.5 KV and is subdivided in 20 zones. Additionally, the characteristics of the circuit allowed us to obtain a database of 630.000 records of single-phase faults and different operating conditions. As a result, we could determine that the locator showed a performance above 98% with 200 suitable selected operating conditions. Conclusions: It is possible to improve the performance of fault locators based on Support Vector Machine. Specifically, these improvements are achieved by properly selecting optimal operating conditions and attributes, since they directly affect the performance in terms of efficiency and the computational cost.

  12. Assessment of control strategies for fault ride through of SCIG-based wind energy conversion systems

    Manaullah

    2016-01-01

    Full Text Available With increasing penetration of wind energy into the power grid, researchers have started focusing more on control and coordination of wind energy conversion systems (WECS with the other components at system level, especially during fault. It is important to implement a suitable fault ride through control strategy to avoid tripping of the generators when the power system is subjected to voltage dips normally below 90% of nominal voltage. The dips below 90% may lead to a significant loss of generation and frequency collapse, followed by a blackout. This article implements and assesses the methodologies to deal with such situations for squirrel cage induction generator-based wind energy conversion systems employing fully rated power electronic converters. Three distinct control techniques—namely, balanced positive sequence control, positive negative sequence control, and dual current control—have been simulated and applied to grid side converter of SCIG-based WECS. The performance of all the three control strategies has been compared and presented in this work. During this study, the system is subjected to the most common unsymmetrical line to ground (LG fault and most severe symmetrical LLL fault on grid for the purpose of anaysis.

  13. An imbalance fault detection method based on data normalization and EMD for marine current turbines.

    Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Benbouzid, Mohamed; Diallo, Demba

    2017-05-01

    This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  14. An empirically based steady state friction law and implications for fault stability.

    Spagnuolo, E; Nielsen, S; Violay, M; Di Toro, G

    2016-04-16

    Empirically based rate-and-state friction laws (RSFLs) have been proposed to model the dependence of friction forces with slip and time. The relevance of the RSFL for earthquake mechanics is that few constitutive parameters define critical conditions for fault stability (i.e., critical stiffness and frictional fault behavior). However, the RSFLs were determined from experiments conducted at subseismic slip rates ( V   0.1 m/s) remains questionable on the basis of the experimental evidence of (1) large dynamic weakening and (2) activation of particular fault lubrication processes at seismic slip rates. Here we propose a modified RSFL (MFL) based on the review of a large published and unpublished data set of rock friction experiments performed with different testing machines. The MFL, valid at steady state conditions from subseismic to seismic slip rates (0.1 µm/s fault frictional stability with implications for slip event styles and relevance for models of seismic rupture nucleation, propagation, and arrest.

  15. Research on evaluation of degree of complexity of mining fault network based on GIS

    Hua Zhang; Yun-jia Wang; Chuan-zhi Liu [China University of Mining and Technology, Jiangsu (China). School of Environment Science and Spatial Informatics

    2007-03-15

    A large number of spatial and attribute data are involved in coal resource evaluation. Databases are a relatively advanced data management technology, but their major defects are the poor graphic and spatial data functions, from which it is difficult to realize scientific management of evaluation data with spatial characteristics and evaluation result maps. On account of these deficiencies, the evaluation of degree of complexity of mining fault network based on a geographic information system (GIS) is proposed which integrates management of spatial and attribute data. A fractal is an index which can reflect the comprehensive information of faults' number, density, size, composition and dynamics mechanism. A fractal dimension is used as the quantitative evaluation index. Evaluation software has been developed based on a component GIS-MapX, with which the degree of complexity of fault network is evaluated quantitatively using the quantitative index of fractal dimensions in Liuqiao No.2 coal mine as an example. Results show that it is effective in acquiring model parameters and enhancing the definition of data and evaluation results with the application of GIS technology. The fault network is a system with fractal structure and its complexity can be described reasonably and accurately by fractal dimension, which provides an effective method for coal resource evaluation. 9 refs., 6 figs., 2 tabs.

  16. Deciphering Stress State of Seismogenic Faults in Oklahoma and Kansas Based on High-resolution Stress Maps

    Qin, Y.; Chen, X.; Haffener, J.; Trugman, D. T.; Carpenter, B.; Reches, Z.

    2017-12-01

    Induced seismicity in Oklahoma and Kansas delineates clear fault trends. It is assumed that fluid injection reactivates faults which are optimally oriented relative to the regional tectonic stress field. We utilized recently improved earthquake locations and more complete focal mechanism catalogs to quantitatively analyze the stress state of seismogenic faults with high-resolution stress maps. The steps of analysis are: (1) Mapping the faults by clustering seismicity using a nearest-neighbor approach, manually picking the fault in each cluster and calculating the fault geometry using principal component analysis. (2) Running a stress inversion with 0.2° grid spacing to produce an in-situ stress map. (3) The fault stress state is determined from fault geometry and a 3D Mohr circle. The parameter `understress' is calculated to quantify the criticalness of these faults. If it approaches 0, the fault is critically stressed; while understress=1 means there is no shear stress on the fault. Our results indicate that most of the active faults have a planar shape (planarity>0.8), and dip steeply (dip>70°). The fault trends are distributed mainly in conjugate set ranges of [50°,70°] and [100°,120°]. More importantly, these conjugate trends are consistent with mapped basement fractures in southern Oklahoma, suggesting similar basement features from regional tectonics. The fault length data shows a loglinear relationship with the maximum earthquake magnitude with an expected maximum magnitude range from 3.2 to 4.4 for most seismogenic faults. Based on 3D local Mohr circle, we find that 61% of the faults have low understress (0.5) are located within highest-rate injection zones and therefore are likely to be influenced by high pore pressure. The faults that hosted the largest earthquakes, M5.7 Prague and M5.8 Pawnee are critically stressed (understress 0.2). These differences may help in understanding earthquake sequences, for example, the predominantly aftershock

  17. Process fault diagnosis using knowledge-based systems

    Sudduth, A.L.

    1991-01-01

    Advancing technology in process plants has led to increased need for computer based process diagnostic systems to assist the operator. One approach to this problem is to use an embedded knowledge based system to interpret measurement signals. Knowledge based systems using only symptom based rules are inadequate for real time diagnosis of dynamic systems; therefore a model based approach is necessary. Though several forms of model based reasoning have been proposed, the use of qualitative causal models incorporating first principles knowledge of process behavior structure, and function appear to have the most promise as a robust modeling methodology. In this paper the structure of a diagnostic system is described which uses model based reasoning and conventional numerical methods to perform process diagnosis. This system is being applied to emergency diesel generator system in nuclear stations

  18. Design and Evaluation of a Protection Relay for a Wind Generator Based on the Positive- and Negative-Sequence Fault Components

    Zheng, T. Y.; Cha, Seung-Tae; Crossley, P. A.

    2013-01-01

    To avoid undesirable disconnection of healthy wind generators (WGs) or a wind power plant, a WG protection relay should discriminate among faults, so that it can operate instantaneously for WG, connected feeder or connection bus faults, it can operate after a delay for inter-tie or grid faults......, and it can avoid operating for parallel WG or adjacent feeder faults. A WG protection relay based on the positive- and negativesequence fault components is proposed in the paper. At stage 1, the proposed relay uses the magnitude of the positive-sequence component in the fault current to distinguish faults...... at a parallel WG connected to the same feeder or at an adjacent feeder, from other faults at a connected feeder, an inter-tie, or a grid. At stage 2, the fault type is first determined using the relationships between the positive- and negative-sequence fault components. Then, the relay differentiates between...

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

    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.

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

    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.

  1. Augmentation of Explicit Spatial Configurations by Knowledge-Based Inference on Geometric Fields

    Dan Tappan

    2009-04-01

    Full Text Available A spatial configuration of a rudimentary, static, realworld scene with known objects (animals and properties (positions and orientations contains a wealth of syntactic and semantic spatial information that can contribute to a computational understanding far beyond what its quantitative details alone convey. This work presents an approach that (1 quantitatively represents what a configuration explicitly states, (2 integrates this information with implicit, commonsense background knowledge of its objects and properties, (3 infers additional, contextually appropriate, commonsense spatial information from and about their interrelationships, and (4 augments the original representation with this combined information. A semantic network represents explicit, quantitative information in a configuration. An inheritance-based knowledge base of relevant concepts supplies implicit, qualitative background knowledge to support semantic interpretation. Together, these structures provide a simple, nondeductive, constraint-based, geometric logical formalism to infer substantial implicit knowledge for intrinsic and deictic frames of spatial reference.

  2. Novel scheme for enhancement of fault ride-through capability of doubly fed induction generator based wind farms

    Vinothkumar, K.; Selvan, M.P.

    2011-01-01

    DFIG based wind farms to fault ride-through.

  3. Novel scheme for enhancement of fault ride-through capability of doubly fed induction generator based wind farms

    Vinothkumar, K. [Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu 620015 (India); Selvan, M.P., E-mail: selvanmp@nitt.ed [Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu 620015 (India)

    2011-07-15

    enhancing the performance of DFIG based wind farms to fault ride-through.

  4. Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

    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.

  5. An empirical Bayesian approach for model-based inference of cellular signaling networks

    Klinke David J

    2009-11-01

    Full Text Available Abstract Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.

  6. Diagnosis of constant faults in read-once contact networks over finite bases

    Busbait, Monther I.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2015-01-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  7. Diagnosis of constant faults in read-once contact networks over finite bases

    Busbait, Monther I.

    2015-03-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  8. A Weibull-based compositional approach for hierarchical dynamic fault trees

    Chiacchio, F.; Cacioppo, M.; D'Urso, D.; Manno, G.; Trapani, N.; Compagno, L.

    2013-01-01

    The solution of a dynamic fault tree (DFT) for the reliability assessment can be achieved using a wide variety of techniques. These techniques have a strong theoretical foundation as both the analytical and the simulation methods have been extensively developed. Nevertheless, they all present the same limits that appear with the increasing of the size of the fault trees (i.e., state space explosion, time-consuming simulations), compromising the resolution. We have tested the feasibility of a composition algorithm based on a Weibull distribution, addressed to the resolution of a general class of dynamic fault trees characterized by non-repairable basic events and generally distributed failure times. The proposed composition algorithm is used to generalize the traditional hierarchical technique that, as previous literature have extensively confirmed, is able to reduce the computational effort of a large DFT through the modularization of independent parts of the tree. The results of this study are achieved both through simulation and analytical techniques, thus confirming the capability to solve a quite general class of dynamic fault trees and overcome the limits of traditional techniques.

  9. Reliability analysis of the solar array based on Fault Tree Analysis

    Wu Jianing; Yan Shaoze

    2011-01-01

    The solar array is an important device used in the spacecraft, which influences the quality of in-orbit operation of the spacecraft and even the launches. This paper analyzes the reliability of the mechanical system and certifies the most vital subsystem of the solar array. The fault tree analysis (FTA) model is established according to the operating process of the mechanical system based on DFH-3 satellite; the logical expression of the top event is obtained by Boolean algebra and the reliability of the solar array is calculated. The conclusion shows that the hinges are the most vital links between the solar arrays. By analyzing the structure importance(SI) of the hinge's FTA model, some fatal causes, including faults of the seal, insufficient torque of the locking spring, temperature in space, and friction force, can be identified. Damage is the initial stage of the fault, so limiting damage is significant to prevent faults. Furthermore, recommendations for improving reliability associated with damage limitation are discussed, which can be used for the redesigning of the solar array and the reliability growth planning.

  10. Intelligent Fault Diagnosis of Rotary Machinery Based on Unsupervised Multiscale Representation Learning

    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.

  11. Seismic Margin Assessment for Research Reactor using Fragility based Fault Tree Analysis

    Kwag, Shinyoung; Oh, Jinho; Lee, Jong-Min; Ryu, Jeong-Soo [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2016-10-15

    The research reactor has been often subjected to external hazards during the design lifetime. Especially, a seismic event can be one of significant threats to the failure of structure system of the research reactor. This failure is possibly extended to the direct core damage of the reactor. For this purpose, the fault tree for structural system failure leading to the core damage under an earthquake accident is developed. The failure probabilities of basic events are evaluated as fragility curves of log-normal distributions. Finally, the plant-level seismic margin is investigated by the fault tree analysis combining with fragility data and the critical path is identified. The plant-level probabilistic seismic margin assessment using the fragility based fault tree analysis was performed for quantifying the safety of research reactor to a seismic hazard. For this, the fault tree for structural system failure leading to the core damage of the reactor under a seismic accident was developed. The failure probabilities of basic events were evaluated as fragility curves of log-normal distributions.

  12. Improving reliability of state estimation programming and computing suite based on analyzing a fault tree

    Kolosok Irina

    2017-01-01

    Full Text Available Reliable information on the current state parameters obtained as a result of processing the measurements from systems of the SCADA and WAMS data acquisition and processing through methods of state estimation (SE is a condition that enables to successfully manage an energy power system (EPS. SCADA and WAMS systems themselves, as any technical systems, are subject to failures and faults that lead to distortion and loss of information. The SE procedure enables to find erroneous measurements, therefore, it is a barrier for the distorted information to penetrate into control problems. At the same time, the programming and computing suite (PCS implementing the SE functions may itself provide a wrong decision due to imperfection of the software algorithms and errors. In this study, we propose to use a fault tree to analyze consequences of failures and faults in SCADA and WAMS and in the very SE procedure. Based on the analysis of the obtained measurement information and on the SE results, we determine the state estimation PCS fault tolerance level featuring its reliability.

  13. A Fault Prognosis Strategy Based on Time-Delayed Digraph Model and Principal Component Analysis

    Ningyun Lu

    2012-01-01

    Full Text Available Because of the interlinking of process equipments in process industry, event information may propagate through the plant and affect a lot of downstream process variables. Specifying the causality and estimating the time delays among process variables are critically important for data-driven fault prognosis. They are not only helpful to find the root cause when a plant-wide disturbance occurs, but to reveal the evolution of an abnormal event propagating through the plant. This paper concerns with the information flow directionality and time-delay estimation problems in process industry and presents an information synchronization technique to assist fault prognosis. Time-delayed mutual information (TDMI is used for both causality analysis and time-delay estimation. To represent causality structure of high-dimensional process variables, a time-delayed signed digraph (TD-SDG model is developed. Then, a general fault prognosis strategy is developed based on the TD-SDG model and principle component analysis (PCA. The proposed method is applied to an air separation unit and has achieved satisfying results in predicting the frequently occurred “nitrogen-block” fault.

  14. Reliability analysis of the solar array based on Fault Tree Analysis

    Wu Jianing; Yan Shaoze, E-mail: yansz@mail.tsinghua.edu.cn [State Key Laboratory of Tribology, Department of Precision Instruments and Mechanology, Tsinghua University,Beijing 100084 (China)

    2011-07-19

    The solar array is an important device used in the spacecraft, which influences the quality of in-orbit operation of the spacecraft and even the launches. This paper analyzes the reliability of the mechanical system and certifies the most vital subsystem of the solar array. The fault tree analysis (FTA) model is established according to the operating process of the mechanical system based on DFH-3 satellite; the logical expression of the top event is obtained by Boolean algebra and the reliability of the solar array is calculated. The conclusion shows that the hinges are the most vital links between the solar arrays. By analyzing the structure importance(SI) of the hinge's FTA model, some fatal causes, including faults of the seal, insufficient torque of the locking spring, temperature in space, and friction force, can be identified. Damage is the initial stage of the fault, so limiting damage is significant to prevent faults. Furthermore, recommendations for improving reliability associated with damage limitation are discussed, which can be used for the redesigning of the solar array and the reliability growth planning.

  15. Machinery Fault Diagnosis Using Two-Channel Analysis Method Based on Fictitious System Frequency Response Function

    Kihong Shin

    2015-01-01

    Full Text Available Most existing techniques for machinery health monitoring that utilize measured vibration signals usually require measurement points to be as close as possible to the expected fault components of interest. This is particularly important for implementing condition-based maintenance since the incipient fault signal power may be too small to be detected if a sensor is located further away from the fault source. However, a measurement sensor is often not attached to the ideal point due to geometric or environmental restrictions. In such a case, many of the conventional diagnostic techniques may not be successfully applicable. In this paper, a two-channel analysis method is proposed to overcome such difficulty. It uses two vibration signals simultaneously measured at arbitrary points in a machine. The proposed method is described theoretically by introducing a fictitious system frequency response function. It is then verified experimentally for bearing fault detection. The results show that the suggested method may be a good alternative when ideal points for measurement sensors are not readily available.

  16. An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

    Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan

    2017-10-01

    It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal fault monitoring and diagnosis based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal faults, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the fault feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal fault diagnosis after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.

  17. Fault specific GIS based seismic hazard maps for the Attica region, Greece

    Deligiannakis, G.; Papanikolaou, I. D.; Roberts, G.

    2018-04-01

    Traditional seismic hazard assessment methods are based on the historical seismic records for the calculation of an annual probability of exceedance for a particular ground motion level. A new fault-specific seismic hazard assessment method is presented, in order to address problems related to the incompleteness and the inhomogeneity of the historical records and to obtain higher spatial resolution of hazard. This method is applied to the region of Attica, which is the most densely populated area in Greece, as nearly half of the country's population lives in Athens and its surrounding suburbs, in the Greater Athens area. The methodology is based on a database of 24 active faults that could cause damage to Attica in case of seismic rupture. This database provides information about the faults slip rates, lengths and expected magnitudes. The final output of the method is four fault-specific seismic hazard maps, showing the recurrence of expected intensities for each locality. These maps offer a high spatial resolution, as they consider the surface geology. Despite the fact that almost half of the Attica region lies on the lowest seismic risk zone according to the official seismic hazard zonation of Greece, different localities have repeatedly experienced strong ground motions during the last 15 kyrs. Moreover, the maximum recurrence for each intensity occurs in different localities across Attica. Highest recurrence for intensity VII (151-156 times over 15 kyrs, or up to a 96 year return period) is observed in the central part of the Athens basin. The maximum intensity VIII recurrence (115 times over 15 kyrs, or up to a 130 year return period) is observed in the western part of Attica, while the maximum intensity IX (73-77/15 kyrs, or a 195 year return period) and X (25-29/15 kyrs, or a 517 year return period) recurrences are observed near the South Alkyonides fault system, which dominates the strong ground motions hazard in the western part of the Attica mainland.

  18. An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique

    Taheri-Garavand, Amin; Ahmadi, Hojjat; Omid, Mahmoud; Mohtasebi, Seyed Saeid; Mollazade, Kaveh; Russell Smith, Alan John; Carlomagno, Giovanni Maria

    2015-01-01

    This research presents a new intelligent fault diagnosis and condition monitoring system for classification of different conditions of cooling radiator using infrared thermal images. The system was adopted to classify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is implemented to decompose the thermal images. Subsequently, statistical texture features are extracted from the original images and are decomposed into thermal images. The significant selected features are used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6 topology. The classification results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator. - Highlights: • Intelligent fault diagnosis of cooling radiator using thermal image processing. • Thermal image processing in a multiscale representation structure by 2D-DWT. • Selection features based on a hybrid system that uses both GA and ANN. • Application of ANN as classifier. • Classification accuracy of fault detection up to 93.83%

  19. Deciphering the paleoseismic history of the central Dead Sea fault (Yammouneh fault, Lebanon) based on multiple luminescence dating techniques

    Le Beon, M.; Tseng, Y. C.; Klinger, Y.; Elias, A.; Kunz, A.; Sursock, A.; Daeron, M.; Tapponnier, P.; Jomaa, R.

    2017-12-01

    The Yammouneh fault is the main strike-slip branch of the Dead Sea fault system in Lebanon. The morphology of the northern Yammouneh fault is characterized by a series of basins that represent archives for Late Pleistocene paleo-environments and paleo-earthquakes. We excavated a 4-m-deep trench across the fault in the Jbab el-Homr basin that revealed a succession of remarkable, very thin palustrine and lacustrine layers, ruptured by at least 17 earthquakes. Absolute ages of 4 samples from 0.5 to 3.7 m depth are obtained by optically stimulated luminescence dating on fine-grain quartz and on fine-grain K-feldspar using both infrared luminescence at 50˚C (IRSL50) and at a high temperature of 225˚C (pIRIR225). A fair agreement is obtained between the quartz ages (from 26.5 ± 3.1 ka at 0.5 m depth to 30.3 ± 3.4 ka at 3.7 m depth) and the pIRIR225 ages (from 26.2 ± 2.3 ka at 0.5 m depth to 25.8 ± 2.1 ka at 3.7 m depth), while the fading-corrected IRSL50 ages are systematically younger (from 18.3 ± 1.6 ka at 0.5 m depth to 21.4 ± 1.8 ka at 3.7 m depth). As proposed in earlier studies, we hypothesize that the IRSL50 fading rate is underestimated. The sedimentary sequence may reflect deposition in a marsh or shallow lake in a pro-glacial environment at a time when a glacier may have occupied the summits of Mount Lebanon. Erosion may have been dominant after the Last Glacial Maximum. Regarding paleo-earthquakes, 14 surface-rupturing events occurred during 3.8 ka with a mean return time of 270 years and probable clustering, while only 2-11 events occurred since 26.5 ka. Firstly, we explain the lack of events since 26.5 ka by the existence of another fault branch, which suggests that the active fault zone migrated with time. Secondly, the shorter mean recurrence time in Jbab compared to the Yammouneh site, located 30 km south may be explained by temporal variations in the earthquake cycle, different locations relative to fault segmentation, or by high-resolution of

  20. Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

    Xiaoming Xu

    2017-01-01

    Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

  1. A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

    2007-01-01

    Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine(SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

  2. EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning.

    Zhao, Chao; Jiang, Jingchi; Guan, Yi; Guo, Xitong; He, Bin

    2018-05-01

    Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. Assessment of statistical education in Indonesia: Preliminary results and initiation to simulation-based inference

    Saputra, K. V. I.; Cahyadi, L.; Sembiring, U. A.

    2018-01-01

    Start in this paper, we assess our traditional elementary statistics education and also we introduce elementary statistics with simulation-based inference. To assess our statistical class, we adapt the well-known CAOS (Comprehensive Assessment of Outcomes in Statistics) test that serves as an external measure to assess the student’s basic statistical literacy. This test generally represents as an accepted measure of statistical literacy. We also introduce a new teaching method on elementary statistics class. Different from the traditional elementary statistics course, we will introduce a simulation-based inference method to conduct hypothesis testing. From the literature, it has shown that this new teaching method works very well in increasing student’s understanding of statistics.

  4. Transposing an active fault database into a fault-based seismic hazard assessment for nuclear facilities - Part 2: Impact of fault parameter uncertainties on a site-specific PSHA exercise in the Upper Rhine Graben, eastern France

    Chartier, Thomas; Scotti, Oona; Clément, Christophe; Jomard, Hervé; Baize, Stéphane

    2017-09-01

    We perform a fault-based probabilistic seismic hazard assessment (PSHA) exercise in the Upper Rhine Graben to quantify the relative influence of fault parameters on the hazard at the Fessenheim nuclear power plant site. Specifically, we show that the potentially active faults described in the companion paper (Jomard et al., 2017, hereafter Part 1) are the dominant factor in hazard estimates at the low annual probability of exceedance relevant for the safety assessment of nuclear installations. Geological information documenting the activity of the faults in this region, however, remains sparse, controversial and affected by a high degree of uncertainty. A logic tree approach is thus implemented to explore the epistemic uncertainty and quantify its impact on the seismic hazard estimates. Disaggregation of the peak ground acceleration (PGA) hazard at a 10 000-year return period shows that the Rhine River fault is the main seismic source controlling the hazard level at the site. Sensitivity tests show that the uncertainty on the slip rate of the Rhine River fault is the dominant factor controlling the variability of the seismic hazard level, greater than the epistemic uncertainty due to ground motion prediction equations (GMPEs). Uncertainty on slip rate estimates from 0.04 to 0.1 mm yr-1 results in a 40 to 50 % increase in hazard levels at the 10 000-year target return period. Reducing epistemic uncertainty in future fault-based PSHA studies at this site will thus require (1) performing in-depth field studies to better characterize the seismic potential of the Rhine River fault; (2) complementing GMPEs with more physics-based modelling approaches to better account for the near-field effects of ground motion and (3) improving the modelling of the background seismicity. Indeed, in this exercise, we assume that background earthquakes can only host M 6. 0 earthquakes have been recently identified at depth within the Upper Rhine Graben (see Part 1) but are not accounted

  5. Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs

    Xu, Xianghua; Gao, Xueyong; Wan, Jian; Xiong, Naixue

    2011-01-01

    This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP) localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms. PMID:22163972

  6. Compressed sensing of roller bearing fault based on multiple down-sampling strategy

    Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang

    2016-01-01

    Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults. (paper)

  7. Quantitative study of tectonic geomorphology along Haiyuan fault based on airborne LiDAR

    Chen, Tao; Zhang, Pei Zhen; Liu, Jing; Li, Chuan You; Ren, Zhi Kun; Hudnut, Kenneth W.

    2014-01-01

    High-precision and high-resolution topography are the fundamental data for active fault research. Light detection and ranging (LiDAR) presents a new approach to build detailed digital elevation models effectively. We take the Haiyuan fault in Gansu Province as an example of how LiDAR data may be used to improve the study of active faults and the risk assessment of related hazards. In the eastern segment of the Haiyuan fault, the Shaomayin site has been comprehensively investigated in previous research because of its exemplary tectonic topographic features. Based on unprecedented LiDAR data, the horizontal and vertical coseismic offsets at the Shaomayin site are described. The measured horizontal value is about 8.6 m, and the vertical value is about 0.8 m. Using prior dating ages sampled from the same location, we estimate the horizontal slip rate as 4.0 ± 1.0 mm/a with high confidence and define that the lower bound of the vertical slip rate is 0.4 ± 0.1 mm/a since the Holocene. LiDAR data can repeat the measurements of field work on quantifying offsets of tectonic landform features quite well. The offset landforms are visualized on an office computer workstation easily, and specialized software may be used to obtain displacement quantitatively. By combining precious chronological results, the fundamental link between fault activity and large earthquakes is better recognized, as well as the potential risk for future earthquake hazards.

  8. Compressed sensing of roller bearing fault based on multiple down-sampling strategy

    Wang, Huaqing; Ke, Yanliang; Luo, Ganggang; Tang, Gang

    2016-02-01

    Roller bearings are essential components of rotating machinery and are often exposed to complex operating conditions, which can easily lead to their failures. Thus, to ensure normal production and the safety of machine operators, it is essential to detect the failures as soon as possible. However, it is a major challenge to maintain a balance between detection efficiency and big data acquisition given the limitations of sampling theory. To overcome these limitations, we try to preserve the information pertaining to roller bearing failures using a sampling rate far below the Nyquist sampling rate, which can ease the pressure generated by the large-scale data. The big data of a faulty roller bearing’s vibration signals is firstly reduced by a down-sample strategy while preserving the fault features by selecting peaks to represent the data segments in time domain. However, a problem arises in that the fault features may be weaker than before, since the noise may be mistaken for the peaks when the noise is stronger than the vibration signals, which makes the fault features unable to be extracted by commonly-used envelope analysis. Here we employ compressive sensing theory to overcome this problem, which can make a signal enhancement and reduce the sample sizes further. Moreover, it is capable of detecting fault features from a small number of samples based on orthogonal matching pursuit approach, which can overcome the shortcomings of the multiple down-sample algorithm. Experimental results validate the effectiveness of the proposed technique in detecting roller bearing faults.

  9. Trust Index Based Fault Tolerant Multiple Event Localization Algorithm for WSNs

    Jian Wan

    2011-06-01

    Full Text Available This paper investigates the use of wireless sensor networks for multiple event source localization using binary information from the sensor nodes. The events could continually emit signals whose strength is attenuated inversely proportional to the distance from the source. In this context, faults occur due to various reasons and are manifested when a node reports a wrong decision. In order to reduce the impact of node faults on the accuracy of multiple event localization, we introduce a trust index model to evaluate the fidelity of information which the nodes report and use in the event detection process, and propose the Trust Index based Subtract on Negative Add on Positive (TISNAP localization algorithm, which reduces the impact of faulty nodes on the event localization by decreasing their trust index, to improve the accuracy of event localization and performance of fault tolerance for multiple event source localization. The algorithm includes three phases: first, the sink identifies the cluster nodes to determine the number of events occurred in the entire region by analyzing the binary data reported by all nodes; then, it constructs the likelihood matrix related to the cluster nodes and estimates the location of all events according to the alarmed status and trust index of the nodes around the cluster nodes. Finally, the sink updates the trust index of all nodes according to the fidelity of their information in the previous reporting cycle. The algorithm improves the accuracy of localization and performance of fault tolerance in multiple event source localization. The experiment results show that when the probability of node fault is close to 50%, the algorithm can still accurately determine the number of the events and have better accuracy of localization compared with other algorithms.

  10. α-Cut method based importance measure for criticality analysis in fuzzy probability – Based fault tree analysis

    Purba, Julwan Hendry; Sony Tjahyani, D.T.; Widodo, Surip; Tjahjono, Hendro

    2017-01-01

    Highlights: •FPFTA deals with epistemic uncertainty using fuzzy probability. •Criticality analysis is important for reliability improvement. •An α-cut method based importance measure is proposed for criticality analysis in FPFTA. •The α-cut method based importance measure utilises α-cut multiplication, α-cut subtraction, and area defuzzification technique. •Benchmarking confirm that the proposed method is feasible for criticality analysis in FPFTA. -- Abstract: Fuzzy probability – based fault tree analysis (FPFTA) has been recently developed and proposed to deal with the limitations of conventional fault tree analysis. In FPFTA, reliabilities of basic events, intermediate events and top event are characterized by fuzzy probabilities. Furthermore, the quantification of the FPFTA is based on fuzzy multiplication rule and fuzzy complementation rule to propagate uncertainties from basic event to the top event. Since the objective of the fault tree analysis is to improve the reliability of the system being evaluated, it is necessary to find the weakest path in the system. For this purpose, criticality analysis can be implemented. Various importance measures, which are based on conventional probabilities, have been developed and proposed for criticality analysis in fault tree analysis. However, not one of those importance measures can be applied for criticality analysis in FPFTA, which is based on fuzzy probability. To be fully applied in nuclear power plant probabilistic safety assessment, FPFTA needs to have its corresponding importance measure. The objective of this study is to develop an α-cut method based importance measure to evaluate and rank the importance of basic events for criticality analysis in FPFTA. To demonstrate the applicability of the proposed measure, a case study is performed and its results are then benchmarked to the results generated by the four well known importance measures in conventional fault tree analysis. The results

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

    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. Enhanced DET-Based Fault Signature Analysis for Reliable Diagnosis of Single and Multiple-Combined Bearing Defects

    In-Kyu Jeong

    2015-01-01

    Full Text Available To early identify cylindrical roller bearing failures, this paper proposes a comprehensive bearing fault diagnosis method, which consists of spectral kurtosis analysis for finding the most informative subband signal well representing abnormal symptoms about the bearing failures, fault signature calculation using this subband signal, enhanced distance evaluation technique- (EDET- based fault signature analysis that outputs the most discriminative fault features for accurate diagnosis, and identification of various single and multiple-combined cylindrical roller bearing defects using the simplified fuzzy adaptive resonance map (SFAM. The proposed comprehensive bearing fault diagnosis methodology is effective for accurate bearing fault diagnosis, yielding an average classification accuracy of 90.35%. In this paper, the proposed EDET specifically addresses shortcomings in the conventional distance evaluation technique (DET by accurately estimating the sensitivity of each fault signature for each class. To verify the efficacy of the EDET-based fault signature analysis for accurate diagnosis, a diagnostic performance comparison is carried between the proposed EDET and the conventional DET in terms of average classification accuracy. In fact, the proposed EDET achieves up to 106.85% performance improvement over the conventional DET in average classification accuracy.

  13. Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles

    Chen, Zeyu; Xiong, Rui; Tian, Jinpeng; Shang, Xiong; Lu, Jiahuan

    2016-01-01

    Highlights: • The characteristics of ESC fault of lithium-ion battery are investigated experimentally. • The proposed method to simulate the electrical behavior of ESC fault is viable. • Ten parameters in the presented fault model were optimized using a DPSO algorithm. • A two-layer model-based fault diagnosis approach for battery ESC is proposed. • The effective and robustness of the proposed algorithm has been evaluated. - Abstract: This study investigates the external short circuit (ESC) fault characteristics of lithium-ion battery experimentally. An experiment platform is established and the ESC tests are implemented on ten 18650-type lithium cells considering different state-of-charges (SOCs). Based on the experiment results, several efforts have been made. (1) The ESC process can be divided into two periods and the electrical and thermal behaviors within these two periods are analyzed. (2) A modified first-order RC model is employed to simulate the electrical behavior of the lithium cell in the ESC fault process. The model parameters are re-identified by a dynamic-neighborhood particle swarm optimization algorithm. (3) A two-layer model-based ESC fault diagnosis algorithm is proposed. The first layer conducts preliminary fault detection and the second layer gives a precise model-based diagnosis. Four new cells are short-circuited to evaluate the proposed algorithm. It shows that the ESC fault can be diagnosed within 5 s, the error between the model and measured data is less than 0.36 V. The effectiveness of the fault diagnosis algorithm is not sensitive to the precision of battery SOC. The proposed algorithm can still make the correct diagnosis even if there is 10% error in SOC estimation.

  14. Active Fault Isolation in MIMO Systems

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    isolation is based directly on the input/output s ignals applied for the fault detection. It is guaranteed that the fault group includes the fault that had occurred in the system. The second step is individual fault isolation in the fault group . Both types of isolation are obtained by applying dedicated......Active fault isolation of parametric faults in closed-loop MIMO system s are considered in this paper. The fault isolation consists of two steps. T he first step is group- wise fault isolation. Here, a group of faults is isolated from other pos sible faults in the system. The group-wise fault...

  15. Intelligent fault recognition strategy based on adaptive optimized multiple centers

    Zheng, Bo; Li, Yan-Feng; Huang, Hong-Zhong

    2018-06-01

    For the recognition principle based optimized single center, one important issue is that the data with nonlinear separatrix cannot be recognized accurately. In order to solve this problem, a novel recognition strategy based on adaptive optimized multiple centers is proposed in this paper. This strategy recognizes the data sets with nonlinear separatrix by the multiple centers. Meanwhile, the priority levels are introduced into the multi-objective optimization, including recognition accuracy, the quantity of optimized centers, and distance relationship. According to the characteristics of various data, the priority levels are adjusted to ensure the quantity of optimized centers adaptively and to keep the original accuracy. The proposed method is compared with other methods, including support vector machine (SVM), neural network, and Bayesian classifier. The results demonstrate that the proposed strategy has the same or even better recognition ability on different distribution characteristics of data.

  16. Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.

    Xiaobo Guo

    Full Text Available Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs. It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC curve and the precision-recall (PR curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.

  17. Human disease MiRNA inference by combining target information based on heterogeneous manifolds.

    Ding, Pingjian; Luo, Jiawei; Liang, Cheng; Xiao, Qiu; Cao, Buwen

    2018-04-01

    The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omics data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM. Copyright © 2018 Elsevier Inc. All rights reserved.

  18. Inferring late-Holocene climate in the Ecuadorian Andes using a chironomid-based temperature inference model

    Matthews-Bird, Frazer; Brooks, Stephen J.; Holden, Philip B.; Montoya, Encarni; Gosling, William D.

    2016-06-01

    Presented here is the first chironomid calibration data set for tropical South America. Surface sediments were collected from 59 lakes across Bolivia (15 lakes), Peru (32 lakes), and Ecuador (12 lakes) between 2004 and 2013 over an altitudinal gradient from 150 m above sea level (a.s.l) to 4655 m a.s.l, between 0-17° S and 64-78° W. The study sites cover a mean annual temperature (MAT) gradient of 25 °C. In total, 55 chironomid taxa were identified in the 59 calibration data set lakes. When used as a single explanatory variable, MAT explains 12.9 % of the variance (λ1/λ2 = 1.431). Two inference models were developed using weighted averaging (WA) and Bayesian methods. The best-performing model using conventional statistical methods was a WA (inverse) model (R2jack = 0.890; RMSEPjack = 2.404 °C, RMSEP - root mean squared error of prediction; mean biasjack = -0.017 °C; max biasjack = 4.665 °C). The Bayesian method produced a model with R2jack = 0.909, RMSEPjack = 2.373 °C, mean biasjack = 0.598 °C, and max biasjack = 3.158 °C. Both models were used to infer past temperatures from a ca. 3000-year record from the tropical Andes of Ecuador, Laguna Pindo. Inferred temperatures fluctuated around modern-day conditions but showed significant departures at certain intervals (ca. 1600 cal yr BP; ca. 3000-2500 cal yr BP). Both methods (WA and Bayesian) showed similar patterns of temperature variability; however, the magnitude of fluctuations differed. In general the WA method was more variable and often underestimated Holocene temperatures (by ca. -7 ± 2.5 °C relative to the modern period). The Bayesian method provided temperature anomaly estimates for cool periods that lay within the expected range of the Holocene (ca. -3 ± 3.4 °C). The error associated with both reconstructions is consistent with a constant temperature of 20 °C for the past 3000 years. We would caution, however, against an over-interpretation at this stage. The reconstruction can only

  19. Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

    Zhou Qing

    2017-01-01

    Full Text Available Sensorless Drive Diagnosis can be used to assess the process data without the need for additional cost-intensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multi-objective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

  20. Fault detection for hydraulic pump based on chaotic parallel RBF network

    Ma Ning

    2011-01-01

    Full Text Available Abstract In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.

  1. Application of energies of optimal frequency bands for fault diagnosis based on modified distance function

    Zamanian, Amir Hosein [Southern Methodist University, Dallas (United States); Ohadi, Abdolreza [Amirkabir University of Technology (Tehran Polytechnic), Tehran (Iran, Islamic Republic of)

    2017-06-15

    Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application.

  2. Faulting of gas-hydrate-bearing marine sediments - contribution to permeability

    Dillon, William P.; Holbrook, W.S.; Drury, Rebecca; Gettrust, Joseph; Hutchinson, Deborah; Booth, James; Taylor, Michael

    1997-01-01

    Extensive faulting is observed in sediments containing high concentrations of methane hydrate off the southeastern coast of the United States. Faults that break the sea floor show evidence of both extension and shortening; mud diapirs are also present. The zone of recent faulting apparently extends from the ocean floor down to the base of gas-hydrate stability. We infer that the faulting resulted from excess pore pressure in gas trapped beneath the gas hydrate-beating layer and/or weakening and mobilization of sediments in the region just below the gas-hydrate stability zone. In addition to the zone of surface faults, we identified two buried zones of faulting, that may have similar origins. Subsurface faulted zones appear to act as gas traps.

  3. Research on fault diagnosis of nuclear power plants based on genetic algorithms and fuzzy logic

    Zhou Yangping; Zhao Bingquan

    2001-01-01

    Based on genetic algorithms and fuzzy logic and using expert knowledge, mini-knowledge tree model and standard signals from simulator, a new fuzzy-genetic method is developed to fault diagnosis in nuclear power plants. A new replacement method of genetic algorithms is adopted. Fuzzy logic is used to calculate the fitness of the strings in genetic algorithms. Experiments on the simulator show it can deal with the uncertainty and the fuzzy factor

  4. Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

    Zhang, Han

    Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

  5. Fault Identification Algorithm Based on Zone-Division Wide Area Protection System

    Xiaojun Liu; Youcheng Wang; Hub Hu

    2014-01-01

    As the power grid becomes more magnified and complicated, wide-area protection system in the practical engineering application is more and more restricted by the communication level. Based on the concept of limitedness of wide-area protection system, the grid with complex structure is divided orderly in this paper, and fault identification and protection action are executed in each divided zone to reduce the pressure of the communication system. In protection zone, a new wide-area...

  6. Fault Tolerant Feedback Control

    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....

  7. Supervisory Adaptive Network-Based Fuzzy Inference System (SANFIS Design for Empirical Test of Mobile Robot

    Yi-Jen Mon

    2012-10-01

    Full Text Available A supervisory Adaptive Network-based Fuzzy Inference System (SANFIS is proposed for the empirical control of a mobile robot. This controller includes an ANFIS controller and a supervisory controller. The ANFIS controller is off-line tuned by an adaptive fuzzy inference system, the supervisory controller is designed to compensate for the approximation error between the ANFIS controller and the ideal controller, and drive the trajectory of the system onto a specified surface (called the sliding surface or switching surface while maintaining the trajectory onto this switching surface continuously to guarantee the system stability. This SANFIS controller can achieve favourable empirical control performance of the mobile robot in the empirical tests of driving the mobile robot with a square path. Practical experimental results demonstrate that the proposed SANFIS can achieve better control performance than that achieved using an ANFIS controller for empirical control of the mobile robot.

  8. In silico model-based inference: a contemporary approach for hypothesis testing in network biology.

    Klinke, David J

    2014-01-01

    Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. © 2014 American Institute of Chemical Engineers.

  9. The development of adaptive decision making: Recognition-based inference in children and adolescents.

    Horn, Sebastian S; Ruggeri, Azzurra; Pachur, Thorsten

    2016-09-01

    Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment, particularly the predictive power (validity) of recognition. Little is known about developmental differences in use of the RH. In this study, the authors examined (a) to what extent children and adolescents recruit the RH when making judgments, and (b) around what age adaptive use of the RH emerges. Primary schoolchildren (M = 9 years), younger adolescents (M = 12 years), and older adolescents (M = 17 years) made comparative judgments in task environments with either high or low recognition validity. Reliance on the RH was measured with a hierarchical multinomial model. Results indicated that primary schoolchildren already made systematic use of the RH. However, only older adolescents adaptively adjusted their strategy use between environments and were better able to discriminate between situations in which the RH led to correct versus incorrect inferences. These findings suggest that the use of simple heuristics does not progress unidirectionally across development but strongly depends on the task environment, in line with the perspective of ecological rationality. Moreover, adaptive heuristic inference seems to require experience and a developed base of domain knowledge. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  10. Fault tolerant control of a three-phase three-wire shunt active filter system based on reliability analysis

    Poure, P. [Laboratoire d' Instrumentation Electronique de Nancy LIEN, EA 3440, Nancy-Universite, Faculte des Sciences et Techniques, BP 239, 54506 Vandoeuvre Cedex (France); Weber, P.; Theilliol, D. [Centre de Recherche en Automatique de Nancy UMR 7039, Nancy-Universite, CNRS, Faculte des Sciences et Techniques, BP 239, 54506 Vandoeuvre Cedex (France); Saadate, S. [Groupe de Recherches en Electrotechnique et Electronique de Nancy UMR 7037, Nancy-Universite, CNRS, Faculte des Sciences et Techniques, BP 239, 54506 Vandoeuvre Cedex (France)

    2009-02-15

    This paper deals with fault tolerant shunt three-phase three-wire active filter topologies for which reliability is very important in industry applications. The determination of the optimal reconfiguration structure among various ones with or without redundant components is discussed based on reliability criteria. First, the reconfiguration of the inverter is detailed and a fast fault diagnosis method for power semi-conductor or driver fault detection and compensation is presented. This method avoids false fault detection due to power semi-conductors switching. The control architecture and algorithm are studied and a fault tolerant control strategy is considered. Simulation results in open and short circuit cases validate the theoretical study. Finally, the reliability of the studied three-phase three-wire filter shunt active topologies is analyzed to determine the optimal one. (author)

  11. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

    2016-03-01

    As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.

  12. Fault diagnosis of an intelligent hydraulic pump based on a nonlinear unknown input observer

    Zhonghai MA

    2018-02-01

    Full Text Available Hydraulic piston pumps are commonly used in aircraft. In order to improve the viability of aircraft and energy efficiency, intelligent variable pressure pump systems have been used in aircraft hydraulic systems more and more widely. Efficient fault diagnosis plays an important role in improving the reliability and performance of hydraulic systems. In this paper, a fault diagnosis method of an intelligent hydraulic pump system (IHPS based on a nonlinear unknown input observer (NUIO is proposed. Different from factors of a full-order Luenberger-type unknown input observer, nonlinear factors of the IHPS are considered in the NUIO. Firstly, a new type of intelligent pump is presented, the mathematical model of which is established to describe the IHPS. Taking into account the real-time requirements of the IHPS and the special structure of the pump, the mechanism of the intelligent pump and failure modes are analyzed and two typical failure modes are obtained. Furthermore, a NUIO of the IHPS is performed based on the output pressure and swashplate angle signals. With the residual error signals produced by the NUIO, online intelligent pump failure occurring in real-time can be detected. Lastly, through analysis and simulation, it is confirmed that this diagnostic method could accurately diagnose and isolate those typical failure modes of the nonlinear IHPS. The method proposed in this paper is of great significance in improving the reliability of the IHPS. Keywords: Fault diagnosis, Hydraulic piston pump, Model-based, Nonlinear unknown input observer (NUIO, Residual error

  13. A study on group decision-making based fault multi-symptom-domain consensus diagnosis

    He Yongyong; Chu Fulei; Zhong Binglin

    2001-01-01

    In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. In this paper, in order to utilize multiple symptom domains to improve the diagnosis accuracy, an idea of fault multi-symptom-domain consensus diagnosis is developed. From the point of view of the group decision-making, two particular multi-symptom-domain diagnosis strategies are proposed. The proposed strategies use BP (Back-Propagation) neural networks as diagnosis models in various symptom domains, and then combine the outputs of these networks by two combination schemes, which are based on Dempster-Shafer evidence theory and fuzzy integral theory, respectively. Finally, a case study pertaining to the fault diagnosis for rotor-bearing systems is given in detail, and the results show that the proposed diagnosis strategies are feasible and more efficient than conventional stacked-vector methods

  14. Fault Diagnosis for Hydraulic Servo System Using Compressed Random Subspace Based ReliefF

    Yu Ding

    2018-01-01

    Full Text Available Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF (CRSR method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data.

  15. Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

    Li Sun

    2014-01-01

    Full Text Available Fluidized bed combustion (FBC boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD. The simulation results demonstrate that the approach is feasible and effective.

  16. Nonlinear Robust Observer-Based Fault Detection for Networked Suspension Control System of Maglev Train

    Yun Li

    2013-01-01

    Full Text Available A fault detection approach based on nonlinear robust observer is designed for the networked suspension control system of Maglev train with random induced time delay. First, considering random bounded time-delay and external disturbance, the nonlinear model of the networked suspension control system is established. Then, a nonlinear robust observer is designed using the input of the suspension gap. And the estimate error is proved to be bounded with arbitrary precision by adopting an appropriate parameter. When sensor faults happen, the residual between the real states and the observer outputs indicates which kind of sensor failures occurs. Finally, simulation results using the actual parameters of CMS-04 maglev train indicate that the proposed method is effective for maglev train.

  17. Fault-Tolerant Region-Based Control of an Underwater Vehicle with Kinematically Redundant Thrusters

    Zool H. Ismail

    2014-01-01

    Full Text Available This paper presents a new control approach for an underwater vehicle with a kinematically redundant thruster system. This control scheme is derived based on a fault-tolerant decomposition for thruster force allocation and a region control scheme for the tracking objective. Given a redundant thruster system, that is, six or more pairs of thrusters are used, the proposed redundancy resolution and region control scheme determine the number of thruster faults, as well as providing the reference thruster forces in order to keep the underwater vehicle within the desired region. The stability of the presented control law is proven in the sense of a Lyapunov function. Numerical simulations are performed with an omnidirectional underwater vehicle and the results of the proposed scheme illustrate the effectiveness in terms of optimizing the thruster forces.

  18. MAP Fault Localization Based on Wide Area Synchronous Phasor Measurement Information

    Zhang, Yagang; Wang, Zengping

    2015-02-01

    In the research of complicated electrical engineering, the emergence of phasor measurement units (PMU) is a landmark event. The establishment and application of wide area measurement system (WAMS) in power system has made widespread and profound influence on the safe and stable operation of complicated power system. In this paper, taking full advantage of wide area synchronous phasor measurement information provided by PMUs, we have carried out precise fault localization based on the principles of maximum posteriori probability (MAP). Large numbers of simulation experiments have confirmed that the results of MAP fault localization are accurate and reliable. Even if there are interferences from white Gaussian stochastic noise, the results from MAP classification are also identical to the actual real situation.

  19. Methodology for reliability allocation based on fault tree analysis and dualistic contrast

    TONG Lili; CAO Xuewu

    2008-01-01

    Reliability allocation is a difficult multi-objective optimization problem.This paper presents a methodology for reliability allocation that can be applied to determine the reliability characteristics of reactor systems or subsystems.The dualistic contrast,known as one of the most powerful tools for optimization problems,is applied to the reliability allocation model of a typical system in this article.And the fault tree analysis,deemed to be one of the effective methods of reliability analysis,is also adopted.Thus a failure rate allocation model based on the fault tree analysis and dualistic contrast is achieved.An application on the emergency diesel generator in the nuclear power plant is given to illustrate the proposed method.

  20. Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

    Jiang Xingmeng

    2016-01-01

    Full Text Available Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD and relevance vector machine (RVM is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD method.