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Sample records for active fault diagnosis

  1. Active fault diagnosis by temporary destabilization

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob

    2006-01-01

    An active fault diagnosis method for parametric or multiplicative faults is proposed. The method periodically adds a term to the controller that for a short period of time renders the system unstable if a fault has occurred, which facilitates rapid fault detection. An illustrative example is given....

  2. Fault tolerant control based on active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2005-01-01

    An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR...

  3. Active fault diagnosis by controller modification

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    2010-01-01

    Two active fault diagnosis methods for additive or parametric faults are proposed. Both methods are based on controller reconfiguration rather than on requiring an exogenous excitation signal, as it is otherwise common in active fault diagnosis. For the first method, it is assumed that the system...... in a way that guarantees the continuity of transition and global stability using a recent result on observer parameterization. An illustrative example inspired by a field study of a drag racing vehicle is given. For the second method, an active fault diagnosis method for parametric faults is proposed...

  4. A setup for active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2006-01-01

    A setup for active fault diagnosis (AFD) of parametric faults in dynamic systems is formulated in this paper. It is shown that it is possible to use the same setup for both open loop systems, closed loop systems based on a nominal feedback controller as well as for closed loop systems based...

  5. Active fault diagnosis in closed-loop uncertain systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2006-01-01

    Fault diagnosis of parametric faults in closed-loop uncertain systems by using an auxiliary input vector is considered in this paper, i.e. active fault diagnosis (AFD). The active fault diagnosis is based directly on the socalled fault signature matrix, related to the YJBK (Youla, Jabr, Bongiorno...... and Kucera) parameterization. Conditions are given for exact detection and isolation of parametric faults in closed-loop uncertain systems....

  6. Fault diagnosis

    Science.gov (United States)

    Abbott, Kathy

    1990-01-01

    The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to

  7. Active Fault Diagnosis in Sampled-data Systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2015-01-01

    The focus in this paper is on active fault diagnosis (AFD) in closed-loop sampleddata systems. Applying the same AFD architecture as for continuous-time systems does not directly result in the same set of closed-loop matrix transfer functions. For continuous-time systems, the LFT (linear fractional...... transformation) structure in the connection between the parametric faults and the matrix transfer function (also known as the fault signature matrix) applied for AFD is not directly preserved for sampled-data system. As a consequence of this, the AFD methods cannot directly be applied for sampled-data systems....... Two methods are considered in this paper to handle the fault signature matrix for sampled-data systems such that standard AFD methods can be applied. The first method is based on a discretization of the system such that the LFT structure is preserved resulting in the same LFT structure in the fault...

  8. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems....... These inputs are disturbance inputs, reference inputs and auxilary inputs. The diagnosis of the system is derived by an evaluation of the signature from the inputs in the residual outputs. The changes of the signatures form the external inputs are used for detection and isolation of the parametric faults....

  9. Active Fault Diagnosis and Assessment for Aircraft Health Management Project

    Data.gov (United States)

    National Aeronautics and Space Administration — To address the NASA LaRC need for innovative methods and tools for the diagnosis of aircraft faults and failures, Physical Optics Corporation (POC) proposes to...

  10. Fault Diagnosis in a Centrifugal Pump Using Active Magnetic Bearings

    Directory of Open Access Journals (Sweden)

    Rainer Nordmann

    2004-01-01

    compared to state-of-the-art diagnostic tools which are only based on the measurement of the systems outputs, i.e., displacements. In this article, the different steps of the model-based diagnosis, which are modeling, generation of significant features, respectively symptoms, fault detection, and the diagnosis procedure itself are presented and in particular, it is shown how an exemplary fault is detected and identified.

  11. Active Fault Diagnosis for Hybrid Systems Based on Sensitivity Analysis and EKF

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas

    2011-01-01

    An active fault diagnosis approach for different kinds of faults is proposed. The input of the approach is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision i...

  12. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    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...

  13. Rapprochement between Active Fault Diagnosis and Change Detection in ARMAX Systems

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2006-01-01

    The connection between AFD (Active Fault Diagnosis), ARMAX systems and RST controllers etc. are considered in this paper. It is shown that the applied setup in modern AFD for closed loop systems can be considered as a generalization of the setup used in connection with traditional methods for sys...... for system identification and controller design in the polynomial setting....

  14. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-10-13

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  15. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Peng Jiang

    2016-10-01

    Full Text Available Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB and a Lowest False Positive criterion (LFP, for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  16. Network Fault Diagnosis Using DSM

    Institute of Scientific and Technical Information of China (English)

    Jiang Hao; Yan Pu-liu; Chen Xiao; Wu Jing

    2004-01-01

    Difference similitude matrix (DSM) is effective in reducing information system with its higher reduction rate and higher validity. We use DSM method to analyze the fault data of computer networks and obtain the fault diagnosis rules. Through discretizing the relative value of fault data, we get the information system of the fault data. DSM method reduces the information system and gets the diagnosis rules. The simulation with the actual scenario shows that the fault diagnosis based on DSM can obtain few and effective rules.

  17. On infinite horizon active fault diagnosis for a class of non-linear non-Gaussian systems

    Directory of Open Access Journals (Sweden)

    Punčochár Ivo

    2014-12-01

    Full Text Available The paper considers the problem of active fault diagnosis for discrete-time stochastic systems over an infinite time horizon. It is assumed that the switching between a fault-free and finitely many faulty conditions can be modelled by a finite-state Markov chain and the continuous dynamics of the observed system can be described for the fault-free and each faulty condition by non-linear non-Gaussian models with a fully observed continuous state. The design of an optimal active fault detector that generates decisions and inputs improving the quality of detection is formulated as a dynamic optimization problem. As the optimal solution obtained by dynamic programming requires solving the Bellman functional equation, approximate techniques are employed to obtain a suboptimal active fault detector.

  18. Actuator Fault Detection and Diagnosis for Quadrotors

    NARCIS (Netherlands)

    Lu, P.; Van Kampen, E.-J.; Yu, B.

    2014-01-01

    This paper presents a method for fault detection and diagnosis of actuator loss of effectiveness for a quadrotor helicopter. This paper not only considers the detection of the actuator loss of effectiveness faults, but also addresses the diagnosis of the faults. The detection and estimation of the f

  19. Diagnosis and fault-tolerant control

    CERN Document Server

    Blanke, Mogens; Lunze, Jan; Staroswiecki, Marcel

    2016-01-01

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

  20. Diagnosis Method for Analog Circuit Hard fault and Soft Fault

    Directory of Open Access Journals (Sweden)

    Baoru Han

    2013-09-01

    Full Text Available Because the traditional BP neural network slow convergence speed, easily falling in local minimum and the learning process will appear oscillation phenomena. This paper introduces a tolerance analog circuit hard fault and soft fault diagnosis method based on adaptive learning rate and the additional momentum algorithm BP neural network. Firstly, tolerance analog circuit is simulated by OrCAD / Pspice circuit simulation software, accurately extracts fault waveform data by matlab program automatically. Secondly, using the adaptive learning rate and momentum BP algorithm to train neural network, and then applies it to analog circuit hard fault and soft fault diagnosis. With shorter training time, high precision and global convergence effectively reduces the misjudgment, missing, it can improve the accuracy of fault diagnosis and fast.  

  1. Quantitative Diagnosis of Fault Severity Trend of Rolling Element Bearings

    Institute of Scientific and Technical Information of China (English)

    CUI Lingli; MA Chunqing; ZHANG Feibin; WANG Huaqing

    2015-01-01

    The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy(WPCE) is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.

  2. Diagnosis and Fault-tolerant Control

    DEFF Research Database (Denmark)

    Blanke, Mogens; Kinnaert, Michel; Lunze, Jan;

    The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process...... the applicability of the presented methods. The theoretical results are illustrated by two running examples which are used throughout the book. The book addresses engineering students, engineers in industry and researchers who wish to get a survey over the variety of approaches to process diagnosis and fault...

  3. Fault diagnosis of active magnetic bearings based on Gaussian GLRT detector

    DEFF Research Database (Denmark)

    Nagel, Leon; Galeazzi, Roberto; Voigt, Andreas Jauernik

    2016-01-01

    generalized likelihood ratio test is proposed for detecting faults striking the electromagnet. The detector is capable of detecting and isolating the occurrence of faults in e.g. the windings of bearing by tracking changes in the mean value of a Gaussian distribution. The statistical distribution...... of the residuals in non faulty condition is characterized by experimental data of a full-scale bearing-rotor system. Verification of the detection performance is done through simulated data of a nonlinear model of the magnetic bearing calibrated against the real system....

  4. Fuzzy fault diagnosis system of MCFC

    Institute of Scientific and Technical Information of China (English)

    Wang Zhenlei; Qian Feng; Cao Guangyi

    2005-01-01

    A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.

  5. Fault Diagnosis in Deaerator Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    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.

  6. Navigation System Fault Diagnosis for Underwater Vehicle

    DEFF Research Database (Denmark)

    Falkenberg, Thomas; Gregersen, Rene Tavs; Blanke, Mogens

    2014-01-01

    This paper demonstrates fault diagnosis on unmanned underwater vehicles (UUV) based on analysis of structure of the nonlinear dynamics. Residuals are generated using dierent approaches in structural analysis followed by statistical change detection. Hypothesis testing thresholds are made signal b...

  7. Cooperative Human-Machine Fault Diagnosis

    Science.gov (United States)

    Remington, Roger; Palmer, Everett

    1987-02-01

    Current expert system technology does not permit complete automatic fault diagnosis; significant levels of human intervention are still required. This requirement dictates a need for a division of labor that recognizes the strengths and weaknesses of both human and machine diagnostic skills. Relevant findings from the literature on human cognition are combined with the results of reviews of aircrew performance with highly automated systems to suggest how the interface of a fault diagnostic expert system can be designed to assist human operators in verifying machine diagnoses and guiding interactive fault diagnosis. It is argued that the needs of the human operator should play an important role in the design of the knowledge base.

  8. Solving fault diagnosis problems linear synthesis techniques

    CERN Document Server

    Varga, Andreas

    2017-01-01

    This book addresses fault detection and isolation topics from a computational perspective. Unlike most existing literature, it bridges the gap between the existing well-developed theoretical results and the realm of reliable computational synthesis procedures. The model-based approach to fault detection and diagnosis has been the subject of ongoing research for the past few decades. While the theoretical aspects of fault diagnosis on the basis of linear models are well understood, most of the computational methods proposed for the synthesis of fault detection and isolation filters are not satisfactory from a numerical standpoint. Several features make this book unique in the fault detection literature: Solution of standard synthesis problems in the most general setting, for both continuous- and discrete-time systems, regardless of whether they are proper or not; consequently, the proposed synthesis procedures can solve a specific problem whenever a solution exists Emphasis on the best numerical algorithms to ...

  9. Transformer fault diagnosis using continuous sparse autoencoder.

    Science.gov (United States)

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

    2016-01-01

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

  10. Novel Fault Diagnosis Scheme for HVDC System via ESO

    Institute of Scientific and Technical Information of China (English)

    YAN Bing-yong; TIAN Zuo-hua; SHI Song-jiao

    2007-01-01

    A novel fault detection and identification (FDI) scheme for HVDC (High Voltage Direct Current Transmission) system was presented. It was based on the unique active disturbance rejection concept, where the HVDC system faults were estimated using an extended states observer (ESO). Firstly, the mathematical model of HVDC system was constructed, where the system states and disturbance were treated as an extended state. An augment HVDC system was established by using the extended state in rectify side and converter side, respectively. Then, a fault diagnosis filter was established to diagnose the HVDC system faults via the ESO theory. The evolution of the extended state in the augment HVDC system can reflect the actual system faults and disturbances, which can be used for the fault diagnosis purpose. A novel feature of this approach is that it can simultaneously detect and identify the shape and magnitude of the HVDC faults and disturbance. Finally, different kinds of HVDC faults were simulated to illustrate the feasibility and effectiveness of the proposed ESO based FDI approach. Compared with the neural network based or support vector machine based FDI approach, the ESO based FDI scheme can reduce the fault detection time dramatically and track the actual system fault accurately. What's more important, it needs not do complex online calculations and the training of neural network so that it can be applied into practice.

  11. Fault Diagnosis in HVAC Chillers

    Science.gov (United States)

    Choi, Kihoon; Namuru, Setu M.; Azam, Mohammad S.; Luo, Jianhui; Pattipati, Krishna R.; Patterson-Hine, Ann

    2005-01-01

    Modern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for fault detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.

  12. Fault Diagnosis of Autonomous Underwater Vehicles

    Directory of Open Access Journals (Sweden)

    Xiao Liang

    2013-04-01

    Full Text Available In this study, we propose the least disturbance algorithm adding scale factor and shift factor. The dynamic learning ratio can be calculated to minimize the scale factor and shift factor of wavelet function and the variation of net weights and the algorithm improve the stability and the convergence of wavelet neural network. It was applied to build the dynamical model of autonomous underwater vehicles and the residuals are generated by comparing the outputs of the dynamical model with the real state values in the condition of thruster fault. Fault detection rules are distilled by residual analysis to execute thruster fault diagnosis. The results of simulation prove the effectiveness.

  13. Combination of Fault Tree and Neural Networks in Excavator Diagnosis

    OpenAIRE

    Li Guoping; Zhang Qingwei; Ma Xiao

    2013-01-01

    By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA) and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree...

  14. Efficient RT-Level Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    Ozgur Sinanoglu; Alex Orailoglu

    2005-01-01

    Increasing IC densities necessitate diagnosis methodologies with enhanced defect locating capabilities. Yet the computational effort expended in extracting diagnostic information and the stringent storage requirements constitute major concerns due to the tremendous number of faults in typical ICs. In this paper, we propose an RT-level diagnosis methodology capable of responding to these challenges. In the proposed scheme, diagnostic information is computed on a grouped fault effect basis, enhancing both the storage and the computational aspects. The fault effect grouping criteria are identified based on a module structure analysis, improving the propagation ability of the diagnostic information through RT modules. Experimental results show that the proposed methodology provides superior speed-ups and significant diagnostic information compression at no sacrifice in diagnostic resolution, compared to the existing gate-level diagnosis approaches.

  15. A methodology for distributed fault diagnosis

    Science.gov (United States)

    Gupta, V.; Puig, V.; Blesa, J.

    2017-01-01

    In this paper, a methodology for distributed fault diagnosis is proposed. The algorithm places the sensors in a system in such a manner that the partition of a system into various subsystems becomes easier facilitating the implementation of a distributed fault diagnosis system. This algorithm also reduces or minimized the number of sensors to be used or install thus reducing overall cost. Binary integer linear programming is used for optimization in this algorithm. Real case study of Barcelona water network has been used to demonstrate and validate the proposed algorithm.

  16. Sensor fault diagnosis with a probabilistic decision process

    Science.gov (United States)

    Sharifi, Reza; Langari, Reza

    2013-01-01

    In this paper a probabilistic approach to sensor fault diagnosis is presented. The proposed method is applicable to systems whose dynamic can be approximated with only few active states, especially in process control where we usually have a relatively slow dynamics. Unlike most existing probabilistic approaches to fault diagnosis, which are based on Bayesian Belief Networks, in this approach the probabilistic model is directly extracted from a parity equation. The relevant parity equation can be found using a model of the system or through principal component analysis of data measured from the system. In addition, a sensor detectability index is introduced that specifies the level of detectability of sensor faults in a set of analytically redundant sensors. This index depends only on the internal relationships of the variables of the system and noise level. The method is tested on a model of the Tennessee Eastman process and the result shows a fast and reliable prediction of fault in the detectable sensors.

  17. Faults and Diagnosis Systems in Power Converters

    DEFF Research Database (Denmark)

    Lee, Kyo-Beum; Choi, Uimin

    2014-01-01

    efforts have been put into making these systems better in terms of reliability in order to achieve high power source availability, reduce the cost of energy and also increase the reliability of overall systems. Among the components used in power converters, a power device and a capacitor fault occurs most......A power converter is needed in almost all kinds of renewable energy systems and drive systems. It is used both for controlling the renewable source and for interfacing with the load, which can be grid-connected or working in standalone mode. Further, it drives the motors efficiently. Increasing...... frequently. Therefore, it is important to monitor the power device and capacitor fault to increase the reliability of power electronics. In this chapter, the diagnosis methods for power device fault will be discussed by dividing into open- and short-circuit faults. Then, the condition monitoring methods...

  18. Mechanical Fault Diagnosis Using Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    LI Ling-jun; ZHANG Zhou-suo; HE Zheng-jia

    2003-01-01

    The Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory ( SLT) , which can get good classification effects even with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents a SVM-based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearings is conducted. The vibration signals acquired from the bearings are used directly in the calculating without the preprocessing of extracting its features. Compared with the methods based on Artificial Neural Network (ANN), the SVM-based meth-od has desirable advantages. It is applicable for on-line diagnosis of mechanical systems.

  19. Data-Driven Adaptive Observer for Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Shen Yin

    2012-01-01

    Full Text Available This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

  20. A Dynamic Integrated Fault Diagnosis Method for Power Transformers

    Directory of Open Access Journals (Sweden)

    Wensheng Gao

    2015-01-01

    Full Text Available In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

  1. Study on Knowledge -based Intelligent Fault Diagnosis of Hydraulic System

    Directory of Open Access Journals (Sweden)

    Xuexia Liu

    2012-12-01

    Full Text Available A general framework of hydraulic fault diagnosis system was studied. It consisted of equipment knowledge bases, real-time databases, fusion reasoning module, knowledge acquisition module and so on. A tree-structure model of fault knowledge was established. Fault nodes knowledge was encapsulated by object-oriented technique. Complete knowledge bases were made including fault bases and diagnosis bases. It could describe the fault positions, the structure of fault, cause-symptom relationships, diagnosis principles and other knowledge. Taking the fault of left and right lifting oil cylinder out of sync for example, the diagnostic results show that the methods were effective.

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

    CERN Document Server

    Shen, Qikun; Shi, Peng

    2017-01-01

    This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .

  3. Combination of Fault Tree and Neural Networks in Excavator Diagnosis

    Directory of Open Access Journals (Sweden)

    Li Guoping

    2013-04-01

    Full Text Available By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device.

  4. Application of the fault diagnosis strategy based on hierarchical information fusion in motors fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.

  5. Bispectrum Analysis in Fault Diagnosis of Gears

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The application of bispectrum analysis in fault diagnosis of gears is studied in this paper. Bispectrum analysis is capable of removing Gaussian or symmetric non-Gaussian noise and providing more information than power spectrum analysis. The results of the research show that normal gear signals, cracked gear signals and broken gear signals can be easily distinguished by using bispectrum as the signal features. The bispectrum diagonal slice Bx(ω1,ω2) can be used to identify the gear condition automatically.

  6. Fault Diagnosis and Fault-Tolerant Control of Uncertain Robot Manipulators Using High-Order Sliding Mode

    Directory of Open Access Journals (Sweden)

    Mien Van

    2016-01-01

    Full Text Available A robust fault diagnosis and fault-tolerant control (FTC system for uncertain robot manipulators without joint velocity measurement is presented. The actuator faults and robot manipulator component faults are considered. The proposed scheme is designed via an active fault-tolerant control strategy by combining a fault diagnosis scheme based on a super-twisting third-order sliding mode (STW-TOSM observer with a robust super-twisting second-order sliding mode (STW-SOSM controller. Compared to the existing FTC methods, the proposed FTC method can accommodate not only faults but also uncertainties, and it does not require a velocity measurement. In addition, because the proposed scheme is designed based on the high-order sliding mode (HOSM observer/controller strategy, it exhibits fast convergence, high accuracy, and less chattering. Finally, computer simulation results for a PUMA560 robot are obtained to verify the effectiveness of the proposed strategy.

  7. Diagnosis and Fault-tolerant Control, 3rd Edition

    DEFF Research Database (Denmark)

    Blanke, Mogens; Kinnaert, Michel; Lunze, Jan;

    The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process...

  8. Application of ActiveX technology in Remote Fault Diagnosis System based on B/S%ActiveX技术在基于B/S模式的远程故障诊断系统中的应用

    Institute of Scientific and Technical Information of China (English)

    刘平; 魏文军

    2012-01-01

    Firstly the Browser/Server structure and the characteristic of ActiveX technology were introduced. Combined with equipment fault diagnosis, a model of Remote Fault Diagnosis System based on Browser/ Server mode was designed. ActiveX technology was utilized in the System, and Web pages embraced ActiveX controls were downloaded on the client through browser. The function of remote fault diagnosis was achieved through the properties and methods of ActiveX controls, finally the issue of ActiveX controls maintenance and security were illustrated.%介绍了B/S结构和ActiveX技术的特点,结合设备故障诊断设计一种基于B/S模式的远程故障诊断系统的模型.系统中应用ActiveX技术,在客户端通过浏览器下载包含ActiveX控件的Web页面,通过ActiveX控件的属性和方法来帮助实现远程故障诊断的功能,最后对ActiveX控件的升级维护及安全性问题加以说明.

  9. Active Fault Isolation in MIMO Systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    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 iso...

  10. Synthetic Intelligent Fault Diagnosis Technology for Complex Process

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A fault diagnosis method of knowledge-based fuzzy neural network is proposed for complex process, which is hard to develop practical mathematical model. Fault detection is performed through a knowledge-based system, where fault detection heuristic rules have been generated from deep and shallow knowledge of the process. The fuzzy neural network performs the fault diagnosis task. This method does not need practical mathematical models of objects, so it is a strong implement for complex process.

  11. Robust fault diagnosis for a class of nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Zhanshan WANG; Huaguang ZHANG

    2006-01-01

    Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.

  12. Intelligent Fault Diagnosis in Lead-zinc Smelting Process

    Institute of Scientific and Technical Information of China (English)

    Wei-Hua Gui; Chun-Hua Yang; Jing Teng

    2007-01-01

    According to the fault characteristic of the imperial smelting process (ISP), a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault symptom and expert system is employed for effective fault diagnosis of the process. Furthermore, fuzzy abductive inference is introduced to diagnose multiple faults. Feasibility of the proposed system is demonstrated through a pilot plant case study.

  13. Bevel Gearbox Fault Diagnosis using Vibration Measurements

    Directory of Open Access Journals (Sweden)

    Hartono Dennis

    2016-01-01

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

  14. Intelligent Fault Diagnosis in a Power Distribution Network

    Directory of Open Access Journals (Sweden)

    Oluleke O. Babayomi

    2016-01-01

    Full Text Available This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.

  15. Fault diagnosis with the Aladdin transient classifier

    Science.gov (United States)

    Roverso, Davide

    2003-08-01

    The purpose of Aladdin is to assist plant operators in the early detection and diagnosis of faults and anomalies in the plant that either have an impact on the plant performance, or that could lead to a plant shutdown or component damage if allowed to go unnoticed. The kind of early fault detection and diagnosis performed by Aladdin is aimed at allowing more time for decision making, increasing the operator awareness, reducing component damage, and supporting improved plant availability and reliability. In this paper we describe in broad lines the Aladdin transient classifier, which combines techniques such as recurrent neural network ensembles, Wavelet On-Line Pre-processing (WOLP), and Autonomous Recursive Task Decomposition (ARTD), in an attempt to improve the practical applicability and scalability of this type of systems to real processes and machinery. The paper focuses then on describing an application of Aladdin to a Nuclear Power Plant (NPP) through the use of the HAMBO experimental simulator of the Forsmark 3 boiling water reactor NPP in Sweden. It should be pointed out that Aladdin is not necessarily restricted to applications in NPPs. Other types of power plants, or even other types of processes, can also benefit from the diagnostic capabilities of Aladdin.

  16. Fault Diagnosis of Power Systems Using Intelligent Systems

    Science.gov (United States)

    Momoh, James A.; Oliver, Walter E. , Jr.

    1996-01-01

    The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based fault diagnosis tool. Such a tool will allow NASA ground controllers to re-establish a normal or near-normal degraded operating state of the EPS (a DC power system) for Space Station Alpha by isolating the faulted branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any faults in them. A proposed solution involves using the Fault Diagnosis Intelligent System (FDIS) to perform near-real time fault diagnosis of Alpha's EPS by downloading power transient telemetry at fault-time from onboard data loggers. The FDIS uses an ANN clustering algorithm augmented with a wavelet transform feature extractor. This combination enables this system to perform pattern recognition of the power transient signatures to diagnose the fault type and its location down to the orbital replaceable unit. FDIS has been tested using a simulation of the LeRC Testbed Space Station Freedom configuration including the topology from the DDCU's to the electrical loads attached to the TPDU's. FDIS will work in conjunction with the Power Management Load Scheduler to determine what the state of the system was at the time of the fault condition. This information is used to activate the appropriate diagnostic section, and to refine if necessary the solution obtained. In the latter case, if the FDIS reports back that it is equally likely that the faulty device as 'start tracker #1' and 'time generation unit,' then based on a priori knowledge of the system's state, the refined solution would be 'star tracker #1' located in cabinet ITAS2. It is concluded from the present studies that artificial intelligence diagnostic abilities are improved with the addition of the wavelet transform, and that when such a system such as FDIS is coupled to the Power Management Load Scheduler, a faulty device can be located and isolated

  17. Fault Diagnosis and Fault Tolerant Control with Application on a Wind Turbine Low Speed Shaft Encoder

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Sardi, Hector Eloy Sanchez; Escobet, Teressa

    2015-01-01

    . This sensor has to be correct as blade pitch actions should be different at different azimuth angle as the wind speed varies within the rotor field due to different phenomena. A scheme detecting faults in this sensor has previously been designed for the application of a high end fault diagnosis and fault...... tolerant control of wind turbines using a benchmark model. In this paper, the fault diagnosis scheme is improved and integrated with a fault accommodation scheme which enables and disables the individual pitch algorithm based on the fault detection. In this way, the blade and tower loads are not increased...

  18. Mine-Hoist Active Fault Tolerant Control System and Strategy

    Institute of Scientific and Technical Information of China (English)

    WANG Zhi-jie; WANG Yao-cai; MENG Jiang; ZHAO Peng-cheng; CHANG Yan-wei

    2005-01-01

    Based on fault diagnosis and fault tolerant technologies, the mine-hoist active fault-tolerant control system (MAFCS) is presented with corresponding strategies,, which includes the fault diagnosis module (FDM), the dynamic library (DL) and the fault-tolerant control module (FCM). When a fault is judged from some sensor by FDM, FCM reconfigure the state of MAFCS by calling the parameters from all sub libraries in DL, in order to ensure the reliability and safety of mine hoist. The simulating result shows that, MAFCS is of certain intelligence, which can adopt the corresponding control strategies according to different fault modes, even when there are quite difference between the real data and the prior fault modes.

  19. Optimal constructions for active diagnosis

    OpenAIRE

    Haar, Stefan; Haddad, Serge; Melliti, Tarek; Schwoon, Stefan

    2017-01-01

    International audience; Diagnosis is the task of detecting fault occurrences in a partially observed system. Depending on the possible observations, a discrete-event system may be diagnosable or not. Active diagnosis aims at controlling the system to render it diagnosable. Past research has proposed solutions for this problem, but their complexity remains to be improved. Here, we solve the decision and synthesis problems for active diagnosability, proving that (1) our procedures are optimal w...

  20. Parity space-based fault diagnosis of CCBII braking system

    Institute of Scientific and Technical Information of China (English)

    黄志武; 杨迎泽; 王晶; 李赟

    2013-01-01

    Fault diagnosis is a key issue of the CCBII(computer controlled brake II) braking system, because the CCBII braking system is very complicated and nonlinear, which may exhibit isolated and multi-component coupled faults. A parity space-based method was proposed for fault diagnosis of CCBII braking systems. Firstly, the mathematical models were established according to three function modules of CCBII braking systems where the air fluid theory was utilized. Then, parity vector and threshold function were designed for each output of the system so as to identify more system faults. Fault character matrix was built based on the causal relationship between the output and the fault according to the system function and internal structure. Finally, fault detection and isolation can be realized by the comparison of the observed system output and the fault character matrix. Simulation results show that the proposed method is entirely feasible and effective.

  1. Improved wavelet analysis for induction motors mixed-fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hanlei; ZHOU Jiemin; LI Gang

    2007-01-01

    Eccentricity is one of the frequent faults of induction motors,and it may cause rub between the rotor and the stator.Early detection of significant rub from pure eccentricity can prolong the lifespan of induction motors.This paper is devoted to such mixed-fault diagnosis:eccentricity plus rub fault.The continuous wavelet transform(CWT)is employed to analyze vibration signals obtained from the motor body.An improved continuous wavelet trartsform was proposed to alleviate the frequency aliasing.Experimental results show that the proposed method can effectively distinguish two types of faults,single-fault of eccentricity and mixed-fault of eccentricity plus rub.

  2. Application of Ferrography to Fault Diagnosis of Hydraulic Systems

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper deals with research on the successful use of ferrography as a wear measurement method for condition monitoring and fault diagnosis of hydraulic systems.The analysis program and progression is discussed, and a case study for condition monitoring and fault diagnosis of hydraulic systems by means of ferrography is also reviewed.

  3. A Complete Analytic Model for Fault Diagnosis of Power Systems

    Institute of Scientific and Technical Information of China (English)

    LIU Daobing; GU Xueping; LI Haipeng

    2011-01-01

    Interconnections of the modem bulk electric power systems, while contributing to the operating economy and reliability by means of mutual assistance between the subsystems, result in an increased complexity of fault diagnosis and a more serious consequence of misdiagnosis. The online fault diagnosis has become a more challenging problem for dispatchers to operate a power system securely,

  4. Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

    OpenAIRE

    Weigang Wen; Gao, Robert X.; Weidong Cheng

    2016-01-01

    The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by w...

  5. Data-Driven Adaptive Observer for Fault Diagnosis

    OpenAIRE

    Shen Yin; Xuebo Yang; Hamid Reza Karimi

    2012-01-01

    This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After...

  6. Application of extension method to fault diagnosis of transformer

    Institute of Scientific and Technical Information of China (English)

    DENG Hong-gui; CAO Jian; LUO An; XIA Xiang-yang

    2007-01-01

    A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively and quantitatively describing the fault diagnosis problem of power transformers. The degree of relation based on the dependent functions WaS employed to determine then ature and the grade of the faults in a transformer system.And the proposed method was verified with the experimental data.The results show that accuracy rate of the diagnosis method exceeds 90% and two kinds of faults call be detected at the same time.

  7. Fault Diagnosis and Fault Handling for Autonomous Aircraft

    DEFF Research Database (Denmark)

    Hansen, Søren

    Unmanned Aerial vehicles (UAVs) or drones are used increasingly for missions where piloted aircraft are unsuitable. The unmanned aircraft has a number of advantages with respect to size, weight and manoeuvrability that makes it possible for them to solve tasks that an aircraft previously has been...... that the fault is discovered in time such that appropriate actions can be taken. That could either be the aircraft controlling computer taking the fault into account or a human operator that intervenes. Detection of faults that occur during flight is exactly the subject of this thesis. Safety towards faults...... to another type of aircraft with different parameters. Amongst the main findings of this research project is a method to handle faults on the UAV’s pitot tube, which measures the aircraft speed. A set of software redundancies based on GPS velocity information and engine thrust are used to detect abnormal...

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

    CERN Document Server

    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...

  9. Fault Diagnosis of Nonlinear Systems Using Structured Augmented State Models

    Institute of Scientific and Technical Information of China (English)

    Jochen Aβfalg; Frank Allg(o)wer

    2007-01-01

    This paper presents an internal model approach for modeling and diagnostic functionality design for nonlinear systems operating subject to single- and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems that are switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and exemplified for the fault diagnosis of the well-known three-tank benchmark. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance.

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

    Science.gov (United States)

    Du, Jun; Wang, Shaoping; Zhang, Haiyan

    2013-04-01

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

  11. Fault Diagnosis of Machine Based on Fuzzy Reliability Theory

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    According to life analysis in reliability theory, certain diagnosis rules can be used to diagnose machines' faults. On this basis, considering the indefiniteness in machine working states, the accurate diagnosis rule was extended to fuzzy diagnosis rule by using basic concepts and methods of fuzzy mathematics. The formulas of fault probability under different conditions were deduced. In the end, an example is given and the results of two methods were compared.

  12. Research and application of hierarchical model for multiple fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    An Ruoming; Jiang Xingwei; Song Zhengji

    2005-01-01

    Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well-known Mozetic's approach, a novel hierarchical model-based diagnosis methodology is put forward for improving efficiency of multi-fault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used: one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are given to show the superiority of the presented technique.

  13. Data-driven design of fault diagnosis and fault-tolerant control systems

    CERN Document Server

    Ding, Steven X

    2014-01-01

    Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

  14. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    Directory of Open Access Journals (Sweden)

    Xiang Wang

    2015-07-01

    Full Text Available 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.

  15. Online Fault Diagnosis Method Based on Nonlinear Spectral Analysis

    Institute of Scientific and Technical Information of China (English)

    WEI Rui-xuan; WU Li-xun; WANG Yong-chang; HAN Chong-zhao

    2005-01-01

    The fault diagnosis based on nonlinear spectral analysis is a new technique for the nonlinear fault diagnosis, but its online application could be limited because of the enormous compution requirements for the estimation of general frequency response functions. Based on the fully decoupled Volterra identification algorithm, a new online fault diagnosis method based on nonlinear spectral analysis is presented, which can availably reduce the online compution requirements of general frequency response functions. The composition and working principle of the method are described, the test experiments have been done for damping spring of a vehicle suspension system by utilizing the new method, and the results indicate that the method is efficient.

  16. Application of MBAM Neural Network in CNC Machine Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    宋刚; 胡德金

    2004-01-01

    In order to improve the bidirectional associative memory (BAM) performance, a modified BAM model (MBAM) is used to enhance neural network(NN)'s memory capacity and error correction capability, theoretical analysis and experiment results illuminate that MBAM performs much better than the original BAM. The MBAM is used in computer numeric control(CNC) machine fault diagnosis, it not only can complete fault diagnosis correctly but also have fairly high error correction capability for disturbed Input Information sequence. Moreover MBAM model is a more convenient and effective method of solving the problem of CNC electric system fault diagnosis.

  17. Fault Diagnosis and Reliability Analysis Using Fuzzy Logic Method

    Institute of Scientific and Technical Information of China (English)

    Miao Zhinong; Xu Yang; Zhao Xiangyu

    2006-01-01

    A new fuzzy logic fault diagnosis method is proposed. In this method, fuzzy equations are employed to estimate the component state of a system based on the measured system performance and the relationship between component state and system performance which is called as "performance-parameter" knowledge base and constructed by expert. Compared with the traditional fault diagnosis method, this fuzzy logic method can use humans intuitive knowledge and dose not need a precise mapping between system performance and component state. Simulation proves its effectiveness in fault diagnosis. Then, the reliability analysis is performed based on the fuzzy logic method.

  18. Application of General fractal Dimension to Coupling Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper presents the coucept of general and sensitive dimension, and also proposes the calculation formula of the general dimension least squares method. By calculating and analyzing the power spectrum and general dimension from the fault sample, the relationship is achieved between sample status and the general dimension from vibration signals function of general dimension is proposed, and calculations are carried out for a monitor signal and samples signal. The diagnosis method based on fractal theory is effective through the concrete examples of the steam-electric generating set fault diagnosis, and the correlation coefficient of general dimension between a monitor signal and samples signal can improve the accuracy for fault diagnosis.

  19. Fault diagnosis and fault-tolerant control of photovoltaic micro-inverter

    Institute of Scientific and Technical Information of China (English)

    李舟; 彭涛; 张鹏飞; 韩华; 杨建

    2016-01-01

    An observer-based fault diagnosis method and a fault tolerant control for open-switch fault and current sensor fault are proposed for interleaved flyback converters of a micro-inverter system. First, based on the topology of a grid-connected micro-inverter, a mathematical model of the flyback converters is established. Second, a state observer is applied to estimate the currents online and generate corresponding residuals. The fault is diagnosed by comparing the residuals with the thresholds. Finally, a fault-tolerant control that consists of a fault-tolerant topology for the faulty switch and a simple software redundancy control for the faulty current sensor, is proposed to achieve a fault-tolerant operation. The feasibility and effectiveness of the proposed method has been verified by simulation and experimental results.

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

    Energy Technology Data Exchange (ETDEWEB)

    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)

  1. Failure characteristics analysis and fault diagnosis for liquid rocket engines

    CERN Document Server

    Zhang, Wei

    2016-01-01

    This book concentrates on the subject of health monitoring technology of Liquid Rocket Engine (LRE), including its failure analysis, fault diagnosis and fault prediction. Since no similar issue has been published, the failure pattern and mechanism analysis of the LRE from the system stage are of particular interest to the readers. Furthermore, application cases used to validate the efficacy of the fault diagnosis and prediction methods of the LRE are different from the others. The readers can learn the system stage modeling, analyzing and testing methods of the LRE system as well as corresponding fault diagnosis and prediction methods. This book will benefit researchers and students who are pursuing aerospace technology, fault detection, diagnostics and corresponding applications.

  2. Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

    Directory of Open Access Journals (Sweden)

    Shoubin Wang

    2014-01-01

    Full Text Available As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.

  3. Diagnosis of airspeed measurement faults for unmanned aerial vehicles

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2014-01-01

    Airspeed sensor faults are common causes for incidents with unmanned aerial vehicles with pitot tube clogging or icing being the most common causes. Timely diagnosis of such faults or other artifacts in signals from airspeed sensing systems could potentially prevent crashes. This paper employs...

  4. Fault diagnosis of a Wind Turbine Rotor using a Multi-blade Coordinate Framework

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2012-01-01

    Fault diagnosis of a wind turbine rotor is considered. The faults considered are sensor faults and blades mounted with a pitch offset. A fault at a single blade will result in asymmetries in the rotor, which can be applied for fault diagnosis. The diagnosis is derived by using the multiblade coor...

  5. Robust Fault Diagnosis Algorithm for a Class of Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Hai-gang Xu

    2015-01-01

    Full Text Available A kind of robust fault diagnosis algorithm to Lipschitz nonlinear system is proposed. The novel disturbances constraint condition of the nonlinear system is derived by group algebra method, and the novel constraint condition can meet the system stability performance. Besides, the defined robust performance index of fault diagnosis observer guarantees the robust. Finally, the effectiveness of the algorithm proposed is proved in the simulations.

  6. A Neural Network Appraoch to Fault Diagnosis in Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    尉乃红; 杨士元; 等

    1996-01-01

    Thia paper presents a neural network based fault diagnosis approach for analog circuits,taking the tolerances of circuit elements into account.Specifically,a normalization rule of input information,a pseudo-fault domain border(PFDB)pattern selection method and a new output error function are proposed for training the backpropagation(BP) network to be a fault diagnoser.Experimental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy,and provides at least an order-of-magnitude improvement in post-fault diagnostic speed.

  7. Fault Diagnosis for Fuel Cell Based on Naive Bayesian Classification

    Directory of Open Access Journals (Sweden)

    Liping Fan

    2013-07-01

    Full Text Available Many kinds of uncertain factors may exist in the process of fault diagnosis and affect diagnostic results. Bayesian network is one of the most effective theoretical models for uncertain knowledge expression and reasoning. The method of naive Bayesian classification is used in this paper in fault diagnosis of a proton exchange membrane fuel cell (PEMFC system. Based on the model of PEMFC, fault data are obtained through simulation experiment, learning and training of the naive Bayesian classification are finished, and some testing samples are selected to validate this method. Simulation results demonstrate that the method is feasible.    

  8. Fault Diagnosis of an Intelligent Building Facility Using Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qi-ding; XU Jin-yu; BAI Er-lei

    2008-01-01

    There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model for fault diagnosis of the refrigeration system of an intelligent building facility, gave the networks parameters, and analyzed the reasoning mechanism. Based on the model, some data was analyzed and diagnosed by adopting Bayesian networks reasoning platform GeNIe. The result shows that the diagnosis effect is more comprehensive and reasonable than the other method.

  9. Study on BSS Algorithm used on Fault Diagnosis of Gearbox

    Directory of Open Access Journals (Sweden)

    Yu Chen

    2013-06-01

    Full Text Available The gearbox is a complicated rotating machinery equipment, in order to realize the gearbox fault early detection and prevention, it is the key to carry out the online diagnosis. This paper used the adaptive variable step-length natural gradient blind source separation algorithm to realize the helicopter gearbox meshing vibration signal and fault vibration signal effective separation. Through the algorithm simulation, the accuracy of the algorithm gained the verification and the separation error trended to zero, which has higher separation precision. This algorithm can realize the complicated mechanical vibration signal blind source separation and fault diagnosis, which has a broad application prospect.

  10. Fault diagnosis with neural networks. Part 1: Trajectory recognition

    Directory of Open Access Journals (Sweden)

    Enrique Eduardo Tarifa

    2010-04-01

    Full Text Available The present investigation was focused on formulating a method for designing a fault diagnosis system for chemical plants by using artificial neural networks. Fault diagnosis is aimed at identifying a fault which affects a given process by analysing the signs supplied by process sensors. Neuronal networks are mathematical models which try to imitate the functioning of the human brain. A neural network is defined by its structure and the learning method used. The difficulty with diagnosing faults lies in recognising the tralectories (temporal series of data followed by process variables when a fault affects the process; when tralectories are recognised, the associated fault is also identified. The theory so developed recommended an optimised structure and training method for the neural networks to use. Both the proposed structure and the training method were tested by carrying out comparative studies between traditional structures and a training method. The results showed the superiority of the neural networks designed and trained with the method proposed in this work. Except for simple processes, fault diagnosis is a more complex problem than simply identifying tralectories, because a fault may cause an infinite set of tralectories (i.e. flow. The fundaments established in this work are thus used in Part II, where the analysis is extended to recognise flows.

  11. Knowledge-driven board-level functional fault diagnosis

    CERN Document Server

    Ye, Fangming; Chakrabarty, Krishnendu; Gu, Xinli

    2017-01-01

    This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evalua...

  12. Node Grouping in System-Level Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Dafang; XIE Gaogang; MIN Yinghua

    2001-01-01

    With the popularization of network applications and multiprocessor systems,dependability of systems has drawn considerable attention. This paper presents a new technique of node grouping for system-level fault diagnosis to simplify the complexity of large system diagnosis. The technique transforms a complicated system to a group network, where each group may consist of many nodes that are either fault-free or faulty. It is proven that the transformation leads to a unique group network to ease system diagnosis. Then it studies systematically one-step t-faults diagnosis problem based on node grouping by means of the concept of independent point sets and gives a simple sufficient and necessary condition. The paper presents a diagnosis procedure for t-diagnosable systems. Furthermore, an efficient probabilistic diagnosis algorithm for practical applications is proposed based on the belief that most of the nodes in a system are fault-free. The result of software simulation shows that the probabilistic diagnosis provides high probability of correct diagnosis and low diagnosis cost, and is suitable for systems of any kind of topology.

  13. Nonlinear fault diagnosis method based on kernel principal component analysis

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu; Zhang Chunkai; Shao Huihe

    2005-01-01

    To ensure the system run under working order, detection and diagnosis of faults play an important role in industrial process. This paper proposed a nonlinear fault diagnosis method based on kernel principal component analysis (KPCA). In proposed method, using essential information of nonlinear system extracted by KPCA, we constructed KPCA model of nonlinear system under normal working condition. Then new data were projected onto the KPCA model. When new data are incompatible with the KPCA model, it can be concluded that the nonlinear system isout of normal working condition. Proposed method was applied to fault diagnosison rolling bearings. Simulation results show proposed method provides an effective method for fault detection and diagnosis of nonlinear system.

  14. Fault detection and diagnosis of diesel engine valve trains

    Science.gov (United States)

    Flett, Justin; Bone, Gary M.

    2016-05-01

    This paper presents the development of a fault detection and diagnosis (FDD) system for use with a diesel internal combustion engine (ICE) valve train. A novel feature is generated for each of the valve closing and combustion impacts. Deformed valve spring faults and abnormal valve clearance faults were seeded on a diesel engine instrumented with one accelerometer. Five classification methods were implemented experimentally and compared. The FDD system using the Naïve-Bayes classification method produced the best overall performance, with a lowest detection accuracy (DA) of 99.95% and a lowest classification accuracy (CA) of 99.95% for the spring faults occurring on individual valves. The lowest DA and CA values for multiple faults occurring simultaneously were 99.95% and 92.45%, respectively. The DA and CA results demonstrate the accuracy of our FDD system for diesel ICE valve train fault scenarios not previously addressed in the literature.

  15. Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

    Directory of Open Access Journals (Sweden)

    Weigang Wen

    2016-01-01

    Full Text Available The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

  16. Recognition of Active Faults and Stress Field

    Science.gov (United States)

    Azuma, T.

    2012-12-01

    Around the plate-boundary region, the directions of maximum and minimum stress related to the plate motion is one of the key for the recognition of active faults. For example, it is typical idea that there are many N-S trading reverse faults, NE-SW and NW-SE trending strike slip faults and less normal faults (only near volcanoes) in Japan, where the compressional stress with E-W direction is dominant caused by the motion of the subduction of the Pacific Plate beneath the North American Plate. After the 2011 Tohoku earthquake (Mj 9.0), however, many earthquakes with the mechanism of the normal fault type occurred in the coastal region of the northern-east Japan. On 11th April 2011, the Fukushima Hamadori Earthquake (Mj 7.0) occurred accompanying surface faults along two faults, the Idosawa fault and the Yunotake fault, that recognized as active faults by the Research Group for Active Fault of Japan (1980, 1991). It impacted on active fault study by the reason of not only the appearance of two traces of significant surface faults with maximum displacement up to 2.1 m, but also the reactivation of the normal faults under the E-W compressional stress field. When we identify the active faults, it is one of the key whether the direction of slip on the fault consists with the stress field in that area or not. And there is a technique to recognized whether the fault is active or not by using the data of the direction of stress in the field and the geometry of the fault plane. Though it is useful for the fault in the rock without overlain Quaternary deposits, we should care that the active faults may react caused by the temporal stress condition after the generation of large earthquakes.

  17. Active Fault Research (1996); Katsudanso kenkyu (1996)

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-03-25

    This is a general collection of papers dealing with the research of active faults. In Japan, since very heavy damage was produced by the Hyogoken-Nambu earthquake of January, 1955, discussion of active faults has promptly grown very active. In relation to the said earthquake, detailed maps of earthquake faults that emerged in the same, trench investigations of the Awajishima surface fault rupture related to the same, and the circumstances of the southern and northern ends of the Nojima earthquake fault are reported. Discussion is made about the re-examination of precaution faults and the possibility of the presence of C-class active faults, dealing with the entirety of Japan. Itemized discussion covers the fossil liquefaction observed on the campus of Hokkaido University, fault outcrop at the geological boundary west of Hanamaki and at the western edge of the Kitakami lowland, morphology at the Median Tectonic Line active fault system Iyo fault, fault outcrop discovered at the Iwakuni active fault system Otake fault, and the Kokura Higashi fault and the topography surrounding it (northern part of Kyushu) are introduced. Furthermore, there are reports on the F1 fault and neotectonics in the Tan-Lu fracture zone in the Linyi area, Shandong Province, eastern part of China.

  18. Improving Robustness of Network Fault Diagnosis to Uncertainty in Observations

    DEFF Research Database (Denmark)

    Grønbæk, Lars Jesper; Schwefel, Hans-Peter; Ceccarelli, Andrea

    2010-01-01

    Performing decentralized network fault diagnosis based on network traffic is challenging. Besides inherent stochastic behaviour of observations, measurements may be subject to errors degrading diagnosis timeliness and accuracy. In this paper we present a novel approach in which we aim to mitigate...

  19. Electric machines modeling, condition monitoring, and fault diagnosis

    CERN Document Server

    Toliyat, Hamid A; Choi, Seungdeog; Meshgin-Kelk, Homayoun

    2012-01-01

    With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condi

  20. Fault diagnosis and prognostic of solid oxide fuel cells

    Science.gov (United States)

    Wu, XiaoJuan; Ye, Qianwen

    2016-07-01

    One of the major hurdles for solid oxide fuel cell (SOFC) commercialization is poor long-term performance and durability. Accurate fault diagnostic and prognostic technologies are two important tools to improve SOFC durability. In literature, plenty of diagnosis techniques for SOFC systems have been successfully designed. However, no literature studies SOFC fault prognosis approaches. In this paper a unified fault diagnosis and prognosis strategy is presented to identify faults (anode poisoning, cathode humidification or normal) and predict the remaining useful life for SOFC systems. Using a squares support vector machine (LS-SVM) classifier, a diagnosis model is built to identify SOFC different types of faults. After fault detection, two hidden semi-Mark models (HSMMs) are respectively employed to estimate SOFC remaining useful life in the case of anode poisoning and cathode humidification. The simulation results show that the fault recognition rates with the LS-SVM model are at best 97%, and the predicted error of the remaining useful life is within ±20%.

  1. MODIFIED LAPLACIAN EIGENMAP METHOD FOR FAULT DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    JIANG Quansheng; JIA Minping; HU Jianzhong; XU Feiyun

    2008-01-01

    A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.

  2. Distributed adaptive diagnosis of sensor faults using structural response data

    Science.gov (United States)

    Dragos, Kosmas; Smarsly, Kay

    2016-10-01

    The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

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

    Science.gov (United States)

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-18

    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.

  4. Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

    Science.gov (United States)

    Sharifi, Reza; Langari, Reza

    2017-02-01

    This paper presents a methodology for sensor fault diagnosis in nonlinear systems using a Mixture of Probabilistic Principal Component Analysis (MPPCA) models. This methodology separates the measurement space into several locally linear regions, each of which is associated with a Probabilistic PCA (PPCA) model. Using the transformation associated with each PPCA model, a parity relation scheme is used to construct a residual vector. Bayesian analysis of the residuals forms the basis for detection and isolation of sensor faults across the entire range of operation of the system. The resulting method is demonstrated in its application to sensor fault diagnosis of a fully instrumented HVAC system. The results show accurate detection of sensor faults under the assumption that a single sensor is faulty.

  5. A data structure and algorithm for fault diagnosis

    Science.gov (United States)

    Bosworth, Edward L., Jr.

    1987-01-01

    Results of preliminary research on the design of a knowledge based fault diagnosis system for use with on-orbit spacecraft such as the Hubble Space Telescope are presented. A candidate data structure and associated search algorithm from which the knowledge based system can evolve is discussed. This algorithmic approach will then be examined in view of its inability to diagnose certain common faults. From that critique, a design for the corresponding knowledge based system will be given.

  6. Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

    OpenAIRE

    Shoubin Wang; Xiaogang Sun; Chengwei Li

    2014-01-01

    As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance mat...

  7. Robust fault diagnosis for a class of nonlinear systems with time delay

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Robust fault diagnosis problems based on adaptive observer technique are studied for a class of time delayed nonlinear system with external disturbance. Adaptive fault updating laws were designed to estimate the fault and to guarantee the stability of the diagnosis system. The effects of adjusting parameters in adaptive fault updating laws on the fault estimation accuracy were analyzed. For a designed fault diagnosis system, the super bounds of the state estimation error and fault estimation error of the adaptive observer were discussed, which further showed how the parameters in the adaptive fault updating laws influenced the fault estimation accuracy.Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.

  8. Development of CIMS and FMS in Faults Diagnosis System

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The research and practice of CIMS and FMS has brought about a great development to advanced manufacturing systems for decades. The experience of failure and success during the process of development is a revelation and reference for the design of a fault diagnosis system. This paper focuses on its function of directing to the design of a fault diagnosis system in terms of the flexibility of the system, the human's importance in the system, and the design of a distributed system. In view of the tendency of CIMS and FMS, the article also states the principle that the new fault diagnosis system should be improved by enhancing hardware in software, remote Internet service, and sustainable development.

  9. A Fault Dictionary-Based Fault Diagnosis Approach for CMOS Analog Integrated Circuits

    Directory of Open Access Journals (Sweden)

    Mouna Karmani

    2011-09-01

    Full Text Available In this paper, we propose a simulation-before-test (SBT fault diagnosis methodology based on the use of a fault dictionary approach. This technique allows the detection and localization of the most likely defects of open-circuit type occurring in Complementary Metal–Oxide–Semiconductor (CMOS analog integrated circuits (ICs interconnects. The fault dictionary is built by simulating the most likely defects causing the faults to be detected at the layout level. Then, for each injected fault, the spectre’s frequency responses and the power consumption obtained by simulation are stored in a table which constitutes the fault dictionary.In fact, each line in the fault dictionary constitutes a fault signature used to identify and locate a considered defect. When testing, the circuit under test is excited with the same stimulus, and the responses obtained are compared to the stored ones. To prove the efficiency of the proposed technique, a full custom CMOS operational amplifier is implemented in 0.25 μm technology and the most likely faults of open circuit type are deliberately injected and simulated at the layout level.

  10. Analog fault diagnosis by inverse problem technique

    KAUST Repository

    Ahmed, Rania F.

    2011-12-01

    A novel algorithm for detecting soft faults in linear analog circuits based on the inverse problem concept is proposed. The proposed approach utilizes optimization techniques with the aid of sensitivity analysis. The main contribution of this work is to apply the inverse problem technique to estimate the actual parameter values of the tested circuit and so, to detect and diagnose single fault in analog circuits. The validation of the algorithm is illustrated through applying it to Sallen-Key second order band pass filter and the results show that the detecting percentage efficiency was 100% and also, the maximum error percentage of estimating the parameter values is 0.7%. This technique can be applied to any other linear circuit and it also can be extended to be applied to non-linear circuits. © 2011 IEEE.

  11. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements.

  12. Model-based fault detection and diagnosis in ALMA subsystems

    Science.gov (United States)

    Ortiz, José; Carrasco, Rodrigo A.

    2016-07-01

    The Atacama Large Millimeter/submillimeter Array (ALMA) observatory, with its 66 individual telescopes and other central equipment, generates a massive set of monitoring data every day, collecting information on the performance of a variety of critical and complex electrical, electronic and mechanical components. This data is crucial for most troubleshooting efforts performed by engineering teams. More than 5 years of accumulated data and expertise allow for a more systematic approach to fault detection and diagnosis. This paper presents model-based fault detection and diagnosis techniques to support corrective and predictive maintenance in a 24/7 minimum-downtime observatory.

  13. Inter Processor Communication for Fault Diagnosis in Multiprocessor Systems

    Directory of Open Access Journals (Sweden)

    C. D. Malleswar

    1994-04-01

    Full Text Available In the preseJlt paper a simple technique is proposed for fault diagnosis for multiprocessor and multiple system environments, wherein all microprocessors in the system are used in part to check the health of their neighbouring processors. It involves building simple fail-safe serial communication links between processors. Processors communicate with each other over these links and each processor is made to go through certain sequences of actions intended for diagnosis, under the observation of another processor .With limited overheads, fault detection can be done by this method. Also outlined are some of the popular techniques used for health check of processor-based systems.

  14. Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers

    Directory of Open Access Journals (Sweden)

    N. V. Kolesov

    2013-01-01

    Full Text Available A method of fault diagnosis in dynamic systems based on a fuzzy approach is proposed. The new method possesses two basic specific features which distinguish it from the other known fuzzy methods based on the application of fuzzy logic and a bank of state observers. First, this method uses a bank of interacting observers instead of traditional independent observers. The second specific feature of the proposed method is the assumption that there is no strict boundary between the serviceable and disabled technical states of the system, which makes it possible to specify a decision making rule for fault diagnosis.

  15. Fault Diagnosis of Nonlinear Analog Circuits. Volume III. Fault Diagnosis in the Tableau Context.

    Science.gov (United States)

    1983-04-01

    of the limited fault assumption is that of Biernacki and Bandler who developed an approach to multiple fault location for linear networks. Here the...and J. W. Bandler , "Multiple-Fault Location of Analog Circuits," IEEE Trans. on Circuits and Systems, Vol. CAS-28, 361-367, May 1981. [5] R. A. DeCarlo

  16. Research into a distributed fault diagnosis system and its application

    Science.gov (United States)

    Qian, Suxiang; Jiao, Weidong; Lou, Yongjian; Shen, Xiaomei

    2005-12-01

    CORBA (Common Object Request Broker Architecture) is a solution to distributed computing methods over heterogeneity systems, which establishes a communication protocol between distributed objects. It takes great emphasis on realizing the interoperation between distributed objects. However, only after developing some application approaches and some practical technology in monitoring and diagnosis, can the customers share the monitoring and diagnosis information, so that the purpose of realizing remote multi-expert cooperation diagnosis online can be achieved. This paper aims at building an open fault monitoring and diagnosis platform combining CORBA, Web and agent. Heterogeneity diagnosis object interoperate in independent thread through the CORBA (soft-bus), realizing sharing resource and multi-expert cooperation diagnosis online, solving the disadvantage such as lack of diagnosis knowledge, oneness of diagnosis technique and imperfectness of analysis function, so that more complicated and further diagnosis can be carried on. Take high-speed centrifugal air compressor set for example, we demonstrate a distributed diagnosis based on CORBA. It proves that we can find out more efficient approaches to settle the problems such as real-time monitoring and diagnosis on the net and the break-up of complicated tasks, inosculating CORBA, Web technique and agent frame model to carry on complemental research. In this system, Multi-diagnosis Intelligent Agent helps improve diagnosis efficiency. Besides, this system offers an open circumstances, which is easy for the diagnosis objects to upgrade and for new diagnosis server objects to join in.

  17. Review of fault diagnosis and fault-tolerant control for modular multilevel converter of HVDC

    DEFF Research Database (Denmark)

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

    2013-01-01

    This review focuses on faults in Modular Multilevel Converter (MMC) for use in high voltage direct current (HVDC) systems by analyzing the vulnerable spots and failure mechanism from device to system and illustrating the control & protection methods under failure condition. At the beginning......, several typical topologies of MMC-HVDC systems are presented. Then fault types such as capacitor voltage unbalance, unbalance between upper and lower arm voltage are analyzed and the corresponding fault detection and diagnosis approaches are explained. In addition, more attention is dedicated to control...

  18. Fault Diagnosis of Batch Reactor Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Sujatha Subramanian

    2014-01-01

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

  19. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

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

  20. Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

    Science.gov (United States)

    Lei, Yaguo; Han, Dong; Lin, Jing; He, Zhengjia

    2013-07-01

    Planetary gearboxes are widely used in aerospace, automotive and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause gear tooth damage such as fatigue crack and teeth missed etc. The challenging issues in fault diagnosis of planetary gearboxes include selection of sensitive measurement locations, investigation of vibration transmission paths and weak feature extraction. One of them is how to effectively discover the weak characteristics from noisy signals of faulty components in planetary gearboxes. To address the issue in fault diagnosis of planetary gearboxes, an adaptive stochastic resonance (ASR) method is proposed in this paper. The ASR method utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastic resonance system matching input signals. Using the ASR method, the noise may be weakened and weak characteristics highlighted, and therefore the faults can be diagnosed accurately. A planetary gearbox test rig is established and experiments with sun gear faults including a chipped tooth and a missing tooth are conducted. And the vibration signals are collected under the loaded condition and various motor speeds. The proposed method is used to process the collected signals and the results of feature extraction and fault diagnosis demonstrate its effectiveness.

  1. Controller modification applied for active fault detection

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob; Poulsen, Niels Kjølstad

    2014-01-01

    This paper is focusing on active fault detection (AFD) for parametric faults in closed-loop systems. This auxiliary input applied for the fault detection will also disturb the external output and consequently reduce the performance of the controller. Therefore, only small auxiliary inputs are used...

  2. Active fault detection in MIMO systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    The focus in this paper is on active fault detection (AFD) for MIMO systems with parametric faults. The problem of design of auxiliary inputs with respect to detection of parametric faults is investigated. An analysis of the design of auxiliary inputs is given based on analytic transfer functions...

  3. Autoregressive modelling for rolling element bearing fault diagnosis

    Science.gov (United States)

    Al-Bugharbee, H.; Trendafilova, I.

    2015-07-01

    In this study, time series analysis and pattern recognition analysis are used effectively for the purposes of rolling bearing fault diagnosis. The main part of the suggested methodology is the autoregressive (AR) modelling of the measured vibration signals. This study suggests the use of a linear AR model applied to the signals after they are stationarized. The obtained coefficients of the AR model are further used to form pattern vectors which are in turn subjected to pattern recognition for differentiating among different faults and different fault sizes. This study explores the behavior of the AR coefficients and their changes with the introduction and the growth of different faults. The idea is to gain more understanding about the process of AR modelling for roller element bearing signatures and the relation of the coefficients to the vibratory behavior of the bearings and their condition.

  4. A New Fault Diagnosis Method of Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Chih-Hao Chen

    2008-01-01

    Full Text Available This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The faults of rotating machinery considered in this study include imbalance, misalignment, looseness and imbalance combined with misalignment conditions. When such faults occur, they usually induce non-stationary vibrations to the machine. After measuring the vibration signals, the wavelet packets transform is applied to these signals. The fractal dimension of each frequency bands is extracted and the box counting dimension is used to depict the failure characteristics of the vibration signals. The failure modes are then classified by a radial basis function neural network. An experimental study was performed to evaluate the proposed method and the results show that the method can effectively detect and recognize different kinds of faults of rotating machinery.

  5. Fault Diagnosis Of A Water For Injection System Using Enhanced Structural Isolation

    DEFF Research Database (Denmark)

    Laursen, Morten; Blanke, Mogens; Düstegör, Dilek

    2008-01-01

    A water for injection system supplies chilled sterile water as solvent to pharmaceutical products. There are ultimate requirements to the quality of the sterile water, and the consequence of a fault in temperature or in flow control within the process may cause loss of one or more batches...... of the production. Early diagnosis of faults is hence of considerable interest for this process. This study investigates the properties of multiple matchings with respect to isolability and it suggests to explore the topologies of multiple use-modes for the process and to employ active techniques for fault...

  6. Rotor blade online monitoring and fault diagnosis technology research

    DEFF Research Database (Denmark)

    Tesauro, Angelo; Pavese, Christian; Branner, Kim

    Rotor blade online monitoring and fault diagnosis technology is an important way to find blade failure mechanisms and thereby improve the blade design. Condition monitoring of rotor blades is necessary in order to ensure the safe operation of the wind turbine, make the maintenance more economical...

  7. Optimal input design for fault detection and diagnosis

    DEFF Research Database (Denmark)

    Sadegh, Payman; Madsen, Henrik; Holst, J.

    1995-01-01

    In the paper, the design of optimal input signals for detection and diagnosis in a stochastic dynamical system is investigated. The design is based on maximization of Kullback measure between the model under fault and the model under normal operation conditions. It is established that the optimal...

  8. Fault diagnosis and isolation of the componentand sensor for aircraft engine

    Institute of Scientific and Technical Information of China (English)

    QIU Xiao-jie; HUANG Jin-quan; LU Feng; LIU Nan

    2012-01-01

    Aircraft engine component and sensor fault detection and isolation approach was proposed,which included fault type detection module and component-sensor simultaneous fault isolation module.The approach can not only distinguish among sensor fault,component fault and component-sensor simultaneous fault,but also isolate and locate sensor fault and the type of engine component fault when the engine component fault and the sensor faults occur simultaneously.The double-threshold mechanism has been proposed,in which the fault diagnostic threshold changed with the sensor type and the engine condition,and it greatly improved the accuracy and robustness of sensor fault diagnosis system.Simulation results show that the approach proposed can diagnose and isolate the sensor and engine component fault with improved accuracy.It effectively improves the fault diagnosis ability of aircraft engine.

  9. Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert systemand build up intelligent fault diagnosis for a type of mis-sile weapon system, the concrete implementation of a fuzzyNN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, theintelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment.The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosisfor large-scale missile weapon equipment.

  10. A Novel Framework for Real-Time Fault Diagnosis Based on Dynamic Fault Tree Analysis

    Directory of Open Access Journals (Sweden)

    Rongxing Duan

    2013-02-01

    Full Text Available To meet the real-time diagnosis requirements of the complex system, this study proposes a novel framework for real-time fault diagnosis using dynamic fault tree analysis. It pays special attention to meeting two challenges: model development and real-time reasoning. In terms of the challenge of model development, we use a dynamic fault tree model to capture the dynamic behavior of system failure mechanisms and calculate some reliability results by mapping a dynamic fault tree into an equivalent Bayesian Network (BN in order to avoid the infamous state space explosion problem. In terms of the real-time reasoning challenge, we adopt a logic compilation based inference algorithm, which compiles the BN into an arithmetic circuit and retrieves answers to probabilistic queries by evaluating and differentiating the arithmetic circuit. Furthermore, we incorporate sensors data into fault diagnosis, cope with the sensors reliability and propose the schemes on how to update the Diagnostic Importance Factor (DIF and the minimal cut sets. Finally, a case study is given to validate the efficiency of this method.

  11. Application of fault factor method to fault detection and diagnosis for space shuttle main engine

    Science.gov (United States)

    Cha, Jihyoung; Ha, Chulsu; Ko, Sangho; Koo, Jaye

    2016-09-01

    This paper deals with an application of the multiple linear regression algorithm to fault detection and diagnosis for the space shuttle main engine (SSME) during a steady state. In order to develop the algorithm, the energy balance equations, which balances the relation among pressure, mass flow rate and power at various locations within the SSME, are obtained. Then using the measurement data of some important parameters of the engine, fault factors which reflects the deviation of each equation from the normal state are estimated. The probable location of each fault and the levels of severity can be obtained from the estimated fault factors. This process is numerically demonstrated for the SSME at 104% Rated Propulsion Level (RPL) by using the simulated measurement data from the mathematical models of the engine. The result of the current study is particularly important considering that the recently developed reusable Liquid Rocket Engines (LREs) have staged-combustion cycles similarly to the SSME.

  12. A Fault Dictionary-Based Fault Diagnosis Approach for CMOS Analog Integrated Circuits

    Directory of Open Access Journals (Sweden)

    Mouna Karmani

    2011-10-01

    Full Text Available In this paper, we propose a simulation-before-test (SBT fault diagnosis methodology based on the use of afault dictionary approach. This technique allows the detection and localization of the most likely defects ofopen-circuit type occurring in Complementary Metal–Oxide–Semiconductor (CMOS analog integratedcircuits (ICs interconnects. The fault dictionary is built by simulating the most likely defects causing thefaults to be detected at the layout level. Then, for each injected fault, the spectre’s frequency responses andthe power consumption obtained by simulation are stored in a table which constitutes the fault dictionary.In fact, each line in the fault dictionary constitutes a fault signature used to identify and locate aconsidered defect. When testing, the circuit under test is excited with the same stimulus, and the responsesobtained are compared to the stored ones. To prove the efficiency of the proposed technique, a full customCMOS operational amplifier is implemented in 0.25 μm technology and the most likely faults of opencircuittype are deliberately injected and simulated at the layout level.

  13. Fault diagnosis and fault-tolerant control and guidance for aerospace vehicles from theory to application

    CERN Document Server

    Zolghadri, Ali; Cieslak, Jerome; Efimov, Denis; Goupil, Philippe

    2014-01-01

    Fault Diagnosis and Fault-Tolerant Control and Guidance for Aerospace demonstrates the attractive potential of recent developments in control for resolving such issues as improved flight performance, self-protection and extended life of structures. Importantly, the text deals with a number of practically significant considerations: tuning, complexity of design, real-time capability, evaluation of worst-case performance, robustness in harsh environments, and extensibility when development or adaptation is required. Coverage of such issues helps to draw the advanced concepts arising from academic research back towards the technological concerns of industry. Initial coverage of basic definitions and ideas and a literature review gives way to a treatment of important electrical flight control system failures: the oscillatory failure case, runaway, and jamming. Advanced fault detection and diagnosis for linear and nonlinear systems are described. Lastly recovery strategies appropriate to remaining acuator/sensor/c...

  14. A Comparative Study of Genetic Algorithm Parameters for the Inverse Problem-based Fault Diagnosis of Liquid Rocket Propulsion Systems

    Institute of Scientific and Technical Information of China (English)

    Erfu Yang; Hongjun Xiang; Dongbing Gu; Zhenpeng Zhang

    2007-01-01

    Fault diagnosis of liquid rocket propulsion systems (LRPSs) is a very important issue in space launch activities particularly when manned space missions are accompanied, since the safety and reliability can be significantly enhanced by exploiting an efficient fault diagnosis system. Currently, inverse problem-based diagnosis has attracted a great deal of research attention in fault diagnosis domain. This methodology provides a new strategy to model-based fault diagnosis for monitoring the health of propulsion systems. To solve the inverse problems arising from the fault diagnosis of LRPSs, GAs have been adopted in recent years as the first and effective choice of available numerical optimization tools. However, the GA has many control parameters to be chosen in advance and there still lack sound theoretical tools to analyze the effects of these parameters on diagnostic performance analytically. In this paper a comparative study of the influence of GA parameters on diagnostic results is conducted by performing a series of numerical experiments. The objective of this study is to investigate the contribution of individual algorithm parameter to final diagnostic result and provide reasonable estimates for choosing GA parameters in the inverse problem-based fault diagnosis of LRPSs. Some constructive remarks are made in conclusion and will be helpful for the implementation of GA to the fault diagnosis practice of LRPSs in the future.

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

    Science.gov (United States)

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

    2016-12-22

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

  16. Hydrogen release: new indicator of fault activity.

    Science.gov (United States)

    Wakita, H; Nakamura, Y; Kita, I; Fujii, N; Notsu, K

    1980-10-10

    The hydrogen concentration in soil gas has been measured in the area around the Yamasaki Fault, one of the active faults in southwestern Japan. Degassing of a significant amount of hydrogen (up to more than 3 percent by volume) has been observed for sites along the fault zone. The hydrogen concentration in soil gas at sites away from the fault zone was about 0.5 part per million, almost the same as that found in the atmosphere. The spatial distribution of sites with high hydrogen concentrations is quite systematic. A hypothesis on the production of hydrogen by fault movements is postulated.

  17. Fault Diagnosis and Fault-tolerant Control of Modular Multi-level Converter High-voltage DC System: A Review

    DEFF Research Database (Denmark)

    Liu, Hui; Ma, Ke; Wang, Chao;

    2016-01-01

    Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) configuration is a promising solution for the efficient grid integration and bulky power transmission over long distance. However, the large number of series connected identical modules in MMC may increase the probability...... strategies of MMC-HVDC systems for the most common faults happened in MMC-HVDC systems covering MMC faults, DC side faults as well as AC side faults. An important part of this paper is devoted to a discussion of the vulnerable spots as well as failure mechanism of the MMC-HVDC system covering switching...... device fault, DC line faults as well as AC grid faults. Special attention is given to the comparison of the corresponding fault diagnosis and fault-tolerant control approaches. Further, focus is dedicated to control/protection strategies and topologies with fault ride-though capability for MMC...

  18. Design and implementation of an expert system for remote fault diagnosis in ship lift

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship...

  19. Design and implementation of an expert system for remote fault diagnosis in ship lift

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

  20. Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic element is the test-diagnosis fault unit. Every underlying fault cause's membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples,the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.

  1. An adaptive particle filter for mobile robot fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    DUAN Zhuo-hua; FU Ming; CAI Zi-xing; YU Jin-xia

    2006-01-01

    An adaptive particle filter for fault diagnosis of dead-reckoning system was presented, which applied a general framework to integrate rule-based domain knowledge into particle filter. Domain knowledge was exploited to constrain the state space to certain subset. The state space was adjusted by setting the transition matrix. Firstly, the monitored mobile robot and its kinematics models,measurement models and fault models were given. Then, 5 kinds of planar movement states of the robot were estimated with driving speeds of left and right side. After that, the possible (or detectable) fault modes were obtained to modify the transitional probability.There are two typical advantages of this method, i.e. particles will never be drawn from hopeless area of the state space, and the particle number is reduced.

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

    Directory of Open Access Journals (Sweden)

    Xiaojie Guo

    2016-12-01

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

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

    DEFF Research Database (Denmark)

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks....... 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...

  4. Open-Switch Fault Diagnosis and Fault Tolerant for Matrix Converter with Finite Control Set-Model Predictive Control

    DEFF Research Database (Denmark)

    Peng, Tao; Dan, Hanbing; Yang, Jian

    2016-01-01

    To improve the reliability of the matrix converter (MC), a fault diagnosis method to identify single open-switch fault is proposed in this paper. The introduced fault diagnosis method is based on finite control set-model predictive control (FCS-MPC), which employs a time-discrete model of the MC...... topology and a cost function to select the best switching state for the next sampling period. The proposed fault diagnosis method is realized by monitoring the load currents and judging the switching state to locate the faulty switch. Compared to the conventional modulation strategies such as carrier...

  5. Vehicle gearbox fault diagnosis using noise measurements

    Directory of Open Access Journals (Sweden)

    Sameh M. Metwalley, Nabil Hammad, Shawki A. Abouel-Seoud

    2011-03-01

    Full Text Available Noise measurement is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of noise measurement in monitoring gearboxes this has been solely applied on any types of gears (spur, helical, ..etc.. The condition monitoring of a lab-scale, single stage, gearbox, represents the vehicle real gearbox, using non-destructive inspection methodology and the processing of the acquired waveform with advanced signal processing techniques is the aim of the present work. Acoustic emission was utilized for this purpose. The experimental setup and the instrumentation are present in detail. Emphasis is given on the signal processing of the acquired noise measurement signal in order to extract conventional as well as novel parameters potential diagnostic value from the monitoring waveform. The evolution of selected parameters/features versus test time is provided, evaluated and the parameters with most interesting diagnostic behavior are highlighted. The present work also reports the results concluded by long term (~ 6.0 h experiments to a defected gear system, with a transverse cuts ranged from 0.75 mm to 3.0 mm to simulate the tooth crack. Different parameters, related by the analysis of the recording signals coming from acoustic emission are presented and their diagnostic value is discussed for the development of a condition monitoring system.

  6. A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    On the basis of the analysis of faults and their causes of vacuum resin shot dosing equipment, the fuzzy model of fault diagnosis for the equipment is constructed, and the fuzzy relationship matrix, the symptom fuzzy vector, the fuzzy compound arithmetic operator, and the diagnosis principle of the model are determined. Then the fault auto-diagnosis system for the equipment is designed, and the functions for real-time monitoring its operation condition and for fault auto-diagnosis are realized. Finally, the experiments of fault auto-diagnosis are conducted in practical production and the veracity of the system is verified.

  7. Multisensor Data Fusion for Automotive Engine Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    王赟松; 褚福磊; 何永勇; 郭丹

    2004-01-01

    This paper describes mainly a decision-level data fusion technique for fault diagnosis for electronically controlled engines.Experiments on a SANTANA AJR engine show that the data fusion method provides good engine fault diagnosis.In data fusion methods, the data level fusion has small data preprocessing loads and high accuracy, but requires commensurate sensor data and has poor operational performance.The decision-level fusion based on Dempster-Shafer evidence theory can process noncommensurate data and has robust operational performance, reduces ambiguity, increases confidence, and improves system reliability, but has low fusion accuracy and high data preprocessing cost.The feature-level fusion provides good compromise between the above two methods, which becomes gradually mature.In addition, acquiring raw data is a precondition to perform data fusion, so the system for signal acquisition and processing for an automotive engine test is also designed by the virtual instrument technology.

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

    Directory of Open Access Journals (Sweden)

    Chin-Tsung Hsieh

    2014-01-01

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

  9. Study on the Consultation Mechanism of an Internet-Based Remote Fault Diagnosis System

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Aimed at the deficiency of the mechanism of management and consultation, an idea of an internet-based Virtual Diagnosis Center (VDC) for machine fault is proposed, and the key elements of remote consultation are abstracted. Around the key elements, the construct scheme and cooperative mechanism among experts of VDC are designed. According to the diagnosed object, the context knowledge of a fault machine, fault cases and ActiveX-based analysis tools are integrated into a multimedia consultation environment in VDC to enhance the efficiency of expert consultation. Simultaneously, the technique of push subscription in a SQL Server is utilized to collect machine condition data in an enterprise machine condition database, which ensures the security of the database. The VDC system in Xi'an Jiaotong University has been applied to remote diagnosis of a blower in Wuhan Iron and Steel Corporation and the system construction reasonableness and the running stability are verified.

  10. Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

    Institute of Scientific and Technical Information of China (English)

    Wang Hongjun; Xu Xiaoli; Rosen B G

    2014-01-01

    Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine (PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

  11. Design of Fault Diagnosis Observer for HAGC System on Strip Rolling Mill

    Institute of Scientific and Technical Information of China (English)

    DONG Min; LIU Cai

    2006-01-01

    By building mathematical model for HAGC (hydraulic automation gauge control) system of strip rolling mill, treating faults as unknown inputs induced by model uncertainty, and analyzing fault direction, an unknown input fault diagnosis observer group was designed. Fault detection and isolation were realized through making observer residuals robust to specific faults but sensitive to other faults. Sufficient existence conditions and design of the observers were given in detail. Diagnosis observer parameters for servo valve, cylinder, roller and body rolling mill were obtained respectively. The effectiveness of this diagnosis method was proved by actual data simulations.

  12. Application of particle swarm optimization blind source separation technology in fault diagnosis of gearbox

    Institute of Scientific and Technical Information of China (English)

    黄晋英; 潘宏侠; 毕世华; 杨喜旺

    2008-01-01

    Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.

  13. Research of Multi-Agent System based satellite fault diagnosis technology

    Institute of Scientific and Technical Information of China (English)

    范显峰; 姜兴渭; 黄文虎; 谷吉海

    2002-01-01

    Following the theory of Multi-Agent System (MAS) and using series-wound structure and shunt-wound structure of Agents, the performance of Agent was improved to satisfy the need of satellite fault diagno-sis, and a tridimensional MAS model of satellite fault diagnosis was thus established for the MAS based planardiagnosis system, which decentralizes the whole diagnosing task into subtasks to be performed by different func-tional Agents to make the complicated fault diagnosis very simple and the diagnosis system more intelligent.This method improved the reliability and accuracy of diagnosis and made the maintenance and upgrading of thesatellite fault diagnosis system very easy as well.

  14. Fault diagnosis and fault-tolerant control strategies for non-linear systems analytical and soft computing approaches

    CERN Document Server

    Witczak, Marcin

    2014-01-01

      This book presents selected fault diagnosis and fault-tolerant control strategies for non-linear systems in a unified framework. In particular, starting from advanced state estimation strategies up to modern soft computing, the discrete-time description of the system is employed Part I of the book presents original research results regarding state estimation and neural networks for robust fault diagnosis. Part II is devoted to the presentation of integrated fault diagnosis and fault-tolerant systems. It starts with a general fault-tolerant control framework, which is then extended by introducing robustness with respect to various uncertainties. Finally, it is shown how to implement the proposed framework for fuzzy systems described by the well-known Takagi–Sugeno models. This research monograph is intended for researchers, engineers, and advanced postgraduate students in control and electrical engineering, computer science,as well as mechanical and chemical engineering.

  15. Knowledge Processing Method of Fault Diagnosis Expert Systems for Letter Sorting Equipment

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Based on the analysis of fault diagnosis knowledge of lettersorting machine, this paper proposes a processing method by which the fault diagnosis knowledge is divided into exact knowledge, inadequate knowledge and fuzzy knowledge. Then their presenting and implementing form in fault diagnosis expert system is discussed and studied. It is proved that the expert system has good feasibility in the field of the diagnosis of letter sorting machine.

  16. Sequential fault diagnosis for mechatronics system using diagnostic hybrid bond graph and composite harmony search

    Directory of Open Access Journals (Sweden)

    Ming Yu

    2015-12-01

    Full Text Available This article proposes a sequential fault diagnosis method to handle asynchronous distinct faults using diagnostic hybrid bond graph and composite harmony search. The faults under consideration include fault mode, abrupt fault, and intermittent fault. The faults can occur in different time instances, which add to the difficulty of decision making for fault diagnosis. This is because the earlier occurred fault can exhibit fault symptom which masks the fault symptom of latter occurred fault. In order to solve this problem, a sequential identification algorithm is developed in which the identification task is reactivated based on two conditions. The first condition is that the latter occurred fault has at least one inconsistent coherence vector element which is consistent in coherence vector of the earlier occurred fault, and the second condition is that the existing fault coherence vector has the ability to hide other faults and the second-level residual exceeds the threshold. A new composite harmony search which is capable of handling continuous variables and binary variables simultaneously is proposed for identification purpose. Experiments on a mobile robot system are conducted to assess the proposed sequential fault diagnosis algorithm.

  17. Fuzzy Timing Petri Net for Fault Diagnosis in Power System

    Directory of Open Access Journals (Sweden)

    Alireza Tavakholi Ghainani

    2012-01-01

    Full Text Available A model-based system for fault diagnosis in power system is presented in this paper. It is based on fuzzy timing Petri net (FTPN. The ordinary Petri net (PN tool is used to model the protective components, relays, and circuit breakers. In addition, fuzzy timing is associated with places (token/transition to handle the uncertain information of relays and circuits breakers. The received delay time information of relays and breakers is mapped to fuzzy timestamps, π(τ, as initial marking of the backward FTPN. The diagnosis process starts by marking the backward sub-FTPNs. The final marking is found by going through the firing sequence, σ, of each sub-FTPN and updating fuzzy timestamp in each state of σ. The final marking indicates the estimated fault section. This information is then in turn used in forward FTPN to evaluate the fault hypothesis. The FTPN will increase the speed of the inference engine because of the ability of Petri net to describe parallel processing, and the use of time-tag data will cause the inference procedure to be more accurate.

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

    KAUST Repository

    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. Application of Petri Net to Fault Diagnosis in Satellite

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    A prototype of fault diagnosis based on Petri net, which is developed for a satellite tele-control subsystem, is introduced in this paper. Its structure is first given with the emphasis on a Petri net modeling tool which is designed using the object oriented method. The prototype is connected to the database with DAO (Date Access Object) technique, and makes the Petri net's firing mechanism and its analyzing methods to be packed up as DLL (Dynamic Link Library) documents. Compared with the rule-based expert system method, the Petri net-based one can store the knowledge in mathematical matrix and make inference more quickly and effectively.

  20. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  1. Fault Diagnosis of a Rotary Machine Based on Information Entropy and Rough Set

    Institute of Scientific and Technical Information of China (English)

    LI Jian-lan; HUANG Shu-hong

    2007-01-01

    There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.

  2. Induction motor rotor fault diagnosis method based on double PQ transformation

    Institute of Scientific and Technical Information of China (English)

    HUANG Jin; NIU Faliang; YANG Jiaqiang

    2007-01-01

    This Paper presents a new rotor fault diagnosis method for induction motors which is based on the double PQ transformation.We construct the PQ transformation matrix with the positive sequence fundamental voltage components and their Hilbert transformation as elements.The active power P and the reactive power Q are obtained through the PO transformation of the stator currents.As both P and Q are constant for a healthy motor,they are represented by a dot on the PQ plane.Whereas the P and Q for a rotor broken bar motor are represented by an ellipse because they comprise an additional frequency component 2sfs (s is the slip and js is the supply frequency).Thus,by distinguishing these two different patterns.the rotor broken bar fault is detected.We use the major radius of the ellipse as the fault indicator and the distance between the point of no-load condition and the center of the ellipse on the PQ plane as its normalization value.We thus arrive at the fault severity factor which is fairly independent of the load level and the inertia value of the induction motors.Experimental results have demonstrated that the proposed method is effective in identifying the rotor-broken-bars fault and at determining the severity of the fault.

  3. Multiple Local Reconstruction Model-based Fault Diagnosis for Continuous Processes

    Institute of Scientific and Technical Information of China (English)

    ZHAO Chun-Hui; LI Wen-Qing; SUN You-Xian; GAO Fu-Rong

    2013-01-01

    In the present work,the multiplicity of fault characteristics is proposed and analyzed to improve the fault diagnosis performance.It is based on the following recognition that the underlying fault characteristics in general do not stay constant but will present changes along the time direction.That is,the fault process reveals different variable correlations across different time periods.To analyze the multiplicity of fault characteristics,a fault division algorithm is developed to divide the fault process into multiple local time periods where the fault characteristics are deemed similar within the same local time period.Then a representative fault decomposition model is built in each local time period to reveal the relationships between the fault and normal operation status.In this way,these different fault characteristics can be modeled respectively.The proposed method gives an interesting insight into the fault evolvement behaviors and a more accurate from-fault-to-normal reconstruction result can be expected for fault diagnosis.The feasibility and performance of the proposed fault diagnosis method are illustrated with the Tennessee Eastman process.

  4. GEARBOX FAULT DIAGNOSIS BASED ON EMPIRICAL MODE DECOMPOSITION

    Institute of Scientific and Technical Information of China (English)

    Shen Guoji; Tao Limin; Chen Zhongsheng

    2004-01-01

    Time synchronous averaging of vibration data is a fundament technique for gearbox diagnosis. Currently, this technique relies on hardware tachometer to give phase synchronous information. Empirical mode decomposition (EMD) is introduced to replace time synchronous averaging of gearbox vibration signal. With it, any complicated dataset can be decomposed into a finite and often small number of intrinsic mode functions (IMF). The key problem is how to assure that vibration signals deduced by gear defects could be sifted out by EMD. The characteristic vibration signals of gear defects are proved IMFs, which makes it possible to utilize EMD for the diagnosis of gearbox faults. The method is validated by data from recordings of the vibration of a single-stage spiral bevel gearbox with fatigue pitting. The results show EMD is powerful to extract characteristic information from noisy vibration signals.

  5. STUDY OF A FAULT DIAGNOSIS EXPERT SYSTEM FOR SYNTHETIC MINING SYSTEM HYDRAULIC SUPPORT

    Institute of Scientific and Technical Information of China (English)

    Han Yilun

    2000-01-01

    Fault diagnosis expert system for hydraulic support is studied.The system is achieved by Turbo-prolong Language, it summaries the experience of the domain expert and sets up a fault tree, knowledge base is developed by a productive rule.According to the feature of diagnosis, the system selects forward non-determination inferring and limited depth-first search strategy.It can accomplish expert diagnosis of more than 50 kinds faults in hydraulic support.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  8. On-Line Broken-Bar Fault Diagnosis System of Induction Motor

    Institute of Scientific and Technical Information of China (English)

    ZHANG Rong; WANG Xiuhe

    2008-01-01

    Induction motor faults including mechanical and electrical faults are reviewed. The fault diagnosis methods are summarized. To analyze the influence of stator current, torque, speed and rotor current on faulted bars, a time-stepping transient finite element (FE) model of induction motor with bars faulted is created in this paper. With wavelet package analysis method and FFT method, the simulation result of finite element is analyzed. Based on the simulation analysis, the on-line fault diagnosis system of induction motor with bars faulted is developed. With the speed of broken bars motor changed from 1 478 r/min to 1 445 r/min, the FFT power spectra and the wavelet package decoupling factors are given. The comparison result shows that the on-line diagnosis system can detect broken-bar fault efficiently.

  9. Active probing based Internet service fault management in uncertain and noisy environment

    Institute of Scientific and Technical Information of China (English)

    CHU LingWei; ZOU ShiHong; CHENG ShiDuan; WANG WenDong

    2008-01-01

    In Internet service fault management based on active probing, uncertainty and noises will affect service fault management. In order to reduce the impact, chal lenges of Internet service fault management are analyzed in this paper. Bipartite Bayesian network is chosen to model the dependency relationship between faults and probes, binary symmetric channel is chosen to model noises, and a service fault management approach using active probing is proposed for such an environment. This approach is composed of two phases: fault detection and fault diagnosis. In first phase, we propose a greedy approximation probe selection algorithm (GAPSA), which selects a minimal set of probes while remaining a high probability of fault detection. In second phase, we propose a fault diagnosis probe selection algorithm (FDPSA), which selects probes to obtain more system information based on the symptoms observed in previous phase. To deal with dynamic fault set caused by fault recovery mechanism, we propose a hypothesis inference algorithm based on fault persistent time statistic (FPTS). Simulation results prove the validity and efficiency of our approach.

  10. Health and Maintenance Status Determination and Predictive Fault Diagnosis System Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The objective of this project is to demonstrate intelligent health and maintenance status determination and predictive fault diagnosis techniques for NASA rocket...

  11. Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

    Science.gov (United States)

    Wang, Xianfang; Du, Haoze; Tan, Jinglu

    2016-12-01

    Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

  12. A Study on Turbo-rotor Multi-fault Diagnosis Based on a Neural Network

    Institute of Scientific and Technical Information of China (English)

    SUN Shou-qun; ZHAO San-xing; ZHANG Wei; CHANG Xin-long

    2003-01-01

    The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine. As it has many excellent qualities, the neural network might be used to solve the problems of multi-fault diagnosis of a turbo-rotor system. First, the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing. Then, the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propagation) neural network. According to the BP neural network problems, the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated. The results indicated that the parallel BP neural network method could solve the turbo-rotor multi-fault diagnosis problems.

  13. Data-driven design of fault diagnosis systems nonlinear multimode processes

    CERN Document Server

    Haghani Abandan Sari, Adel

    2014-01-01

    In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target...

  14. Bond graphs for modelling, control and fault diagnosis of engineering systems

    CERN Document Server

    2017-01-01

    This book presents theory and latest application work in Bond Graph methodology with a focus on: • Hybrid dynamical system models, • Model-based fault diagnosis, model-based fault tolerant control, fault prognosis • and also addresses • Open thermodynamic systems with compressible fluid flow, • Distributed parameter models of mechanical subsystems. In addition, the book covers various applications of current interest ranging from motorised wheelchairs, in-vivo surgery robots, walking machines to wind-turbines.The up-to-date presentation has been made possible by experts who are active members of the worldwide bond graph modelling community. This book is the completely revised 2nd edition of the 2011 Springer compilation text titled Bond Graph Modelling of Engineering Systems – Theory, Applications and Software Support. It extends the presentation of theory and applications of graph methodology by new developments and latest research results. Like the first edition, this book addresses readers in a...

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

    Science.gov (United States)

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

    2015-08-01

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

  16. Combinatorial Optimization Algorithms for Dynamic Multiple Fault Diagnosis in Automotive and Aerospace Applications

    Science.gov (United States)

    Kodali, Anuradha

    In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a

  17. Fault-diagnosis applications. Model-based condition monitoring. Acutators, drives, machinery, plants, sensors, and fault-tolerant systems

    Energy Technology Data Exchange (ETDEWEB)

    Isermann, Rolf [Technische Univ. Darmstadt (DE). Inst. fuer Automatisierungstechnik (IAT)

    2011-07-01

    Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book ''Fault-Diagnosis Systems'' published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers. (orig.)

  18. Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot

    DEFF Research Database (Denmark)

    Zhao, Bo; Skjetne, Roger; Blanke, Mogens

    2014-01-01

    filter on the model, the fault diagnosis and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods...

  19. Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

    Science.gov (United States)

    Jeon, Namju; Lee, Hyeongcheol

    2016-12-12

    An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

  20. Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles

    Directory of Open Access Journals (Sweden)

    Namju Jeon

    2016-12-01

    Full Text Available An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

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

    Directory of Open Access Journals (Sweden)

    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.

  2. Research on Satellite Fault Diagnosis and Prediction Using Multi-modal Reasoning

    Institute of Scientific and Technical Information of China (English)

    YangTianshe; SunYanhong; CaoYuping

    2004-01-01

    Diagnosis and prediction of satellite fault are more difficult than that of other equipment due to the complex structure of satellites and the presence of muhi-excite sources of satellite faults. Generally, one kind of reasoning model can only diagnose and predict one kind of satellite faults. In this paper the author introduces an application of a new method using multi-modal reasoning to diagnose and predict satellite faults. The method has been used in the development of knowledge-based satellite fault diagnosis and recovery system (KSFDRS) successfully. It is shown that the method is effective.

  3. InSAR measurements around active faults: creeping Philippine Fault and un-creeping Alpine Fault

    Science.gov (United States)

    Fukushima, Y.

    2013-12-01

    Recently, interferometric synthetic aperture radar (InSAR) time-series analyses have been frequently applied to measure the time-series of small and quasi-steady displacements in wide areas. Large efforts in the methodological developments have been made to pursue higher temporal and spatial resolutions by using frequently acquired SAR images and detecting more pixels that exhibit phase stability. While such a high resolution is indispensable for tracking displacements of man-made and other small-scale structures, it is not necessarily needed and can be unnecessarily computer-intensive for measuring the crustal deformation associated with active faults and volcanic activities. I apply a simple and efficient method to measure the deformation around the Alpine Fault in the South Island of New Zealand, and the Philippine Fault in the Leyte Island. I use a small-baseline subset (SBAS) analysis approach (Berardino, et al., 2002). Generally, the more we average the pixel values, the more coherent the signals are. Considering that, for the deformation around active faults, the spatial resolution can be as coarse as a few hundred meters, we can severely 'multi-look' the interferograms. The two applied cases in this study benefited from this approach; I could obtain the mean velocity maps on practically the entire area without discarding decorrelated areas. The signals could have been only partially obtained by standard persistent scatterer or single-look small-baseline approaches that are much more computer-intensive. In order to further increase the signal detection capability, it is sometimes effective to introduce a processing algorithm adapted to the signal of interest. In an InSAR time-series processing, one usually needs to set the reference point because interferograms are all relative measurements. It is difficult, however, to fix the reference point when one aims to measure long-wavelength deformation signals that span the whole analysis area. This problem can be

  4. Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

    Science.gov (United States)

    Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

    2016-12-01

    Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

  5. Fault Diagnosis Approach of Local Ventilation System in Coal Mines Based on Multidisciplinary Technology

    Institute of Scientific and Technical Information of China (English)

    GONG Xiao-yan; XUE He; TAO Xin-li; HU Ning

    2006-01-01

    In order to reduce the probability of fault occurrence of local ventilation system in coal mine and prevent gas from exceeding the standard limit, an approach incorporating the reliability analysis, rough set theory, genetic algorithm (GA), and intelligent decision support system (IDSS) was used to establish and develop a fault diagnosis system of local ventilation in coal mine. Fault tree model was established and its reliability analysis was performed. The algorithms and software of key fault symptom and fault diagnosis rule acquiring were also analyzed and developed. Finally, a prototype system was developed and demonstrated by a mine instance. The research results indicate that the proposed approach in this paper can accurately and quickly find the fault reason in a local ventilation system of coal mines and can reduce difficulty of the fault diagnosis of the local ventilation system, which is significant to decrease gas exploding accidents in coal mines.

  6. A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks

    Science.gov (United States)

    Cai, Baoping; Liu, Hanlin; Xie, Min

    2016-12-01

    Bayesian network (BN) is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes, but it requires modeling of large and complex systems, in situations such as fault diagnosis and reliability evaluation. Motivated by reduction of the overall complexities of BNs for fault diagnosis, and the reporting of faults that immediately occur, a real-time fault diagnosis methodology of complex systems with repetitive structures is proposed using object-oriented Bayesian networks (OOBNs). The modeling methodology consists of two main phases: an off-line OOBN construction phase and an on-line fault diagnosis phase. In the off-line phase, sensor historical data and expert knowledge are collected and processed to determine the faults and symptoms, and OOBN-based fault diagnosis models are developed subsequently. In the on-line phase, operator experience and sensor real-time data are placed in the OOBNs to perform the fault diagnosis. According to engineering experience, the judgment rules are defined to obtain the fault diagnosis results.

  7. Iterative learning based fault diagnosis for discrete linear uncer tain systems

    Institute of Scientific and Technical Information of China (English)

    Wei Cao; Ming Sun

    2014-01-01

    In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited tech-nology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algo-rithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for dis-crete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.

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

    Directory of Open Access Journals (Sweden)

    Mr.D. V. Kodavade

    2014-09-01

    Full Text Available 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 were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.

  9. Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    Yuan Haiying; Chen Guangju; Xie Yongle

    2007-01-01

    Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit.The feature evaluation and extraction methods based on neural network are presented.Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently.The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency.A fault diagnosis illustration validated this method.

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

    Science.gov (United States)

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

    2015-05-01

    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.

  11. Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

    Directory of Open Access Journals (Sweden)

    Guangxiang Yang

    2015-01-01

    Full Text Available There is a constant need for the safe operation and reliability of antifalling safety device (AFSD of an elevator. This paper reports an experimental study on rotation speed and catching torque monitoring and fault diagnosis of an antifalling safety device in a construction elevator. Denoising the signal using wavelet transform is presented in this paper. Based on the denoising effects for several types of wavelets, the sym8 wavelet basis, which introduces the high order approximation and an adaptive threshold, is employed for denoising the signal. The experimental result shows a maximum data error reduction of 7.5% is obtained and SNRs (signal-to-noise ratio of rotation speed and catching torque are improved for 3.9% and 6.4%, respectively.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  13. In-Flight Fault Diagnosis for Autonomous Aircraft Via Low-Rate Telemetry Channel

    DEFF Research Database (Denmark)

    Blanke, Mogens; Hansen, Søren

    2012-01-01

    An in-flight diagnosis system that is able to detect faults on an unmanned aircraft using real-time telemetry data could provide operator assistance to warn about imminent risks due to faults. However, limited bandwidth of the air-ground radio-link makes diagnosis difficult. Loss of information...

  14. Remarks on Urban Active Fault Exploration and Assessment of Fault Activity

    Institute of Scientific and Technical Information of China (English)

    Deng Qidong; Lu Zaoxun; Yang Zhu'en

    2008-01-01

    According to the practice of urban active fault exploration and associated fault activity assessment conducted in recent years, this paper summarizes the problems encountered in geological, geomorphological, geochemical and geophysical surveys, and proposes the following means and suggestions to solve these problems. To determine the most recent faults or fault zones, emphasis should be placed on identifying the youngest active faults and offset geomorphology. To understand the history of faulting and to discover the latest offset event, it is suggested that geophysical prospecting, drilling and trenching be conducted on one profile.Because of significant uncertainties in late Quaternary dating, we advise systematic sampling and the use of multiple dating methods. Shallow seismic reflection has been proven to be the most useful method in urban active fault exploration. However, there is a pressing need to increase the quality of data acquisition and processing to obtain high resolution images so as to enhance our ability to identify active faults. The combination of seismic P-wave reflection and S-wave reflection methods is proved to be a powerful means to investigate the tectonic environments of the deep crust.

  15. Fault Diagnosis of Nonlinear Systems Based on Hybrid PSOSA Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Ling-Lai Li; Dong-Hua Zhou; Ling Wang

    2007-01-01

    Fault diagnosis of nonlinear systems is of great importance in theory and practice, and the parameter estimation method is an effective strategy. Based on the framework of moving horizon estimation, fault parameters are identified by a proposed intelligent optimization algorithm called PSOSA, which could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA). Simulations on a three-tank system show the effectiveness of this optimization based fault diagnosis strategy.

  16. Towards self-tuning residual generators for UAV control surface fault diagnosis

    DEFF Research Database (Denmark)

    Blanke, Mogens; Hansen, Søren

    2013-01-01

    Control surface fault diagnosis is essential for timely detection of manoeuvring and stability risks for an unmanned aircraft. Timely detection is crucial since control surface related faults impact stability of flight and safety. Reliable diagnosis require well fitting dynamical models but with ...... flights with different members of a population of UAVs that have inherent model uncertainty from one member to another and from one flight to another. Events with actual faults on control surfaces demonstrates the efficacy of the approach....

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

    Science.gov (United States)

    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.

  18. Active fault traces along Bhuj Fault and Katrol Hill Fault, and trenching survey at Wandhay, Kachchh, Gujarat, India

    Indian Academy of Sciences (India)

    Michio Morino; Javed N Malik; Prashant Mishra; Chandrashekhar Bhuiyan; Fumio Kaneko

    2008-06-01

    Several new active fault traces were identified along Katrol Hill Fault (KHF).A new fault (named as Bhuj Fault,BF)that extends into the Bhuj Plain was also identified.These fault traces were identified based on satellite photo interpretation and field survey.Trenches were excavated to identify the paleoseismic events,pattern of faulting and the nature of deformation.New active fault traces were recognized about 1 km north of the topographic boundary between the Katrol Hill and the plain area.The fault exposure along the left bank of Khari River with 10 m wide shear zone in the Mesozoic rocks and showing displacement of the overlying Quaternary deposits is indicative of continued tectonic activity along the ancient fault.The E-W trending active fault traces along the KHF in the western part changes to NE-SW or ENE-WSW near Wandhay village. Trenching survey across a low scarp near Wandhay village reveals three major fault strands F1, F2,and F3.These fault strands displaced the older terrace deposits comprising Sand,Silt and Gravel units along with overlying younger deposits from units 1 to 5 made of gravel,sand and silt. Stratigraphic relationship indicates at least three large magnitude earthquakes along KHF during Late Holocene or recent historic past.

  19. Study of Fault Diagnosis Method for Wind Turbine with Decision Classification Algorithms and Expert System

    Directory of Open Access Journals (Sweden)

    Feng Yongxin

    2012-09-01

    Full Text Available Study on the fault diagnosis method through the combination of decision classification algorithms and expert system. The method of extracting diagnosis rules with the CTree software was given, and a fault diagnosis system based on CLIPS was developed. In order to verify the feasibility of the method, at first the sample data was got through the simulations under fault of direct-drive wind turbine and gearbox, then the diagnosis rules was extracted with the CTree software, at last the fault diagnosis system proposed and the rules was used to extracted to diagnose the fault simulated. Test results showed that the misdiagnosis rate both within 5%, thus the feasibility of the method was verified.

  20. Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

    Directory of Open Access Journals (Sweden)

    Bo Zhou

    2016-01-01

    Full Text Available Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT, is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.

  1. Application of data fusion method to fault diagnosis of nuclear power plant

    Institute of Scientific and Technical Information of China (English)

    XIE Chun-li; XIA Hong; LIU Yong-kuo

    2005-01-01

    The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example. The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely.

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

    Directory of Open Access Journals (Sweden)

    Yingyi Chen

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-14

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

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

    Science.gov (United States)

    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.

  5. A Fault Diagnosis Expert System for a Heavy Motor Used in a Rolling Mill

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A fault diagnosis expert system for a heavy motor used in a rolling mill is established in this paper. The fault diagnosis knowledge base was built, and its knowledge was represented by production rules. The knowledge base includes daily inspection system, brief diagnosis system and precise diagnosis system. A pull-down menu was adopted for the management of the knowledge base. The system can run under the help of expert system development tools. Practical examples show that the expert system can diagnose faults rapidly and precisely.

  6. A Fault Diagnosis Method of Power Systems Based on Gray System Theory

    Directory of Open Access Journals (Sweden)

    Huang Darong

    2015-01-01

    Full Text Available To provide some decision-making suggestions for fault diagnosis in power systems, a new model for identifying fault component is constructed by using Gray theory. Firstly, the basic concepts of Gray theory are introduced and explained in detail. And then the recognition algorithm of the power supply interrupted districts and the assignment principle of fault state vectors are depicted according to the working principle of protective relays (PRs and circuit breakers (CBs. Secondly, based on the concept of the Gray correlation degree, the fault information explanation degree model is constructed and the judging method of malfunction and rejection for PRs and CBs is established. Meanwhile, to achieve the goal of the fault diagnosis, the fault diagnosis procedure that determined which components malfunction is designed for power systems. Finally, some simple experiments have already verified that the proposed method and model are effective and reasonable and the trend of further research is analyzed and summarized.

  7. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

    Science.gov (United States)

    Mao, Wentao; He, Ling; Yan, Yunju; Wang, Jinwan

    2017-01-01

    Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical system, and machine learning has been a promising tool in this field. In many real applications of bearings fault diagnosis, the data tend to be online imbalanced, which means, the number of fault data is much less than the normal data while they are all collected in online sequential way. Suffering from this problem, many traditional diagnosis methods will get low accuracy of fault data which acts as the minority class in the collected bearing data. To address this problem, an online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine. This method introduces the principal curve and granulation division to simulate the flow distribution and overall distribution characteristics of fault data, respectively. Then a confident over-sampling and under-sampling process is proposed to establish the initial offline diagnosis model. In online stage, the obtained granules and principal curves are rebuilt on the bearing data which are arrived in sequence, and after the over-sampling and under-sampling process, the balanced sample set is formed to update the diagnosis model dynamically. A theoretical analysis is provided and proves that, even existing information loss, the proposed method has lower bound of the model reliability. Simulation experiments are conducted on IMS bearing data and CWRU bearing data. The comparative results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms.

  8. Fault Self-Diagnosis for Modular Robotic Systems Using M-Lattice Modules

    Directory of Open Access Journals (Sweden)

    Enguang Guan

    2015-04-01

    Full Text Available In the domain of modular robotic systems, self-configuration, self-diagnosis and self-repair are known to be highly challenging tasks. This paper presents a novel fault self-diagnosis strategy which consists of two parts: fault detection and fault message transmission. In fault detection, a bionic synchronization ‘healthy heartbeat’ method is used to guarantee the high efficiency of the exogenous detection strategy. For fault message transmission, the Dijkstra method is modified to be capable of guiding the passage of fault messages along the optimal path. In a modular robotic system, fault message transmission depends mainly on local communications between adjacent modules, so there is no need for global broadcast information. Computational simulations of one system form, M-Lattice, have demonstrated the practical effectiveness of the proposed strategy. The strategy should be applicable in modular robotic systems in general.

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

    Science.gov (United States)

    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.

  10. Robust Fault Diagnosis Design for Linear Multiagent Systems with Incipient Faults

    Directory of Open Access Journals (Sweden)

    Jingping Xia

    2015-01-01

    Full Text Available The design of a robust fault estimation observer is studied for linear multiagent systems subject to incipient faults. By considering the fact that incipient faults are in low-frequency domain, the fault estimation of such faults is proposed for discrete-time multiagent systems based on finite-frequency technique. Moreover, using the decomposition design, an equivalent conclusion is given. Simulation results of a numerical example are presented to demonstrate the effectiveness of the proposed techniques.

  11. Active Faulting and Quaternary Landforms Deformation Related to the Nain Fault

    Directory of Open Access Journals (Sweden)

    Abolghasem Gourabi

    2011-01-01

    Full Text Available Problem statement: Landforms developed across terrain defining boundary the Nain fault have imprints of recent tectonic activity in the west region of Central Iran. Depositional landforms such as alluvial fans bear signatures of later phases of tectonic activity in the form of faulting of alluvial fan deposits and development of fault traces and scarps within 100 km long and a NW-SE-trending zone, 1000-2000 m wide. Approach: We are addressing the neotectonic landforms based on detailed field work carried out in the Nain exposed active fault segments which brought forward some outstanding morphtectonic evidence of quaternary tectonically activities. Tectonic geomorphology applied to the Nain fault suggests recent subsurface activity along the Nain fault and an interconnecting faulting network of roughly NW-SE-trending, right-lateral, strike-slip segments and mostly NW-SE-oriented, transtensional to normal faults. Results: Evidence for recent activity is provided by faulted Pleistocene-Holocene deposits, fresh scarps in Late Quaternary deposits, 8-15 m lateral offsets locally affecting the drainage pattern of the area, ground creeping, aligning of series of spring faults, deflected streams and fault trace over recent alluvial fans. The existences of strike-slip faults system in the Nain area can be implications for seismic hazard. Conclusion: Motion along these structures suggests, in fact, that cumulative displacements include normal, transtensional and strike-slip components. Based on all evidence of active tectonics, earthquake risk and occurrence area is significant.

  12. Active fault survey on the Tanlu fault zone in Laizhou Bay

    Institute of Scientific and Technical Information of China (English)

    WANG Zhi-cai; YANG Xi-ha; LI Chang-chuan; DENG Qi-dong; DU Xian-song; CHAO Hong-tai; WU Zi-quan; XIAO Lan-xi; SUN Zhao-ming; MIN Wei; LING Hong

    2006-01-01

    Shallow-depth acoustic reflection profiling survey has been conducted on the Tanlu fault zone in Laizhou Bay. It is found that the Tanlu fault zone is obviously active during the late Quaternary and it is still the dominating structure in this region. The Tanlu fault zone consists of two branches. The KL3 fault of the western branch is composed of several high angle normal faults which had been active during the period from the latest Pleistocene to early Holocene, dissected by a series of northeast or approximate east-west trending fault which leaped sediment of the late Pleistocene. The Longkou fault of the eastern branch consists of two right-laterally stepped segments. Late Quaternary offsets and growth strata developed along the Tanlu fault zone verify that the fault zone retained active in the latest Pleistocene to the early Holocene. The Anqiu-Juxian fault that passes through the middle of Shandong and corresponds to the Longkou fault is composed of a series of right-laterally stepped segments. The active faults along the eastern branch of the Tanlu fault zone from the Laizhou bay to the north of Anqiu make up a dextral simple shear deformation zone which is characterized by right-lateral strike-slip movement with dip-slip component during the late Quaternary.

  13. Lateral migration of fault activity in Weihe basin

    Institute of Scientific and Technical Information of China (English)

    冯希杰; 戴王强

    2004-01-01

    Lateral migration of fault activity in Weihe basin is a popular phenomenon and its characteristics are also typical.Taking the activity migrations of Wangshun Mountain piedmont fault toward Lishan piedmont fault and Weinan platform front fault, Dabaopi-Niujiaojian fault toward Shenyusi-Xiaojiazhai fault, among a serial of NE-trending faults from Baoji city to Jingyang County as examples, their migration time and process are analyzed and discussed in the present paper. It is useful for further understanding the structure development and physiognomy evolution history of Weihe basin.

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

    Energy Technology Data Exchange (ETDEWEB)

    Weerasinghe, M

    1998-06-01

    Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

  15. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

  16. Control Surface Fault Diagnosis with Specified Detection Probability - Real Event Experiences

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2013-01-01

    Diagnosis of actuator faults is crucial for aircraft since loss of actuation can have catastrophic consequences. For autonomous aircraft the steps necessary to achieve fault tolerance is limited when only basic and non-redundant sensor and actuators suites are present. Through diagnosis...... that exploits analytical redundancies it is, nevertheless, possible to cheaply enhance the level of safety. This paper presents a method for diagnosing control surface faults by using basic sensors and hardware available on an autonomous aircraft. The capability of fault diagnosis is demonstrated obtaining...... false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft....

  17. Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

    Institute of Scientific and Technical Information of China (English)

    Mou Chen; Chang-Sheng Jiang; Qing-Xian Wu

    2008-01-01

    In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

  18. Sensor fault diagnosis of time-delay systems based on adaptive observer

    Institute of Scientific and Technical Information of China (English)

    YOU Fu-qiang; TIAN Zuo-hua; SHI Song-jiao

    2006-01-01

    Presents a novel approach for the sensor fault diagnosis of time-delay systems by using an adaptive observer technique. The sensor fault is modeled as an additive perturbation described by a time varying function. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. An adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for general system to improve robustness. The stability of fault diagnosis system is proved. Finally, a numerical example is given to illustrate the efficiency of the proposed method.

  19. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.;

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid...... that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filters that do not need a priori information about the fault internal models. Simulation results are based...... on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme....

  20. Fault self-diagnosis designing method of the automotive electronic control system

    Science.gov (United States)

    Ding, Yangyan; Yang, Zhigang; Fu, Xiaolin

    2005-12-01

    The fault self-diagnosis system is an important component of an the automotive electronic control system. Designers of automotive electronic control systems urgently require or need a complete understanding of the self-diagnosis designing method of the control system in order to apply it in practice. Aiming at this exigent need, self-diagnosis methods of designing sensors, electronic control unit (ECU), and actuators, which are the three main parts of automotive electronic control systems, are discussed in this paper. According to the fault types and characteristics of commonly used sensors, self-diagnosis designing methods of the sensors are discussed. Then fault diagnosis techniques of sensors utilizing signal detection and analytical redundancy are analysed and summarized respectively, from the viewpoint of the self-diagnosis designing method. Also, problems about failure self-diagnosis of ECU are analyzed here. For different fault types of an ECU, setting up a circuit monitoring method and a self-detection method of the hardware circuit are adopted respectively. Using these two methods mentioned above, a real-time and on-line technique of failure self-diagnosis is presented. Furthermore, the failure self-diagnosis design method of ECU are summarized. Finally, common faults of actuators are analyzed and the general design method of the failure self-diagnosis system is presented. It is suggested that self-diagnosis design methods relative to the failure of automotive electronic control systems can offer a useful approach to designers of control systems.

  1. Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

    Directory of Open Access Journals (Sweden)

    Jian-wei Yang

    2015-01-01

    Full Text Available Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs, such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1 Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2 Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

  2. Research of the Fault Diagnosis Method for the Thruster of AUV Based on Information Fusion

    Science.gov (United States)

    Wang, Yu-Jia; Zhang, Ming-Jun; Wu, Juan

    Aiming at the problem of thruster fault diagnosis of AUV, the motion condition model of AUV based on the improved dynamic recursive Elman neural network, and the performance model of thruster based on the Radial Basis Function network were established. And the fault fusion diagnosis method was proposed according to the overall and local fault detection. Through comparing the output value of motion condition model with the measured value of actual speed and angle, it obtained the overall fault information. Also, it obtained the direct fault information through analyzing the residual which was produced by comparing the output of the performance model with the measured value of the actual voltage and current of the each thruster. According to the decision level information fusion of two kinds of information, it realized the fault diagnosis of thrusters and analyzed the fault degree and reliability. The results of the fault-simulation experiment show that the proposed fault fusion diagnosis method for the thruster of AUV is feasible and effective.

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

    Directory of Open Access Journals (Sweden)

    Zhixin Yang

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Yunxiao Fu

    2016-06-01

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

  5. Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    TIAN Huifang; SUN Shanxia

    2006-01-01

    By introducing Rough Set Theory and the principle of Support vector machine, a gear fault diagnosis method based on them is proposed. Firstly, diagnostic decision-making is reduced based on rough set theory, and the noise and redundancy in the sample are removed, then, according to the chosen reduction, a support vector machine multi-classifier is designed for gear fault diagnosis. Therefore, SVM' training data can be reduced and running speed can quicken. Test shows its accuracy and efficiency of gear fault diagnosis.

  6. Neural network fault diagnosis method optimization with rough set and genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    SUN Hong-yan; XIE Zhi-jiang; OUYANG Qi

    2006-01-01

    Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

  7. Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jian-Jiun Ding

    2012-07-01

    Full Text Available Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE and multiscale entropy (MSE.

  8. An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP ( Digital Signal Processor) and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration,etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.

  9. Early Oscillation Detection for DC/DC Converter Fault Diagnosis

    Science.gov (United States)

    Wang, Bright L.

    2011-01-01

    The electrical power system of a spacecraft plays a very critical role for space mission success. Such a modern power system may contain numerous hybrid DC/DC converters both inside the power system electronics (PSE) units and onboard most of the flight electronics modules. One of the faulty conditions for DC/DC converter that poses serious threats to mission safety is the random occurrence of oscillation related to inherent instability characteristics of the DC/DC converters and design deficiency of the power systems. To ensure the highest reliability of the power system, oscillations in any form shall be promptly detected during part level testing, system integration tests, flight health monitoring, and on-board fault diagnosis. The popular gain/phase margin analysis method is capable of predicting stability levels of DC/DC converters, but it is limited only to verification of designs and to part-level testing on some of the models. This method has to inject noise signals into the control loop circuitry as required, thus, interrupts the DC/DC converter's normal operation and increases risks of degrading and damaging the flight unit. A novel technique to detect oscillations at early stage for flight hybrid DC/DC converters was developed.

  10. Fault detection and diagnosis in a food pasteurization process with Hidden Markov Models

    OpenAIRE

    Tokatlı, Figen; Cinar, Ali

    2004-01-01

    Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a ...

  11. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    OpenAIRE

    Baldi, P.; Blanke, Mogens; P. Castaldi; Mimmo, N.; S. Simani

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filt...

  12. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    OpenAIRE

    Baldi, Pietro; Blanke, Mogens; Castaldi, Paolo; Mimmo, Nicola; Simani, Silvio

    2016-01-01

    This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank of residual filters organized in a generalized scheme, followed by a fault estimation module consisting of a bank of adaptive estimation filters. The residuals are decoupled from aerodynamic disturbance...

  13. Fault diagnosis in neutral point indirectly grounded system based on information fusion

    Institute of Scientific and Technical Information of China (English)

    于飞; 鞠丽叶; 刘喜梅; 崔平远; 钟秋海

    2003-01-01

    In neutral point indirectly grounded systems, phase-to-ground fault is putting new demands on fault diagnosis technology. Information fusion is applied to detect the phase-to-ground fault, which integrates several sources of information, including line current, line voltage, zero sequence current and voltage, and quintic harmonic wave component. This method is testified through the simulation of Matlab. Simulation results show that the precision and reliability of the detection has been greatly increased.

  14. Robust On-Line Fault Diagnosis for Nonlinear Difference-Algebraic Systems Using Least Squares Estimate

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A new robust on-line fault diagnosis method based on least squares estimate for nonlinear difference-algebraic systems (DAS) with uncertainties is proposed. Based on the known nominal model of the DAS, this method firstly constructs an auxiliary system consisting of a difference equation and an algebraic equation, then, based on the relationship between the state deviation and the faults in the difference equation and the relationship between the algebraic variable deviation and the faults in algebraic equation, it identifies the faults on-line through least squares estimate. This method can not only detect, isolate and identify faults for DAS, but also give the upper bound of the error of fault identification. The simulation results indicate that it can give satisfactory diagnostic results for both abrupt and incipient faults.

  15. Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor

    Institute of Scientific and Technical Information of China (English)

    RONG Ming-xing

    2012-01-01

    In the motor fault diagnosis technique, vibration and stator current frequency components of detection are two main means. This article will discuss the signal detection method based on vibration fault. Because the motor vibration signal is a non-stationary random signal, fault signals often contain a lot of time-varying, burst proper- ties of ingredients. The traditional Fourier signal analysis can not effectively extract the motor fault characteristics, but are also likely to be rich in failure information but a weak signal as noise. Therefore, we introduce wavelet packet transforms to extract the fault characteristics of the signal information. Obtained was the result as the neural network input signal, using the L-M neural network optimization method for training, and then used the BP net- work for fault recognition. This paper uses Matlab software to simulate and confirmed the method of motor fault di- agnosis validity and accuracy

  16. Artificial immunity-based induction motor bearing fault diagnosis

    OpenAIRE

    Hakan ÇALIŞ; ÇAKIR, Abdülkadir; Emre DANDIL

    2013-01-01

    In this study, the artificial immunity of the negative selection algorithm is used for bearing fault detection. It is implemented in MATLAB-based graphical user interface software. The developed software uses amplitudes of the vibration signal in the time and frequency domains. Outer, inner, and ball defects in the bearings of the induction motor are detected by anomaly monitoring. The time instants of the fault occurrence and fault level are determined according to the number of a...

  17. Geophysical characterization of buried active faults: the Concud Fault (Iberian Chain, NE Spain)

    Science.gov (United States)

    Pueyo Anchuela, Óscar; Lafuente, Paloma; Arlegui, Luis; Liesa, Carlos L.; Simón, José L.

    2016-11-01

    The Concud Fault is a 14-km-long active fault that extends close to Teruel, a city with about 35,000 inhabitants in the Iberian Range (NE Spain). It shows evidence of recurrent activity during Late Pleistocene time, posing a significant seismic hazard in an area of moderate-to-low tectonic rates. A geophysical survey was carried out along the mapped trace of the southern branch of the Concud Fault to evaluate the geophysical signature from the fault and the location of paleoseismic trenches. The survey identified a lineation of inverse magnetic dipoles at residual and vertical magnetic gradient, a local increase in apparent conductivity, and interruptions of the underground sediment structure along GPR profiles. The origin of these anomalies is due to lateral contrast between both fault blocks and the geophysical signature of Quaternary materials located above and directly south of the fault. The spatial distribution of anomalies was successfully used to locate suitable trench sites and to map non-exposed segments of the fault. The geophysical anomalies are related to the sedimentological characteristics and permeability differences of the deposits and to deformation related to fault activity. The results illustrate the usefulness of geophysics to detect and map non-exposed faults in areas of moderate-to-low tectonic activity where faults are often covered by recent pediments that obscure geological evidence of the most recent earthquakes. The results also highlight the importance of applying multiple geophysical techniques in defining the location of buried faults.

  18. Application of local wave time-frequency method in reciprocating mechanical fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    Wang Lei; Wang Fengtao; Ma Xiaojiang

    2006-01-01

    To diagnose the reciprocating mechanical fault. We utilized local wave time-frequency approach. Firstly,we gave the principle. Secondly, the application of local wave time-frequency was given. Finally, we discussed its virtue in reciprocating mechanical fault diagnosis.

  19. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Reaction Wheels

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2015-01-01

    This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank...

  20. Research on Gear-broken Fault Diagnosis in a Tank Gearbox

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A fault diagnosis method of working position gear in a tank gearbox is put forward based on simulating the fault of working position gear in an actual tank, extracting the envelope of vibration signal by Hilbert transformation amplitude demodulation method, and zooming the low-frequency band to envelope signal.

  1. Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    CHEN Xinyi; YAN Xuefeng

    2013-01-01

    Fault diagnosis and monitoring are very important for complex chemical process.There are numerous methods that have been studied in this field,in which the effective visualization method is still challenging.In order to get a better visualization effect,a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed.FDA can reduce the dimension of the data in terms of maximizing the separability of the classes.After feature extraction by FDA,SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states.Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method.The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

  2. Nuclear power plant fault diagnosis based on genetic-RBF neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Xiao-cheng; XIE Chun-ling; WANG Yuan-hui

    2006-01-01

    It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neural Network (RBFNN) has the characteristics of optimal approximation and global approximation. The mixed coding of binary system and decimal system is introduced to the structure and parameters of RBFNN, which is trained in course of the genetic optimization. Finally, a fault diagnosis system according to the frequent faults in condensation and feed water system of nuclear power plant is set up. As a result, Genetic-RBF Neural Network (GRBFNN) makes the neural network smaller in size and higher in generalization ability. The diagnosis speed and accuracy are also improved.

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

    Directory of Open Access Journals (Sweden)

    Xianfeng Yuan

    2015-01-01

    presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

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

    Directory of Open Access Journals (Sweden)

    Zengkai Liu

    Full Text Available 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.

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

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

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

  6. Fault detection and diagnosis for refrigerator from compressor sensor

    Energy Technology Data Exchange (ETDEWEB)

    Keres, Stephen L.; Gomes, Alberto Regio; Litch, Andrew D.

    2016-12-06

    A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identified if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  9. Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices.

    Science.gov (United States)

    Wu, Yunkai; Jiang, Bin; Lu, Ningyun; Yang, Hao; Zhou, Yang

    2017-03-01

    This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach.

  10. Fault Diagnosis of a Turbo-unit Based on Wavelet Packet Theory

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In this paper we studied the fault feature of the generator set and the characteristics of wavelet packet theory for signal de-noising. The vibration signal of the generator set in diffrent states is analyzed by using the signal re-construction technique of the wavelet packet theory. The time domain method is given for the generator set fault diagnosis. The experiment results show that the wavelet packet theory can be used to directly identify the state of the generator set and provide a credible new idea for complex machinery fault diagnosis.

  11. An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis

    Science.gov (United States)

    Qiao, Zijian; Lei, Yaguo; Lin, Jing; Jia, Feng

    2017-02-01

    In mechanical fault diagnosis, most traditional methods for signal processing attempt to suppress or cancel noise imbedded in vibration signals for extracting weak fault characteristics, whereas stochastic resonance (SR), as a potential tool for signal processing, is able to utilize the noise to enhance fault characteristics. The classical bistable SR (CBSR), as one of the most widely used SR methods, however, has the disadvantage of inherent output saturation. The output saturation not only reduces the output signal-to-noise ratio (SNR) but also limits the enhancement capability for fault characteristics. To overcome this shortcoming, a novel method is proposed to extract the fault characteristics, where a piecewise bistable potential model is established. Simulated signals are used to illustrate the effectiveness of the proposed method, and the results show that the method is able to extract weak fault characteristics and has good enhancement performance and anti-noise capability. Finally, the method is applied to fault diagnosis of bearings and planetary gearboxes, respectively. The diagnosis results demonstrate that the proposed method can obtain larger output SNR, higher spectrum peaks at fault characteristic frequencies and therefore larger recognizable degree than the CBSR method.

  12. Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

    Directory of Open Access Journals (Sweden)

    Saud Altaf

    2017-01-01

    Full Text Available In this paper, broken rotor bar (BRB fault is investigated by utilizing the Motor Current Signature Analysis (MCSA method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

  13. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis

    Science.gov (United States)

    Liu, Ruonan; Yang, Boyuan; Zhang, Xiaoli; Wang, Shibin; Chen, Xuefeng

    2016-06-01

    Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.

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

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Directory of Open Access Journals (Sweden)

    Lingli Jiang

    2011-01-01

    Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

  16. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    Science.gov (United States)

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

  17. Analysis of experimental result and fault diagnosis for aeroengine rotating shaft

    Science.gov (United States)

    Zhao, Baoqun; Wang, Yuanyang

    2008-10-01

    To increase the accuracy of applying traditional fault diagnosis method to aeroengine vibrant faults, a novel approach based on wavelet neural network is proposed. The effective signal features are acquired by wavelet transform with multi-resolution analysis. These feature vectors then are applied to the neural network for training and testing. The synthesized method of recursive orthogonal least squares algorithm is used to fulfill the network structure and parameter initialization. By means of choosing enough practical samples to verify the proposed network performance, the information representing the faults is inputted into the trained network. According to the output result the fault pattern can be determined. The simulation results and actual applications show that the method can effectively diagnose and analyze the vibrant fault patterns of aeroengine and the diagnosis result is correct.

  18. Application of Extension Neural Network Type-1 to Fault Diagnosis of Electronic Circuits

    Directory of Open Access Journals (Sweden)

    Meng-Hui Wang

    2012-01-01

    Full Text Available The values of electronic components are always deviated, but the functions of the modern circuits are more and more precise, which makes the automatic fault diagnosis of analog circuits very complex and difficult. This paper presents an extension-neural-network-type-1-(ENN-1- based method for fault diagnosis of analog circuits. This proposed method combines the extension theory and neural networks to create a novel neural network. Using the matter-element models of fault types and a correlation function, can be calculated the correlation degree between the tested pattern and every fault type; then, the cause of the circuit malfunction can be directly diagnosed by the analysis of the correlation degree. The experimental results show that the proposed method has a high diagnostic accuracy and is more fault tolerant than the multilayer neural network (MNN and the k-means based methods.

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

    Science.gov (United States)

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

    2016-09-01

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

  20. A New Fault Diagnosis Algorithm for PMSG Wind Turbine Power Converters under Variable Wind Speed Conditions

    Directory of Open Access Journals (Sweden)

    Yingning Qiu

    2016-07-01

    Full Text Available Although Permanent Magnet Synchronous Generator (PMSG wind turbines (WTs mitigate gearbox impacts, they requires high reliability of generators and converters. Statistical analysis shows that the failure rate of direct-drive PMSG wind turbines’ generators and inverters are high. Intelligent fault diagnosis algorithms to detect inverters faults is a premise for the condition monitoring system aimed at improving wind turbines’ reliability and availability. The influences of random wind speed and diversified control strategies lead to challenges for developing intelligent fault diagnosis algorithms for converters. This paper studies open-circuit fault features of wind turbine converters in variable wind speed situations through systematic simulation and experiment. A new fault diagnosis algorithm named Wind Speed Based Normalized Current Trajectory is proposed and used to accurately detect and locate faulted IGBT in the circuit arms. It is compared to direct current monitoring and current vector trajectory pattern approaches. The results show that the proposed method has advantages in the accuracy of fault diagnosis and has superior anti-noise capability in variable wind speed situations. The impact of the control strategy is also identified. Experimental results demonstrate its applicability on practical WT condition monitoring system which is used to improve wind turbine reliability and reduce their maintenance cost.

  1. Research on fault mode and diagnosis of methane sensor

    Institute of Scientific and Technical Information of China (English)

    WANG Qi-jun; CHENG Jiu-long

    2008-01-01

    To improve the reliability of coal mine safety monitoring systems we have analyzed the characteristics of a methane sensor, an important component of the monitoring system of production safety in a coal mine and studied the main type and mode of faults when the sensor was used on-line. We introduced a new method based on artificial neural network to detect faults of me-thane sensors. In addition, using the output information of a single methane sensor, we established a sensor output model of a dy-namic non-linear neural network for on-line fault detection. Finally, the fault of the heating wire of the sensor was simulated, indi-cating that, when the methane sensor had a fault, the predicted output of the neural network clearly deviated from the actual output,exceeding the pre-set threshold and showing that a fault had occurred in the methane sensor. The result shows that the model has good convergence and stability, and is quite capable of meeting the requirements for on-line fault detection of methane sensors.

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

    Directory of Open Access Journals (Sweden)

    Sheraz Ali Khan

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Wei-Li Qin

    2016-01-01

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

  4. Fault Diagnosis for Electrical Distribution Systems using Structural Analysis

    DEFF Research Database (Denmark)

    Knüppel, Thyge; Blanke, Mogens; Østergaard, Jacob

    2014-01-01

    Fault-tolerance in electrical distribution relies on the ability to diagnose possible faults and determine which components or units cause a problem or are close to doing so. Faults include defects in instrumentation, power generation, transformation and transmission. The focus of this paper...... redundancies in large sets of equations only from the structure (topology) of the equations. A salient feature is automated generation of redundancy relations. The method is indeed feasible in electrical networks where circuit theory and network topology together formulate the constraints that define...

  5. Design of a real-time fault diagnosis expert system for the EAST cryoplant

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Zhiwei, E-mail: zzw@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Zhuang Ming, E-mail: zhm@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Lu Xiaofei, E-mail: luxf1212@mail.ustc.edu.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Hu Liangbing, E-mail: huliangbing@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Xia Genhai, E-mail: xgh@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China)

    2012-12-15

    Highlights: Black-Right-Pointing-Pointer An expert system of real-time fault diagnosis for EAST cryoplant is designed. Black-Right-Pointing-Pointer Knowledge base is built via fault tree analysis based on our fault experience. Black-Right-Pointing-Pointer It can make up the deficiency of safety monitoring in cryogenic DCS. Black-Right-Pointing-Pointer It can help operators to find the fault causes and give operation suggestion. Black-Right-Pointing-Pointer It plays a role of operators training in certain degree. - Abstract: The EAST cryoplant consists of a 2 kW/4 K helium refrigerator and a helium distribution system. It is a complex process system which involves many process variables and cryogenic equipments. Each potential fault or abnormal event may influence stability and safety of the cryogenic system, thereby disturbing the fusion experiment. The cryogenic control system can monitor the process data and detect process alarms, but it is difficult to effectively diagnose the fault causes and provide operation suggestions to operators when anomalies occur. Therefore, a real-time fault diagnosis expert system is essential for a safe and steady operation of EAST cryogenic system. After a brief description of the EAST cryoplant and its control system, the structure design of the cryogenic fault diagnosis expert system is proposed. Based on the empirical knowledge, the fault diagnosis model is built adopting fault tree analysis method which considers the uncertainty. The knowledge base and the inference machine are presented in detail. A cross-platform integrated development environment Qt Creator and MySQL database have been used to develop the system. The proposed expert system has a fine graphic user interface for monitoring and operation. Preliminary test was conducted and the results found to be satisfactory.

  6. Power transformer fault diagnosis model based on rough set theory with fuzzy representation

    Institute of Scientific and Technical Information of China (English)

    Li Minghua; Dong Ming; Yan Zhang

    2007-01-01

    Objective Due to the incompleteness and complexity of fault diagnosis for power transformers, a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented. Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space. The fuzzy membership functions corresponding to the indicative regions, modelled by rules, are stored as cases. Results Diagnostic conclusions are made using a similarity measure based on these membership functions. Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis. Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.

  7. Nuclear power plant fault-diagnosis using neural networks with error estimation

    Energy Technology Data Exchange (ETDEWEB)

    Kim, K.; Bartlett, E.B.

    1994-12-31

    The assurance of the diagnosis obtained from a nuclear power plant (NPP) fault-diagnostic advisor based on artificial neural networks (ANNs) is essential for the practical implementation of the advisor to fault detection and identification. The objectives of this study are to develop an error estimation technique (EET) for diagnosis validation and apply it to the NPP fault-diagnostic advisor. Diagnosis validation is realized by estimating error bounds on the advisor`s diagnoses. The 22 transients obtained from the Duane Arnold Energy Center (DAEC) training simulator are used for this research. The results show that the NPP fault-diagnostic advisor are effective at producing proper diagnoses on which errors are assessed for validation and verification purposes.

  8. ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

    Directory of Open Access Journals (Sweden)

    Zhihuai Xiao

    2015-01-01

    Full Text Available Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO- initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the frequency components of the hydroturbine generating unit vibration signals are used as feature vectors for wavelet neural network training to realize mapping relationship from vibration features to fault types. A real vibration fault diagnosis case result of a hydroturbine generating unit shows that the proposed method has faster convergence speed and stronger generalization ability than the traditional wavelet neural network and ACO wavelet neural network. Thus it can provide an effective solution for online vibration fault diagnosis of a hydroturbine generating unit.

  9. Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

    Directory of Open Access Journals (Sweden)

    Manikandan Pandiyan

    2014-09-01

    Full Text Available The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI. Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

  10. Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Lixiang Duan

    2016-01-01

    Full Text Available Effective signal processing in fault detection and diagnosis (FDD is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet packet transform (ULWPT is investigated for machinery incipient fault diagnosis. Undecimated lifting wavelet packet transform is firstly formulated to eliminate the frequency aliasing issue in traditional lifting wavelet packet transform. Next, Volterra series, as a boundary treatment method, is used to preprocess the signal to suppress the end distortion in undecimated lifting wavelet packet transform. Finally, the decomposed wavelet coefficients are trimmed to the original length as the signal of interest for machinery incipient fault detection. Experimental study on a reciprocating compressor is performed to demonstrate the effectiveness of the presented method. The results show that the presented method outperforms the conventional approach by dramatically enhancing the weak defect feature extraction for reciprocating compressor valve fault diagnosis.

  11. Fault diagnosis for micro-gas turbine engine sensors via wavelet entropy.

    Science.gov (United States)

    Yu, Bing; Liu, Dongdong; Zhang, Tianhong

    2011-01-01

    Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can't be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient.

  12. Fault diagnosis of the rolling bearing with optical fiber Bragg grating vibration sensor

    Science.gov (United States)

    Wei, Peng; Dai, Zejing; Zheng, Leilei; Li, Ming

    2016-10-01

    Fault diagnosis of the rolling bearing means a lot for property and life safety. In this paper the Fiber Bragg Grating (FBG) vibration sensor and resonance demodulation technology are used in the fault diagnosis of the rolling bearing. Traditionally, the vibration signals are measured by the resistance strain gauge, accelerometer, etc. But those traditional electronic sensors are usually influenced by the industry electromagnetic noise. But the FBG vibration sensor is totally different. It has a lot of advantages such as small volume, light weight, easy connection and so on. And the high industry electromagnetic noise means nothing to the FBG sensors. In this paper, we use the FBG vibration and temperature sensors to measure the fast strain and temperature signal of the rolling bearing. In order to extract the fault signals from strong background noise, the resonant demodulation technology is used to analyze and process the vibration signals collected by the FBG sensors. In order to verify the reliability of the FBG vibration sensor and resonance demodulation technology applied in the fault diagnosis of the rolling bearing, several experiments are done. Five FBG vibration sensors are attached on the different parts of the rolling bearing to verify its function and its influence on the fault diagnosis of the rolling bearing. The results of the experiments show that the FBG vibration sensor method could be used in fault diagnosis of the rolling bearing. The repetitive experiments show the reliability of the FBG vibration sensors method.

  13. Neural network approach to fault diagnosis in CMOS opamps with gate oxide short faults

    Science.gov (United States)

    Yu, S.; Jervis, B. W.; Eckersall, K. R.; Bell, I. M.; Hall, A. G.; Taylor, G. E.

    1994-04-01

    Faults owing to gate oxide shorts in a CMOS opamp have been diagnosed in simulations using artificial neural networks to identify corresponding variations in supply current. Ramp and sinusoidal signals gave fault diagnostic accuracy of 67 and 83 percent, respectively. Using both test signals 100 percent diagnostic accuracy was achieved.

  14. Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis

    Science.gov (United States)

    Li, Yifan; Liang, Xihui; Zuo, Ming J.

    2017-02-01

    This paper presents a novel signal processing scheme, diagonal slice spectrum assisted optimal scale morphological filter (DSS-OSMF), for rolling element fault diagnosis. In this scheme, the concept of quadratic frequency coupling (QFC) is firstly defined and the ability of diagonal slice spectrum (DSS) in detection QFC is derived. The DSS-OSMF possesses the merits of depressing noise and detecting QFC. It can remove fault independent frequency components and give a clear representation of fault symptoms. A simulated vibration signal and experimental vibration signals collected from a bearing test rig are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearing. In addition, comparisons are performed between a multi-scale morphological filter (MMF) and a DSS-OSMF. DSS-OSMF outperforms MMF in detection of an outer race fault and a rolling element fault of a rolling element bearing.

  15. FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Yu Dejie; Cheng Junsheng; Yang Yu

    2005-01-01

    Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.

  16. Research on intelligent fault diagnosis based on time series analysis algorithm

    Institute of Scientific and Technical Information of China (English)

    CHEN Gang; LIU Yang; ZHOU Wen-an; SONG Jun-de

    2008-01-01

    Aiming to realize fast and accurate fault diagnosisin complex network environment, this article proposes a set ofanomaly detection algorithm and intelligent fault diagnosismodel. Firstly, a novel anomaly detection algorithm based ontime series analysis is put forward to improve the generalizedlikelihood ratio (GLR) test, and thus, detection accuracy isenhanced and the algorithm complexity is reduced. Secondly,the intelligent fault diagnosis model is established byintroducing neural network technology, and thereby, theanomaly information of each node in end-to-end network isintegrated and processed in parallel to intelligently diagnosethe fault cause. Finally, server backup solution in enterpriseinformation network is taken as the simulation scenario. Theresults demonstrate that the proposed method can not onlydetect fault occurrence in time, but can also implement onlinediagnosis for fault cause, and thus, real-time and intelligent faultmanagement process is achieved.

  17. Active fault diagnosis based on stochastic tests

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2008-01-01

    or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown...... how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example....

  18. Quaternary Geology and Surface Faulting Hazard: Active and Capable Faults in Central Apennines, Italy

    Science.gov (United States)

    Falcucci, E.; Gori, S.

    2015-12-01

    The 2009 L'Aquila earthquake (Mw 6.1), in central Italy, raised the issue of surface faulting hazard in Italy, since large urban areas were affected by surface displacement along the causative structure, the Paganica fault. Since then, guidelines for microzonation were drew up that take into consideration the problem of surface faulting in Italy, and laying the bases for future regulations about related hazard, similarly to other countries (e.g. USA). More specific guidelines on the management of areas affected by active and capable faults (i.e. able to produce surface faulting) are going to be released by National Department of Civil Protection; these would define zonation of areas affected by active and capable faults, with prescriptions for land use planning. As such, the guidelines arise the problem of the time interval and general operational criteria to asses fault capability for the Italian territory. As for the chronology, the review of the international literature and regulatory allowed Galadini et al. (2012) to propose different time intervals depending on the ongoing tectonic regime - compressive or extensional - which encompass the Quaternary. As for the operational criteria, the detailed analysis of the large amount of works dealing with active faulting in Italy shows that investigations exclusively based on surface morphological features (e.g. fault planes exposition) or on indirect investigations (geophysical data), are not sufficient or even unreliable to define the presence of an active and capable fault; instead, more accurate geological information on the Quaternary space-time evolution of the areas affected by such tectonic structures is needed. A test area for which active and capable faults can be first mapped based on such a classical but still effective methodological approach can be the central Apennines. Reference Galadini F., Falcucci E., Galli P., Giaccio B., Gori S., Messina P., Moro M., Saroli M., Scardia G., Sposato A. (2012). Time

  19. Methods for Probabilistic Fault Diagnosis: An EPS Case Study

    Data.gov (United States)

    National Aeronautics and Space Administration — Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in...

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

    Directory of Open Access Journals (Sweden)

    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.

  1. APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Based on radial basis function (RBF) neural networks, the healthy working model of each sub-system of robot in FMS is established. A new approach to fault on-line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi-layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.

  2. Blade Fault Diagnosis in Small Wind Power Systems Using MPPT with Optimized Control Parameters

    Directory of Open Access Journals (Sweden)

    Jui-Ho Chen

    2015-08-01

    Full Text Available A systematic experiment verification of Chaos Embedded Sliding Mode Extremum Seeking Control for maximum power point tracking and a method for detecting possible faults in small wind turbine systems in advance are proposed in this paper. The chaotic logistic map is used to replace the random function in the particle swarm optimization algorithm for faster searching the optimal control parameter . From the experimental results, it is verified that the Chaos Embedded Sliding Mode Extremum Seeking Control scheme has a better dynamic response than traditional Extremum Seeking Control scheme and Hill-Climbing Search scheme for maximum power point tracking. In the proposed scheme for fault detection, a chaotic synchronization method is used to transform the maximum power point tracking signal into a chaos synchronization error distribution diagram. It is then taken as the characteristic for fault diagnosis purposes. Finally, an extension theory pattern recognition technique is applied to diagnose the fault. Notably, the use of the chaotic dynamic errors as the fault diagnosis characteristic reduces the number of extracted features required, and therefore greatly reduces both the computation time and the hardware implementation cost. From the experimental results, it is shown that the fault diagnosis rate of the proposed method exceeds 98% not only in non-real-time but also in real-time of faults detection of the blades.

  3. Simplified Interval Observer Scheme: A New Approach for Fault Diagnosis in Instruments

    Science.gov (United States)

    Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel

    2011-01-01

    There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

  4. STUDY OF REAL-TIME EXPERT SYSTEM TOOL FOR INDUSTRIAL FAULT MONITORING AND DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    谢桂林; 周建荣

    1992-01-01

    From the requirements ot industrial production,an integrated fault monitoring,diagnosis and repairing system is suggested in this paper. This new scheme of fault monitoring and diagnosis system is realized by a master-slave real-time expert system,and a real-time expert system tool for this system is also developed accordingly. As an example of application of this tool,a realtime expert system for fault monitoring and diagnosis on DC mine hoist is developed. Experiments show that this tool possesses better supporting environment,strong knowledge acquisition ability, and convenience for use. The system developed by this tool not only meets the realtime requirement of DC hoist,but also can give correct diagnosis results.

  5. A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Feng Lu

    2016-10-01

    Full Text Available Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.

  6. Condition Monitoring and Faults Diagnosis for Synchronous Generator Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Omer Elfaki Elbashir

    2013-09-01

    Full Text Available Early detection and diagnosis of incipient fault is desirable for on line condition assessment production quality assurance and improved operational efficiency of synchronous generator running of power supply. Artificial Intelligent techniques are increasly used for condition monitoring and fault diagnosis of machines. In this paper, Artificial Neural Network (ANN approach employed for fault diagnosis in the generator, based on monitoring generator currents to give indication of the winding faults. Feed-forward Network, error back propagation training algorithm are used to perform the generator faults diagnosis and their values. NN which has been trained for all possible operating condition of the machine used to classify the incoming data. The inputs of the NN are the stator and rotor currents, and the output represents the running condition of the generator. The training of the NN achieved by the data through a mathematical model based approach to simulate the generator faults at various degree of severity.This paper evaluates through simulation line currents magnitude of the generator .The final results have been represented on a monitoring unit, built using matlab program, to give early warning of the generator failure.

  7. Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

    Directory of Open Access Journals (Sweden)

    Jie Tao

    2016-01-01

    Full Text Available In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN. By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

  8. Active Fault Detection and Isolation for Hybrid Systems

    DEFF Research Database (Denmark)

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas;

    2009-01-01

    models predicted outputs such that their discrepancies are observable by passive fault diagnosis technique. Isolation of different faults is done by implementation a bank of Extended Kalman Filter (EKF) where the convergence criterion for EKF is confirmed by Genetic Algorithm (GA). The method is applied......An algorithm for active fault detection and isolation is proposed. In order to observe the failure hidden due to the normal operation of the controllers or the systems, an optimization problem based on minimization of test signal is used. The optimization based method imposes the normal and faulty...

  9. Active Fault Characterization in the Urban Area of Vienna

    Science.gov (United States)

    Decker, Kurt; Grupe, Sabine; Hintersberger, Esther

    2016-04-01

    The identification of active faults that lie beneath a city is of key importance for seismic hazard assessment. Fault mapping and characterization in built-up areas with strong anthropogenic overprint is, however, a challenging task. Our study of Quaternary faults in the city of Vienna starts from the re-assessment of a borehole database of the municipality containing several tens of thousands of shallow boreholes. Data provide tight constraints on the geometry of Quaternary deposits and highlight several locations with fault-delimited Middle to Late Pleistocene terrace sediments of the Danube River. Additional information is obtained from geological descriptions of historical outcrops which partly date back to about 1900. The latter were found to be particularly valuable by providing unprejudiced descriptions of Quaternary faults, sometimes with stunning detail. The along-strike continuations of some of the identified faults are further imaged by industrial 2D/3D seismic acquired outside the city limits. The interpretation and the assessment of faults identified within the city benefit from a very well constrained tectonic model of the active Vienna Basin fault system which derived from data obtained outside the city limits. This data suggests that the urban faults are part of a system of normal faults compensating fault-normal extension at a releasing bend of the sinistral Vienna Basin Transfer Fault. Slip rates estimated for the faults in the city are in the range of several hundredths of millimetres per year and match the slip rates of normal faults that were trenched outside the city. The lengths/areas of individual faults estimated from maps and seismic reach up to almost 700 km² suggesting that all of the identified faults are capable of producing earthquakes with magnitudes M>6, some with magnitudes up to M~6.7.

  10. Fault detection and diagnosis using neural network approaches

    Science.gov (United States)

    Kramer, Mark A.

    1992-01-01

    Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

  11. Cyclostationary Analysis for Gearbox and Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhipeng Feng

    2015-01-01

    Full Text Available Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.

  12. Intelligent Fault Diagnosis in Power Transformer with Using Dissolved Gas Analysis in different Standards by Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Rahmat Houshmand

    2007-09-01

    Full Text Available The power electric transformer fault diagnosis is based on dissolved gas-in-oil analysis (DGA. the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC and the key gas method, have limitations such as the “no decision” problem. Various artificial intelligence techniques may help solve the problems and present a better solution. In this paper present a fuzzy systems to fault diagnosis in power electric transformer by dissolved we gas analysis.

  13. Wavelet neural network based fault diagnosis in nonlinear analog circuits

    Institute of Scientific and Technical Information of China (English)

    Yin Shirong; Chen Guangju; Xie Yongle

    2006-01-01

    The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

  14. Diagnosis and Fault-tolerant Control for Ship Station Keeping

    DEFF Research Database (Denmark)

    Blanke, Mogens

    2005-01-01

    design for systems of high complexity, and also analyse the cases of cascaded or multiple faults. The paper takes as example a ship with two CP propellers, rudders and a bow thruster as actuators, and instrumentation with a suite of global position sensors, inertial navigation units and conventional gyro...... units to provide ship motion information. A salient feature of the design mehod is the ability to analyse cases where faults have occurrred and easily determine where in the faulty system diagnosability and controlability are retained....

  15. Active current control in wind power plants during grid faults

    DEFF Research Database (Denmark)

    Martinez, Jorge; Kjær, Phillip C.; Rodriguez, Pedro;

    2010-01-01

    , wind turbines usually have solutions that enable the turbines to control the generation of reactive power during faults. This paper addresses the importance of using an optimal injection of active current during faults in order to fulfil these grid codes. This is of relevant importance for severe......Modern wind power plants are required and designed to ride through faults in electrical networks, subject to fault clearing. Wind turbine fault current contribution is required from most countries with a high amount of wind power penetration. In order to comply with such grid code requirements...... faults, causing low voltages at the point of common coupling. As a consequence, a new wind turbine current controller for operation during faults is proposed. It is shown that to achieve the maximum transfer of reactive current at the point of common coupling, a strategy for optimal setting of the active...

  16. Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Neng-Sheng Pai

    2013-01-01

    Full Text Available Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF neural network and back propagation (BP neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields.

  17. Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

    Directory of Open Access Journals (Sweden)

    Yuan Pu

    2015-01-01

    Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

  18. A Review on Fault Mechanism and Diagnosis Approach for Li-Ion Batteries

    Directory of Open Access Journals (Sweden)

    Chao Wu

    2015-01-01

    Full Text Available Li-ion battery has attracted more and more attention as it is a promising storage device which has long service life, higher energy, and power density. However, battery ageing always occurs during operation and leads to performance degradation and system fault which not only causes inconvenience, but also risks serious consequences such as thermal runaway or even explosion. This paper reviews recent research and development of ageing mechanisms of Li-ion batteries to understand the origins and symptoms of Li-ion battery faults. Common ageing factors are covered with their effects and consequences. Through ageing tests, relationship between performance and ageing factors, as well as cross-dependence among factors can be quantified. Summary of recent research about fault diagnosis technology for Li-ion batteries is concluded with their cons and pros. The suggestions on novel fault diagnosis approach and remaining challenges are provided at the end of this paper.

  19. Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

    Directory of Open Access Journals (Sweden)

    Weigang Wen

    2015-01-01

    Full Text Available Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.

  20. Independent component analysis approach for fault diagnosis of condenser system in thermal power plant

    Institute of Scientific and Technical Information of China (English)

    Ajami Ali; Daneshvar Mahdi

    2014-01-01

    A statistical signal processing technique was proposed and verified as independent component analysis (ICA) for fault detection and diagnosis of industrial systems without exact and detailed model. Actually, the aim is to utilize system as a black box. The system studied is condenser system of one of MAPNA’s power plants. At first, principal component analysis (PCA) approach was applied to reduce the dimensionality of the real acquired data set and to identify the essential and useful ones. Then, the fault sources were diagnosed by ICA technique. The results show that ICA approach is valid and effective for faults detection and diagnosis even in noisy states, and it can distinguish main factors of abnormality among many diverse parts of a power plant’s condenser system. This selectivity problem is left unsolved in many plants, because the main factors often become unnoticed by fault expansion through other parts of the plants.

  1. Illuminating Northern California’s Active Faults

    Science.gov (United States)

    Prentice, Carol S.; Crosby, Christopher J.; Whitehill, Caroline S.; Arrowsmith, J. Ramon; Furlong, Kevin P.; Philips, David A.

    2009-01-01

    Newly acquired light detection and ranging (lidar) topographic data provide a powerful community resource for the study of landforms associated with the plate boundary faults of northern California (Figure 1). In the spring of 2007, GeoEarthScope, a component of the EarthScope Facility construction project funded by the U.S. National Science Foundation, acquired approximately 2000 square kilometers of airborne lidar topographic data along major active fault zones of northern California. These data are now freely available in point cloud (x, y, z coordinate data for every laser return), digital elevation model (DEM), and KMZ (zipped Keyhole Markup Language, for use in Google EarthTM and other similar software) formats through the GEON OpenTopography Portal (http://www.OpenTopography.org/data). Importantly, vegetation can be digitally removed from lidar data, producing high-resolution images (0.5- or 1.0-meter DEMs) of the ground surface beneath forested regions that reveal landforms typically obscured by vegetation canopy (Figure 2)

  2. Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks.

    Science.gov (United States)

    de Bruin, Tim; Verbert, Kim; Babuska, Robert

    2017-03-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from multiple track circuits in a geographic area, faults are diagnosed from their spatial and temporal dependences. A generative model is used to show that the LSTM network can learn these dependences directly from the data. The network correctly classifies 99.7% of the test input sequences, with no false positive fault detections. In addition, the t-Distributed Stochastic Neighbor Embedding (t-SNE) method is used to examine the resulting network, further showing that it has learned the relevant dependences in the data. Finally, we compare our LSTM network with a convolutional network trained on the same task. From this comparison, we conclude that the LSTM network architecture is better suited for the railway track circuit fault detection and identification tasks than the convolutional network.

  3. Control Surface Fault Diagnosis for Small Autonomous Aircraft

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2011-01-01

    Small unmanned aerial vehicles require a large degree of fault-tolerance in order to fulfil their duties in an satisfactory way, both with respect to economy and safety in operation. Small aerial vehicles are commonly constructed without much redundancy in hardware, primarily for reasons of cost...

  4. Fault Diagnosis of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method

    OpenAIRE

    Jinglong Chen; Yu Wang; Zhengjia He; Xiaodong Wang

    2015-01-01

    The demountable disk-drum aero-engine rotor is an important piece of equipment that greatly impacts the safe operation of aircraft. However, assembly looseness or crack fault has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and fault diagnosis technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured vibration data, is developed in this paper. Fir...

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

    KAUST Repository

    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.

  6. Active faulting in the Birjand region of NE Iran

    Science.gov (United States)

    Walker, R. T.; Khatib, M. M.

    2006-08-01

    We use satellite imagery and field observations to investigate the distribution of active faults around Birjand in eastern Iran to determine how the transition between conjugate zones of faulting can be accommodated by diffuse active faulting. In the south of the study area, right-lateral strike-slip faults of the Sistan Suture Zone end in thrusts which die away westward from the strike-slip faults. These thrust terminations appear to allow a northward change to E-W thrusting in central parts of the study area. The introduction of E-W thrusting is, in turn, likely to facilitate a change to E-W left-lateral faulting north of the study region. The relatively diffuse pattern of active faulting at Birjand relates to the regional transition between N-S and E-W strike-slip faulting in northeast Iran, which involves a change from nonrotational to rotational deformation. The change from N-S to E-W faulting is likely to result from the orientation of preexisting structures in Iran and western Afghanistan, which are roughly parallel to the active fault zones. The features described at Birjand also show the influence of preexisting structure on the location and style of active faulting at a local scale, with the position of individual faults apparently controlled by inherited geological weaknesses. Very few modern earthquakes have occurred in the region of Birjand and yet destructive events are known from the historical record. The large number of active faults mapped in this study pose a substantial seismic hazard to Birjand and surrounding regions.

  7. New procedure for gear fault detection and diagnosis using instantaneous angular speed

    Science.gov (United States)

    Li, Bing; Zhang, Xining; Wu, Jili

    2017-02-01

    Besides the extreme complexity of gear dynamics, the fault diagnosis results in terms of vibration signal are sometimes easily misled and even distorted by the interference of transmission channel or other components like bearings, bars. Recently, the research field of Instantaneous Angular Speed (IAS) has attracted significant attentions due to its own advantages over conventional vibration analysis. On the basis of IAS signal's advantages, this paper presents a new feature extraction method by combining the Empirical Mode Decomposition (EMD) and Autocorrelation Local Cepstrum (ALC) for fault diagnosis of sophisticated multistage gearbox. Firstly, as a pre-processing step, signal reconstruction is employed to address the oversampled issue caused by the high resolution of the angular sensor and the test speed. Then the adaptive EMD is used to acquire a number of Intrinsic Mode Functions (IMFs). Nevertheless, not all the IMFs are needed for the further analysis since different IMFs have different sensitivities to fault. Hence, the cosine similarity metric is introduced to select the most sensitive IMF. Even though, the sensitive IMF is still insufficient for the gear fault diagnosis due to the weakness of the fault component related to the gear fault. Therefore, as the final step, ALC is used for the purpose of signal de-noising and feature extraction. The effectiveness and robustness of the new approach has been validated experimentally on the basis of two gear test rigs with gears under different working conditions. Diagnosis results show that the new approach is capable of effectively handling the gear fault diagnosis i.e., the highlighted quefrency and its rahmonics corresponding to the rotary period and its multiple are displayed clearly in the cepstrum record of the proposed method.

  8. Wavelet neural network and its application in fault diagnosis of rolling bearing

    Science.gov (United States)

    Wang, Guo-Feng; Wang, Tai-Yong

    2005-12-01

    In order to realize diagnosis of rolling bearing of rotating machines, the wavelet neural network was proposed. This kind of artificial neural network takes wavelet function as neuron of hidden layer so as to realize nonlinear mapping between fault and symptoms. A algorithm based on minimum mean square error was given to obtain the weight value of network, dilation and translation parameter of wavelet function. To testify the correctness of wavelet neural network, it was adopted in diagnosing the fault type and location of rolling bearing. The final result shows that it can recognize the fault of outer race, inner race and roller accurately.

  9. Actuator fault diagnosis of time-delay systems based on adaptive observer

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    A novel approach for the actuator fault diagnosis of time-delay systems is presented by using an adaptive observer technique. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. An adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for general system to improve robustness. The selection of the threshold for fault detection is also discussed. Finally, a numerical example is given to illustrate the efficiency of the proposed method.

  10. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

    Science.gov (United States)

    Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

    2016-06-17

    Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

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

    Directory of Open Access Journals (Sweden)

    Jian Dang

    2016-01-01

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

  12. The Research of Blind Source Separation (BSS)in Machinery Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Blind source separation (BSS) technology is very use ful in many fields, such as communication, radar and so on. Because of the advantage of BSS that it can separate multi-sources even not knowing the mix-coefficient and the probability distribution, it can also be used in fault diagnosis. In this paper, we first use the BSS to deal with the sound from the machinery in fault diagnosis. We make a simulation of two sound sources and four sensors to test the result. Each source is a narrow-band source, which is composed of several sine waves.The result shows that the two sources can be well separated from the mixed signals. So we can draw a conclusion that BSS can inprove the technology of sound fault diagnosis, especially in rotating machinery.``

  13. Geared induction motor fault diagnosis by current, noise and vibration considering measurement environment

    Directory of Open Access Journals (Sweden)

    Ki-Seok Kim

    2017-01-01

    Full Text Available Lots of motors have been being used in industry. Therefore many studies have been carried out about the failure diagnosis of motors. In this paper, a diagnosis of gear fault connected to a motor shaft is studied. The fault diagnosis is executed through the comparison of normal gear and abnormal gear. In the abnormal gearbox, a tooth of the intermediate gear is damaged. The measured FFT data are compared with the normal data and analyzed for q-axis current, noise and vibration. Fault gear was found by comparing the FFT with normal FFT. From these, the difference between the normal and abnormal states can be seen by the frequency characteristic analysis for the current as well as noise and vibration.

  14. SDG-Based HAZOP and Fault Diagnosis Analysis to the Inversion of Synthetic Ammonia

    Institute of Scientific and Technical Information of China (English)

    L(U) Ning; WANG Xiong

    2007-01-01

    This paper presents some practical applications of signed directed graphs(SDGs)to computeraided hazard and operability study (HAZOP) and fault diagnosis, based on an analysis of the SDG theory.The SDG is modeled for the inversion of synthetic ammonia, which is highly dangerous in process industry,and HAZOP and fault diagnosis based on the SDG model are presented.A new reasoning method,whereby inverse inference is combined with forward inference,is presented to implement SDG fault diagnosis based on a breadth-first algorithm with consistency rules. Compared with conventional inference engines, this new method can better avoid qualitative spuriousness and combination explosion, and can deal with unobservable nodes in SDGs more effectively. Experimental results show the validity and advantages of the new SDG method.

  15. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    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...

  16. Active Tectonics Revealed by River Profiles along the Puqu Fault

    OpenAIRE

    Ping Lu,; Yu Shang

    2015-01-01

    The Puqu Fault is situated in Southern Tibet. It is influenced by the eastward extrusion of Northern Tibet and carries the clockwise rotation followed by the southward extrusion. Thus, the Puqu Fault is bounded by the principal dynamic zones and the tectonic evolution remains active alongside. This study intends to understand the tectonic activity in the Puqu Fault Region from the river profiles obtained from the remotely sensed satellite imagery. A medium resolution Digital Elevation Model (...

  17. A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

    Institute of Scientific and Technical Information of China (English)

    ANG Xue-ye

    2007-01-01

    The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given . It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.

  18. Elementwise Business Diagnosis of Enterprise Activity

    Directory of Open Access Journals (Sweden)

    Skrynkovskyy Ruslan M.

    2016-02-01

    Full Text Available The article presents methodological and indicator apparatus for elementwise business diagnosis of enterprise activity directed at achieving such elementwise diagnostic objectives: diagnosis of return on assets; diagnosis of return on equity capital; diagnosis of production profitability; diagnosis of gross profit margin of product sales; diagnosis of operating margin of product sales; diagnosis of net margin of product sales; diagnosis of absolute liquidity; diagnosis of instant liquidity; diagnosis of overall liquidity; diagnosis of coverage; diagnosis of financial independence; diagnosis of equity capital maneuverability; diagnosis of financial leverage; diagnosis of the long-term investment structure; diagnosis of accounts payable turnover; diagnosis of the accounts payable repayment period, diagnosis of receivables turnover; diagnosis of receivables repayment period; diagnosis of assets turnover; diagnosis of inventories turnover; diagnosis of the inventories turnover period; diagnosis of equity capital turnover; diagnosis of fixed assets turnover (return on assets; diagnosis of capital coefficient; diagnosis of the ratio of output value to the materials cost; diagnosis of material consumption; diagnosis of the total production cost; diagnosis of enterprise market share; diagnosis of fixed assets wear; diagnosis of fixed assets renewal; diagnosis of fixed assets retirement; performance diagnosis; diagnosis of labor intensity, diagnosis of the capital-labour ratio; diagnosis of efficiency; diagnosis of conducting the business; diagnosis of business relations; diagnosis of administrative-legal relations; diagnosis of knowledge management. The elementwise diagnostic objectives of the enterprise system of diagnostic objectives are aimed at a narrow highly detailed diagnostics of individual indicators of the enterprise activity, i.e. the evaluation of specific analytical indicators,monitoring (research of their dynamics, comparison of the planned

  19. Fault tolerant control for uncertain systems with parametric faults

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2006-01-01

    A fault tolerant control (FTC) architecture based on active fault diagnosis (AFD) and the YJBK (Youla, Jarb, Bongiorno and Kucera)parameterization is applied in this paper. Based on the FTC architecture, fault tolerant control of uncertain systems with slowly varying parametric faults...

  20. Determination of minimum sample size for fault diagnosis of automobile hydraulic brake system using power analysis

    Directory of Open Access Journals (Sweden)

    V. Indira

    2015-03-01

    Full Text Available Hydraulic brake in automobile engineering is considered to be one of the important components. Condition monitoring and fault diagnosis of such a component is very essential for safety of passengers, vehicles and to minimize the unexpected maintenance time. Vibration based machine learning approach for condition monitoring of hydraulic brake system is gaining momentum. Training and testing the classifier are two important activities in the process of feature classification. This study proposes a systematic statistical method called power analysis to find the minimum number of samples required to train the classifier with statistical stability so as to get good classification accuracy. Descriptive statistical features have been used and the more contributing features have been selected by using C4.5 decision tree algorithm. The results of power analysis have also been verified using a decision tree algorithm namely, C4.5.

  1. Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears

    Science.gov (United States)

    Guoji, Shen; McLaughlin, Stephen; Yongcheng, Xu; White, Paul

    2014-02-01

    Condition monitoring and fault diagnosis is an important issue for gearbox maintenance and safety. The critical process involved in such activities is to extract reliable features representative of the condition of the gears or gearbox. In this paper a framework is presented for the application of bispectrum to the analysis of gearbox vibration. The bispectrum of a composite signal consisting of multiple periodic components has peaks at the bifrequencies that correspond to closely related components which can be produced by any nonlinearity. As a result, biphase verification is necessary to decrease false-alarming for any bispectrum-based method. A model based on modulated signals is adopted to reveal the bispectrum characteristics for the vibration of a faulty gear, and the corresponding amplitude and phase of the bispectrum expression are deduced. Therefore, a diagnostic approach based on the theoretical result is derived and verified by the analysis of a set of vibration signals from a helicopter gearbox.

  2. A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

    Directory of Open Access Journals (Sweden)

    Guojiang Xiong

    2013-01-01

    Full Text Available Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.

  3. Investigation of the synthetic experiment system of machine equipment fault diagnosis

    Science.gov (United States)

    Liu, Hongyu; Xu, Zening; Yu, Xiaoguang

    2008-12-01

    The invention and manufacturing of the synthetic experiment system of machine equipment fault diagnosis filled in the blank of this kind of experiment equipment in China and obtained national practical new type patent. By the motor speed regulation system, machine equipment fault imitation system, measuring and monitoring system and analysis and diagnosis system of the synthetic experiment system, students can regulate motor speed arbitrarily, imitate multi-kinds of machine equipment parts fault, collect the signals of acceleration, speed, displacement, force and temperature and make multi-kinds of time field, frequency field and figure analysis. The application of the synthetic experiment system in our university's teaching practice has obtained good effect on fostering professional eligibility in measuring, monitoring and fault diagnosis of machine equipment. The synthetic experiment system has the advantages of short training time, quick desirable result and low test cost etc. It suits for spreading in university extraordinarily. If the systematic software was installed in portable computer, user can fulfill measuring, monitoring, signal processing and fault diagnosis on multi-kinds of field machine equipment conveniently. Its market foreground is very good.

  4. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    Science.gov (United States)

    Li, Chuan; Sanchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

    2016-08-01

    Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes.

  5. Vibration Sensor Data Denoising Using a Time-Frequency Manifold for Machinery Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Qingbo He

    2013-12-01

    Full Text Available Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods.

  6. Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis.

    Science.gov (United States)

    He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

    2013-12-27

    Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods.

  7. Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD and fuzzy c-means (FCM clustering method based on Local Mean Decomposition (LMD and time segmentation energy entropy (TSEE is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs. Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples.

  8. Elementwise Business Diagnosis of Enterprise Activity

    OpenAIRE

    Skrynkovskyy Ruslan M.

    2016-01-01

    The article presents methodological and indicator apparatus for elementwise business diagnosis of enterprise activity directed at achieving such elementwise diagnostic objectives: diagnosis of return on assets; diagnosis of return on equity capital; diagnosis of production profitability; diagnosis of gross profit margin of product sales; diagnosis of operating margin of product sales; diagnosis of net margin of product sales; diagnosis of absolute liquidity; diagnosis of ins...

  9. Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines (MPSVMs) using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.

  10. Investigation of candidate data structures and search algorithms to support a knowledge based fault diagnosis system

    Science.gov (United States)

    Bosworth, Edward L., Jr.

    1987-01-01

    The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.

  11. Process Monitoring and Fault Diagnosis for Shell Rolling Production of Seamless Tube

    Directory of Open Access Journals (Sweden)

    Dong Xiao

    2015-01-01

    Full Text Available Continuous rolling production process of seamless tube has many characteristics, including multiperiod and strong nonlinearity, and quickly changing dynamic characteristics. It is difficult to build its mechanism model. In this paper we divide production data into several subperiods by K-means clustering algorithm combined with production process; then we establish a continuous rolling production monitoring and fault diagnosis model based on multistage MPCA method. Simulation experiments show that the rolling production process monitoring and fault diagnosis model based on multistage MPCA method is effective, and it has a good real-time performance, high reliability, and precision.

  12. Machinery fault diagnosis expert system based on case-based reasoning

    Institute of Scientific and Technical Information of China (English)

    LI Wen-hong; SUN Shao-wen; ZHANG Qi

    2007-01-01

    A mechinery fault diagnosis expert system based on case-based reasoning (CBR) technology was established. The process of the CBR fault diagnosis is analyzed from three main aspects: expression and memory, retrieving and matching, and modification and maintenance of a case. The results indicate that the CBR method is flexible and simple to implement, and it has strong self-studying ability. Using a large enough number of case reasoning sets, it can accumulate the experience of problem solving, avoid the difficulty of knowledge acquisition, shorten the course of solving problems, improve efficiency of reasoning, and save the time of developing.

  13. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    Directory of Open Access Journals (Sweden)

    Paola Costamagna

    2016-08-01

    Full Text Available The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  14. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants.

    Science.gov (United States)

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-08-22

    The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  15. Artificial neural network application for space station power system fault diagnosis

    Science.gov (United States)

    Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.

    1995-01-01

    This study presents a methodology for fault diagnosis using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU were used to evaluate the ANN's fault diagnosis capabilities. This on-going study uses EMTP models of the components (distribution lines, SPDU, TPDU, loads) and power sources (DDCU) of Space Station Alpha's electrical Power Distribution System as a basis for the ANN fault diagnostic tool. The results from the two studies are contrasted. In the event of a major fault, ground controllers need the ability to identify the type of fault, isolate the fault to the orbital replaceable unit level and provide the necessary information for the power management expert system to optimally determine a degraded-mode load schedule. To accomplish these goals, the electrical power distribution system's architecture can be subdivided into three major classes: DC-DC converter to loads, DC Switching Unit (DCSU) to Main bus Switching Unit (MBSU), and Power Sources to DCSU. Each class which has its own electrical characteristics and operations, requires a unique fault analysis philosophy. This study identifies these philosophies as Riddles 1, 2 and 3 respectively. The results of the on-going study addresses Riddle-1. It is concluded in this study that the combination of the EMTP models of the DDCU, distribution cables and electrical loads yields a more accurate model of the behavior and in addition yielded more accurate fault diagnosis using ANN versus the results obtained with the SPICE models.

  16. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

    Science.gov (United States)

    Zhen, D.; Wang, T.; Gu, F.; Ball, A. D.

    2013-01-01

    Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time

  17. Active Fault Detection Based on a Statistical Test

    DEFF Research Database (Denmark)

    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. Physically-based modeling of speed sensors for fault diagnosis and fault tolerant control in wind turbines

    Science.gov (United States)

    Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

    2014-12-01

    In this paper, a generic physically-based modeling framework for encoder type speed sensors is derived. The consideration takes into account the nominal fault-free and two most relevant fault cases. The advantage of this approach is a reconstruction of the output waveforms in dependence of the internal physical parameter changes which enables a more accurate diagnosis and identification of faulty incremental encoders i.a. in wind turbines. The objectives are to describe the effect of the tilt and eccentric of the encoder disk on the digital output signals and the influence of the accuracy of the speed measurement in wind turbines. Simulation results show the applicability and effectiveness of the proposed approach.

  19. Functional Modelling for Fault Diagnosis and its application for NPP

    DEFF Research Database (Denmark)

    Lind, Morten; Zhang, Xinxin

    2014-01-01

    The paper presents functional modelling and its application for diagnosis in nuclear power plants.Functional modelling is defined and it is relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed....... The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool the MFM Suite. MFM applications in nuclear power systems are described by two examples a PWR and a FBRreactor. The PWR example show how MFM can be used to model and reason about...

  20. Active Fault Exploration and Seismic Hazard Assessment in Fuzhou City

    Institute of Scientific and Technical Information of China (English)

    Zhu Jinfang; Han Zhujun; Huang Zonglin; Xu Xiwei; Zheng Rongzhang; Fang Shengmin; Bai Denghai; Wang Guangcai; Min Wei; Wen Xueze

    2005-01-01

    It has been proven by a number of earthquake case studies that an active fault-induced earthquake beneath a city can be devastating. It is an urgent issue for seismic hazard reduction to explore the distribution of active faults beneath the urban area and identify the seismic source and the risks underneath. As a pilot project of active fault exploration in China, the project, entitled "Active fault exploration and seismic hazard assessment in Fuzhou City",started in early 2001 and passed the check before acceptance of China Earthquake Administration in August 2004. The project was aimed to solve a series of scientific issues such as fault location, dating, movement nature, deep settings, seismic risk and hazard,preparedness of earthquake prevention and disaster reduction, and etc. by means of exploration and assessment of active faults by stages, i.e., the preliminary survey and identification of active faults in target area, the exploration of deep seismotectonic settings, the risk evaluation of active seismogenic faults, the construction of geographic information system of active faults, and so on. A lot of exploration methods were employed in the project such as the detection of absorbed mercury, free mercury and radon in soil, the geological radar,multi-channel DC electrical method, tsansient electromagnetic method, shallow seismic refraction and reflection, effect contrast of explored sources, and various sounding experiments, to establish the buried Quaternary standard section of the Fuzhou basin. By summing up, the above explorations and experiments have achieved the following results and conclusions:(1) The results of the synthetic pilot project of active fault exploration in Fuzhou City demonstrate that, on the basis of sufficient collection, sorting out and analysis of geological,geophysical and borehole data, the best method for active fault exploration (location) and seismic risk assessnent (dating and characterizing) in urban area is the combination

  1. A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems

    Directory of Open Access Journals (Sweden)

    Davide Bresolin

    2011-06-01

    Full Text Available Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem for hybrid systems from a game-theoretical point of view. A hybrid system is a system mixing continuous and discrete behaviours that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and we define a Fault Diagnosis Game on them, using two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behaviour of the system and announces whether a fault has occurred or not. Existence of a winning strategy for the diagnoser implies that faults can be detected correctly, while computing such a winning strategy corresponds to implement a diagnoser for the system. We will show how to determine the existence of a winning strategy, and how to compute it, for some decidable classes of hybrid automata like o-minimal hybrid automata.

  2. Fault Diagnosis of Train Axle Box Bearing Based on Multifeature Parameters

    Directory of Open Access Journals (Sweden)

    Xiaofeng Li

    2015-01-01

    Full Text Available Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%.

  3. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    Science.gov (United States)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran

    2016-05-01

    Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.

  4. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

    Science.gov (United States)

    He, Shuilong; Chen, Jinglong; Zhou, Zitong; Zi, Yanyang; Wang, Yanxue; Wang, Xiaodong

    2016-08-01

    Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

  5. Spacing and strength of active continental strike-slip faults

    Science.gov (United States)

    Zuza, Andrew V.; Yin, An; Lin, Jessica; Sun, Ming

    2017-01-01

    Parallel and evenly-spaced active strike-slip faults occur widely in nature across diverse tectonic settings. Despite their common existence, the fundamental question of what controls fault spacing remains unanswered. Here we present a mechanical model for the generation of parallel strike-slip faults that relates fault spacing to the following parameters: (1) brittle-crust thickness, (2) fault strength, (3) crustal strength, and (4) crustal stress state. Scaled analogue experiments using dry sand, dry crushed walnut shells, and viscous putty were employed to test the key assumptions of our quantitative model. The physical models demonstrate that fault spacing (S) is linearly proportional to brittle-layer thickness (h), both in experiments with only brittle materials and in two-layer trials involving dry sand overlying viscous putty. The S / h slope in the two-layer sand-putty experiments may be controlled by the (1) rheological/geometric properties of the viscous layer, (2) effects of distributed basal loading caused by the viscous shear of the putty layer, and/or (3) frictional interaction at the sand-putty interface (i.e., coupling between the viscous and brittle layers). We tentatively suggest that this third effect exerts the strongest control on fault spacing in the analogue experiments. By applying our quantitative model to crustal-scale strike-slip faults using fault spacing and the seismogenic-zone thickness obtained from high-resolution earthquake-location data, we estimate absolute fault friction of active strike-slip faults in Asia and along the San Andreas fault system in California. We show that the average friction coefficient of strike-slip faults in the India-Asia collisional orogen is lower than that of faults in the San Andreas fault system. Weaker faults explain why deformation penetrates >3500 km into Asia from the Himalaya and why the interior of Asia is prone to large (M > 7.0) devastating earthquakes along major intra-continental strike

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

    Directory of Open Access Journals (Sweden)

    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.

  7. A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

    Science.gov (United States)

    Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang

    2016-09-01

    A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

  8. A fault diagnosis based reconfigurable longitudinal control system for managing loss of air data sensors for a civil aircraft

    OpenAIRE

    Varga, Andreas; Ossmann, Daniel; Joos, Hans-Dieter

    2014-01-01

    An integrated fault diagnosis based fault tolerant longitudinal control system architecture is proposed for civil aircraft which can accommodate partial or total losses of angle of attack and/or calibrated airspeed sensors. A triplex sensor redundancy is assumed for the normal operation of the aircraft using a gain scheduled longitudinal normal control law. The fault isolation functionality is provided by a bank of 6 fault detection filters, which individually monitor each of the 6 sensors us...

  9. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid...... on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme....

  10. Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

    Institute of Scientific and Technical Information of China (English)

    TANG Xian-lun; ZHUANG Ling; QIU Guo-qing; CAI Jun

    2009-01-01

    The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.

  11. Fault detection and diagnosis in nonlinear systems a differential and algebraic viewpoint

    CERN Document Server

    Martinez-Guerra, Rafael

    2014-01-01

    The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant an...

  12. State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    MRUGALSKI, M.

    2012-08-01

    Full Text Available Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach.

  13. Detection and Diagnosis of Gear Fault By the Single Gear Tooth Analysis Technique

    Institute of Scientific and Technical Information of China (English)

    MENG Tao; LIAO Ming-fu

    2003-01-01

    This paper presents a procedure of single gear tooth analysis for early detection and diagnosis of gear faults. The objective of this procedure is to develop a method for more sensitive detection of the incipient faults and locating the faults in the gear. The main idea of the single gear tooth analysis is that the vibration signals collected with a high sampling rate are divided into a number of segments with the same time interval. The number of signal segments is equal to that of the gear teeth. The analysis of individual segments reveals more sensitively the changes of the vibration signals in both time and frequency domain caused by gear faults. In addition, the location of a failed tooth can be indicated in terms of the position of the segment that deviates from the normal segments. An experimental investigation verified the advantages of the single gear tooth analysis.

  14. Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways

    Directory of Open Access Journals (Sweden)

    Keting Hu

    2016-03-01

    Full Text Available In this paper, a diagnosis plan is proposed to settle the detection and isolation problem of open switch faults in high-speed railway traction system traction inverters. Five entropy forms are discussed and compared with the traditional fault detection methods, namely, discrete wavelet transform and discrete wavelet packet transform. The traditional fault detection methods cannot efficiently detect the open switch faults in traction inverters because of the low resolution or the sudden change of the current. The performances of Wavelet Packet Energy Shannon Entropy (WPESE, Wavelet Packet Energy Tsallis Entropy (WPETE with different non-extensive parameters, Wavelet Packet Energy Shannon Entropy with a specific sub-band (WPESE3,6, Empirical Mode Decomposition Shannon Entropy (EMDESE, and Empirical Mode Decomposition Tsallis Entropy (EMDETE with non-extensive parameters in detecting the open switch fault are evaluated by the evaluation parameter. Comparison experiments are carried out to select the best entropy form for the traction inverter open switch fault detection. In addition, the DC component is adopted to isolate the failure Isolated Gate Bipolar Transistor (IGBT. The simulation experiments show that the proposed plan can diagnose single and simultaneous open switch faults correctly and timely.

  15. Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group

    Directory of Open Access Journals (Sweden)

    Hua Zhang

    2016-05-01

    Full Text Available In a parallel-connected battery group (PCBG, capacity degradation is usually caused by the inconsistency between a faulty cell and other normal cells, and the inconsistency occurs due to two potential causes: an aging inconsistency fault or a loose contacting fault. In this paper, a novel method is proposed to perform online and real-time capacity fault diagnosis for PCBGs. Firstly, based on the analysis of parameter variation characteristics of a PCBG with different fault causes, it is found that PCBG resistance can be taken as an indicator for both seeking the faulty PCBG and distinguishing the fault causes. On one hand, the faulty PCBG can be identified by comparing the PCBG resistance among PCBGs; on the other hand, two fault causes can be distinguished by comparing the variance of the PCBG resistances. Furthermore, for online applications, a novel recursive-least-squares algorithm with restricted memory and constraint (RLSRMC, in which the constraint is added to eliminate the “imaginary number” phenomena of parameters, is developed and used in PCBG resistance identification. Lastly, fault simulation and validation results demonstrate that the proposed methods have good accuracy and reliability.

  16. Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network

    Institute of Scientific and Technical Information of China (English)

    Ali UYSAL; Raif BAYIR

    2013-01-01

    The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of de-tecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

  17. A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

    Directory of Open Access Journals (Sweden)

    Zhang Chaolong

    2015-06-01

    Full Text Available Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

  18. Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis

    Science.gov (United States)

    Gu, Xiaohui; Yang, Shaopu; Liu, Yongqiang; Hao, Rujiang

    2016-12-01

    Envelope analysis is one of the most useful methods in localized fault diagnosis of rolling element bearings. However, there is a challenge in selecting the optimal resonance band. In this paper, a novel method based on kurtogram and frequency domain correlated kurtosis is proposed. To obtain the correct relationship between the node and frequency band in wavelet packet transform, a vital process named frequency ordering is conducted to solve the frequency folding problem due to down sampling. Correlated kurtosis of envelope spectrum instead of correlated kurtosis of envelope signal or kurtosis of envelope spectrum is utilized to generate the kurtogram, in which the maximum value can indicate the optimal band for envelope analysis. Several cases of experimental bearing fault signals are used to evaluate the immunity of the proposed method to strong noise interference. The improved performance has also been compared with two previous developed methods. The results demonstrate the effectiveness and robustness of the method in fault diagnosis of rolling element bearings.

  19. Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

    Science.gov (United States)

    Dwyer, Matthew B.; Purandare, Rahul; Person, Suzette

    2010-01-01

    Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, fault diagnosis, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of fault diagnosis.

  20. Distribution Network Fault Diagnosis Method Based on Granular Computing-BP

    Directory of Open Access Journals (Sweden)

    CHEN Zhong-xiao

    2013-01-01

    Full Text Available To deal with the complexity and uncertainty of distribution network fault information, a method of fault diagnosis based on granular computing and BP is proposed. This method uses attribute reduction advantages of granular computing theory and self-learning and knowledge acquisition ability of BP neural network. It put granular computing theory as the front-end processor of the BP neural network, namely simplify primitive information making use of granular computing reduction, and according to the concepts of relative granularity and significance of attributes based on binary granular computing are proposed to select input of BP, thereby reducing solving scale, and then construct neural network based on the minimum attribute sets, using BP neural network to model and parameter identify, reduce the BP study training time, improve the accuracy of the fault diagnosis. The distribution network example verifies the rationality and effectiveness of the proposed method.

  1. Performance of wavelet analysis and neural network for detection and diagnosis of rotating machine fault

    Science.gov (United States)

    Kang, Shanlin; Kang, Yuzhe; Chen, Jingwei

    2008-10-01

    A novel approach combining wavelet transform with neural network is proposed for vibration fault diagnosis of turbo-generator set in power system. The multi-resolution analysis technology is used to acquire the feature vectors which are applied to train and test the neural network. Feature extraction involves preliminary processing of measurements to obtain suitable parameters which reveal weather an interesting pattern is emerging. The feature extraction technique is needed for preliminary processing of recorded time-series vibrations over a long period of time to obtain suitable parameters. The neural network parameters are determined by means of the recursive orthogonal least squares algorithm. In network training procedure, much simulation and practical samples are utilized to verify and test the network performance. And according to the output result, the fault pattern can be recognized. The actual applications show that the method is effective for detection and diagnosis of rotating machine fault and the experiment result is correct.

  2. Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis

    Institute of Scientific and Technical Information of China (English)

    Kechang FU; Liankui DAI; Tiejun WU; Ming ZHU

    2009-01-01

    A new sensor fault diagnosis method based on structured kernel principal component analysis (KPCA) is proposed for nonlinear processes.By performing KPCA on subsets of variables,a set of structured residuals,i.e.,scaled powers of KPCA,can be obtained in the same way as partial PCA.The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis,according to a properly designed incidence matrix.Sensor fault sensitivity and critical sensitivity are defined,based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA.The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor (CSTR) process.

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

    Directory of Open Access Journals (Sweden)

    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.

  4. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    Science.gov (United States)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

  5. STUDY ON NATURAL LANGUAGE INTERFACE OF NETWORK FAULT DIAGNOSIS EXPERT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    Liu Peiqi; Li Zengzhi; Zhao Yinliang

    2006-01-01

    The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and embarrasses the enthusiasm of using expert system. Combining with the study on the expert system of network fault diagnosis, the natural language interface of the expert system has been discussed in this article. This interface can understand and generate Chinese sentences. Using this interface, the user and field experts can use the expert system to diagnose the fault of network conveniently. In the article, first, the extended production rule has been proposed. Then the methods of Chinese sentence generation from conceptual graphs and the model of expert system are introduced in detail. Using this model, the network fault diagnosis expert system and its natural language interface have been developed with Prolog.

  6. Fault Diagnosis in Transformer Based on Weighted Degree of Grey Slope Incidence of Optimized Entropy

    Directory of Open Access Journals (Sweden)

    Zhang Anping

    2016-01-01

    Full Text Available Dissolved gas analysis (DGA is an important method to find the hidden or incipient insulation faults of oil-immersed power transformer. However, code deficiency exists in the gas ratio methods specified by the IEC standard and complexity of fault diagnosis for power transformer. Hence a new model based on optimized weighted degree of grey slope incidence was put forward. Firstly, the entropy weight is used to determine objective weight of indices; then the model fault types are obtained by weighted degree of grey slope incidence. The combination of entropy weight with grey slope incidence analysis can fully utilize over all information of DGA and give full play to the superiority of grey slope incidence, which overcomes shortcomings of original grey slope incidence analysis. The experimental results also demonstrate that the improved method has higher accuracy compared with three-ratio method and general grey slope incidence analysis method. The diagnosis accuracy is 92.8%.

  7. Faulting processes in active faults - Evidences from TCDP and SAFOD drill core samples

    Energy Technology Data Exchange (ETDEWEB)

    Janssen, C.; Wirth, R.; Wenk, H. -R.; Morales, L.; Naumann, R.; Kienast, M.; Song, S. -R.; Dresen, G. [UCB; (GFZ); (NTU)

    2014-08-20

    The microstructures, mineralogy and chemistry of representative samples collected from the cores of the San Andreas Fault drill hole (SAFOD) and the Taiwan Chelungpu-Fault Drilling project (TCDP) have been studied using optical microscopy, TEM, SEM, XRD and XRF analyses. SAFOD samples provide a transect across undeformed host rock, the fault damage zone and currently active deforming zones of the San Andreas Fault. TCDP samples are retrieved from the principal slip zone (PSZ) and from the surrounding damage zone of the Chelungpu Fault. Substantial differences exist in the clay mineralogy of SAFOD and TCDP fault gouge samples. Amorphous material has been observed in SAFOD as well as TCDP samples. In line with previous publications, we propose that melt, observed in TCDP black gouge samples, was produced by seismic slip (melt origin) whereas amorphous material in SAFOD samples was formed by comminution of grains (crush origin) rather than by melting. Dauphiné twins in quartz grains of SAFOD and TCDP samples may indicate high seismic stress. The differences in the crystallographic preferred orientation of calcite between SAFOD and TCDP samples are significant. Microstructures resulting from dissolution–precipitation processes were observed in both faults but are more frequently found in SAFOD samples than in TCDP fault rocks. As already described for many other fault zones clay-gouge fabrics are quite weak in SAFOD and TCDP samples. Clay-clast aggregates (CCAs), proposed to indicate frictional heating and thermal pressurization, occur in material taken from the PSZ of the Chelungpu Fault, as well as within and outside of the SAFOD deforming zones, indicating that these microstructures were formed over a wide range of slip rates.

  8. Diagnosis of inverter switch open circuit faults based on neutral point voltage signal analysis

    Directory of Open Access Journals (Sweden)

    Liwei GUO

    Full Text Available Using the current signal to diagnose inverter faults information is apt to be affected by the load, noise and other factors; besides, it requires long diagnosis period with special algorithms and the diagnosis result is easily to be incorrect with no-load or light-load. Focusing on this issue, the logical analysis method is proposed for correlation logical analysis of leg neutral-point voltage and pulse signal to realize the diagnosis of the open circuit faults of inverter switches. The logical expressions of output signals of inverter power tube open-circuit faults is put forward and interrelated hardware circuit design is also elaborated. Delaying the rising edge of inverter power tube's pulse signal can effectively avoid the diagnosis error caused by the power tube's switching on/off. The experiment results show that the method can effectively diagnose the open-circuit faults of single-phase single power tube inverter in real-time and the hardware circuit cost is low, which shows it is effective and feasible.

  9. Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

    Directory of Open Access Journals (Sweden)

    Rusmir Bajric

    2016-01-01

    Full Text Available Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

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

    DEFF Research Database (Denmark)

    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...

  11. Realization of Fault Diagnosis for ATS Based on Fault Tree Analysis%ATS故障树法故障诊断功能的实现

    Institute of Scientific and Technical Information of China (English)

    任献彬; 姜志森

    2013-01-01

    In absence of the transcendental experience of fault diagnosis,fault tree is an effective method which can be easily realized in engineering.With analyzing and inducing the association relationship between test items and SRUs,the expressing method of fault diagnosis information in fault tree database is obtained.The structure of fault tree,the data format of database,and fault diagnosis reasoning procedure are proposed,and the fault diagnosis system for ATS is designed.In this method,fault tree database can be designed easily,fault diagnosis procedure can be expressed definitely.This method has been applied in two types of ATS,both fault isolation rate and false alarm rate all meet the system needs.%当缺乏故障诊断先验知识时,故障树法是工程上易于实现的一种有效的故障诊断方法.通过分析、归纳测试项目与SRU的关联关系,得出了故障树模型中故障诊断知识的表达方式.以Access数据库为基础,提出了故障树的结构、数据组织形式及故障诊断的推理方法,并设计了适用于自动测试系统的故障诊断系统.该方法具有故障诊断推理过程表达明确、树模型易于建立等优点,已应用到两型机载电子设备的故障诊断中,故障隔离率和虚警率都达到了设计要求.

  12. Faults

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Through the study of faults and their effects, much can be learned about the size and recurrence intervals of earthquakes. Faults also teach us about crustal...

  13. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-11-01

    Full Text Available Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD and multi-layer classifier (MLC is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs. The IMF matrix is divided into submatrices to compute the local singular values (LSV. The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs and a support vector machine (SVM is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods.

  14. NEW FEATURE SELECTION METHOD IN MACHINE FAULT DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    Wang Xinfeng; Qiu Jing; Liu Guanjun

    2005-01-01

    Aiming to deficiency of the filter and wrapper feature selection methods, a new method based on composite method of filter and wrapper method is proposed. First the method filters original features to form a feature subset which can meet classification correctness rate, then applies wrapper feature selection method select optimal feature subset. A successful technique for solving optimization problems is given by genetic algorithm (GA). GA is applied to the problem of optimal feature selection. The composite method saves computing time several times of the wrapper method with holding the classification accuracy in data simulation and experiment on bearing fault feature selection. So this method possesses excellent optimization property, can save more selection time, and has the characteristics of high accuracy and high efficiency.

  15. Improving Diagnosability of Hybrid Systems through Active Diagnosis

    Data.gov (United States)

    National Aeronautics and Space Administration — Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete...

  16. Insurance Applications of Active Fault Maps Showing Epistemic Uncertainty

    Science.gov (United States)

    Woo, G.

    2005-12-01

    Insurance loss modeling for earthquakes utilizes available maps of active faulting produced by geoscientists. All such maps are subject to uncertainty, arising from lack of knowledge of fault geometry and rupture history. Field work to undertake geological fault investigations drains human and monetary resources, and this inevitably limits the resolution of fault parameters. Some areas are more accessible than others; some may be of greater social or economic importance than others; some areas may be investigated more rapidly or diligently than others; or funding restrictions may have curtailed the extent of the fault mapping program. In contrast with the aleatory uncertainty associated with the inherent variability in the dynamics of earthquake fault rupture, uncertainty associated with lack of knowledge of fault geometry and rupture history is epistemic. The extent of this epistemic uncertainty may vary substantially from one regional or national fault map to another. However aware the local cartographer may be, this uncertainty is generally not conveyed in detail to the international map user. For example, an area may be left blank for a variety of reasons, ranging from lack of sufficient investigation of a fault to lack of convincing evidence of activity. Epistemic uncertainty in fault parameters is of concern in any probabilistic assessment of seismic hazard, not least in insurance earthquake risk applications. A logic-tree framework is appropriate for incorporating epistemic uncertainty. Some insurance contracts cover specific high-value properties or transport infrastructure, and therefore are extremely sensitive to the geometry of active faulting. Alternative Risk Transfer (ART) to the capital markets may also be considered. In order for such insurance or ART contracts to be properly priced, uncertainty should be taken into account. Accordingly, an estimate is needed for the likelihood of surface rupture capable of causing severe damage. Especially where a

  17. A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongliang Lv

    2016-01-01

    Full Text Available A novel fault diagnosis method based on variational mode decomposition (VMD and multikernel support vector machine (MKSVM optimized by Immune Genetic Algorithm (IGA is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

  18. Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model

    Institute of Scientific and Technical Information of China (English)

    YAO LiNa; WANG Hong

    2008-01-01

    The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the ra-tional square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Conver-gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demon-strate the effectiveness of the proposed algorithm.

  19. Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

    Science.gov (United States)

    Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

    1996-01-01

    A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

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

    Directory of Open Access Journals (Sweden)

    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.

  1. A New Method of PV Array Faults Diagnosis in Smart Grid

    Directory of Open Access Journals (Sweden)

    Ze Cheng

    2014-01-01

    Full Text Available A new fault diagnosis method is proposed for PV arrays with SP connection in this study, the advantages of which are that it would minimize the number of sensors needed and that the accuracy and anti-interference ability are improved with the introduction of fuzzy group decision-making theory. We considered five “decision makers” contributing to the diagnosis of PV array faults, including voltage, current, environmental temperature, panel temperature, and solar illumination. The accuracy and reliability of the proposed method were verified experimentally, and the possible factors contributing to diagnosis deviation were analyzed, based on which solutions were suggested to reduce or eliminate errors in aspects of hardware and software.

  2. Techniques for Surveying Urban Active Faults by Seismic Methods

    Institute of Scientific and Technical Information of China (English)

    Xu Mingcai; Gao Jinghua; Liu Jianxun; Rong Lixin

    2005-01-01

    Using the seismic method to detect active faults directly below cities is an irreplaceable prospecting technique. The seismic method can precisely determine the fault position. Seismic method itself can hardly determine the geological age of fault. However, by considering in connection with the borehole data and the standard geological cross-section of the surveyed area, the geological age of reflected wave group can be qualitatively (or semi-quantitatively)determined from the seismic depth profile. To determine the upper terminal point of active faults directly below city, it is necessary to use the high-resolution seismic reflection technique.To effectively determine the geometric feature of deep faults, especially to determine the relation between deep and shallow fracture structures, the seismic reflection method is better than the seismic refraction method.

  3. Structural Analysis Extended with Active Fault Isolation - Methods and Algorithms

    DEFF Research Database (Denmark)

    Gelso, Esteban R.; Blanke, Mogens

    2009-01-01

    on system inputs can considerably enhance fault isolability. This paper investigates this possibility of active fault isolation from a structural point of view. While such extension of the structural analysis approach was suggested earlier, algorithms and case studies were needed to explore this theory....... The paper develops algorithms for investigation of the possibilities of active structural isolation and it offers illustrative examples and a larger case study to explore the properties of active structural isolability ideas....

  4. A new rolling bearing fault diagnosis method based on GFT impulse component extraction

    Science.gov (United States)

    Ou, Lu; Yu, Dejie; Yang, Hanjian

    2016-12-01

    Periodic impulses are vital indicators of rolling bearing faults. The extraction of impulse components from rolling bearing vibration signals is of great importance for fault diagnosis. In this paper, vibration signals are taken as the path graph signals in a manifold perspective, and the Graph Fourier Transform (GFT) of vibration signals are investigated from the graph spectrum domain, which are both introduced into the vibration signal analysis. To extract the impulse components efficiently, a new adjacency weight matrix is defined, and then the GFT of the impulse component and harmonic component in the rolling bearing vibration signals are analyzed. Furthermore, as the GFT graph spectrum of the impulse component is mainly concentrated in the high-order region, a new rolling bearing fault diagnosis method based on GFT impulse component extraction is proposed. In the proposed method, the GFT of a vibration signal is firstly performed, and its graph spectrum coefficients in the high-order region are extracted to reconstruct different impulse components. Next, the Hilbert envelope spectra of these impulse components are calculated, and the envelope spectrum values at the fault characteristic frequency are arranged in order. Furthermore, the envelope spectrum with the maximum value at the fault characteristic frequency is selected as the final result, from which the rolling bearing fault can be diagnosed. Finally, an index KR, which is the product of the kurtosis and Hilbert envelope spectrum fault feature ratio of the extracted impulse component, is put forward to measure the performance of the proposed method. Simulations and experiments are utilized to demonstrate the feasibility and effectiveness of the proposed method.

  5. Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

    Science.gov (United States)

    Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe

    2011-12-01

    The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.

  6. An integrated approach to planetary gearbox fault diagnosis using deep belief networks

    Science.gov (United States)

    Chen, Haizhou; Wang, Jiaxu; Tang, Baoping; Xiao, Ke; Li, Junyang

    2017-02-01

    Aiming at improving the accuracy of planetary gearbox fault diagnosis, an integrated scheme based on dimensionality reduction method and deep belief networks (DBNs) is presented in this paper. Firstly, the acquired vibration signals are decomposed into mono-component called intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD), and then Teager-Kaiser energy operator (TKEO) is used to track the instantaneous amplitude (IA) and instantaneous frequency (IF) of a mono-component amplitude modulation (AM) and frequency modulation (FM) signal. Secondly, a high dimensional feature set is constructed through extracting statistical features from six different signal groups. Then, an integrated dimensionality reduction method combining feature selection and feature extraction techniques is proposed to yield a more sensitive and lower dimensional feature set, which not only reduces the computation burden for fault diagnosis but also improves the separability of the samples by integrating the label information. Further, the low dimensional feature set is fed into DBNs classifier to identify the fault types using the optimal parameters selected by particle swarm optimization algorithm (PSO). Finally, two independent cases study of planetary gearbox fault diagnosis are carried out on test rig, and the results show that the proposed method provides higher accuracy in comparison with the existing methods.

  7. A novel identification method of Volterra series in rotor-bearing system for fault diagnosis

    Science.gov (United States)

    Xia, Xin; Zhou, Jianzhong; Xiao, Jian; Xiao, Han

    2016-01-01

    Volterra series is widely employed in the fault diagnosis of rotor-bearing system to prevent dangerous accidents and improve economic efficiency. The identification of the Volterra series involves the infinite-solution problems which is caused by the periodic characteristic of the excitation signal of rotor-bearing system. But this problem has not been considered in the current identification methods of the Volterra series. In this paper, a key kernels-PSO (KK-PSO) method is proposed for Volterra series identification. Instead of identifying the Volterra series directly, the key kernels of Volterra are found out to simply the Volterra model firstly. Then, the Volterra series with the simplest formation is identified by the PSO method. Next, simulation verification is utilized to verify the feasibility and effectiveness of the KK-PSO method by comparison to the least square (LS) method and traditional PSO method. Finally, experimental tests have been done to get the Volterra series of a rotor-bearing test rig in different states, and a fault diagnosis system is built with a neural network to classify different fault conditions by the kernels of the Volterra series. The analysis results indicate that the KK-PSO method performs good capability on the identification of Volterra series of rotor-bearing system, and the proposed method can further improve the accuracy of fault diagnosis.

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

    Science.gov (United States)

    Li, Ying; Wang, Kun

    2017-01-01

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

  9. Diagnosis of Multiple Fixture Faults in Multiple-Station Manufacturing Processes Based on State Space Approach

    Institute of Scientific and Technical Information of China (English)

    田兆青; 来新民; 林忠钦

    2004-01-01

    Dimensional quality is one of the most critical challenges in industries, which uses the multistage manufacturing process (MMP) such as assembly and machining for automotive and aerospace industries. According to investigations, fixture faults accounted for 72% of all the dimensional faults. Previous studies focused on only one fault or multiple faults occurred in one station or one fault in multiple stations, but these cases rarely appear in the real manufacturing. This paper presents a method for diagnosis of multiple fixture faults in the multi-station manufacturing process. The proposed method is based on the state space model of the MMP processes, which carries the information of the fixture layout geometry and sensor position. To identify the root cause, three continuous steps were used: a) development of the state space model and the construction of the statistics variables on offline mode, b) measurement of the coordinate measuring machines data on online mode and calculation of the statistics variables, and c) diagnostic algorithm for identifying the root cause. The presented paper integrates the state space model of the manufacturing processes and hypothesis test considering the impact of the measure noises. A case study verifies the proposed method.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. Application of Improved LMD, SVD Technique and RVM to Fault Diagnosis of Diesel Valve Trains

    Institute of Scientific and Technical Information of China (English)

    Liu Yu刘昱; Zhang Junhong张俊红; Lin Jiewei林杰威; Bi Fengrong毕凤荣

    2015-01-01

    Targeting the non-stationary characteristics of diesel engine vibration signals and the limitations of sin-gular value decomposition (SVD) technique, a new method based on improved local mean decomposition (LMD), SVD technique and relevance vector machine (RVM) was proposed for the identification of diesel valve fault in this study. Firstly, the vibration signals were acquired through the vibration sensors installed on the cylinder head in one normal state and four fault states of valve trains. Secondly, an improved LMD method was used to decompose the non-stationary signals into a set of stationary product functions (PF), from which the initial feature vector matri-ces can be formed automatically. Then, the singular values were obtained by applying the SVD technique to the initial feature vector matrixes. Finally, slant binary tree and sort separability criterion were combined to determine the structure of multi-class RVM, and the singular values were regarded as the fault feature vectors of RVM in the identification of fault types of diesel valve clearance. The experimental results showed that the proposed fault diag-nosis method can effectively extract the features of diesel valve clearance and identify the diesel valve fault accu-rately.

  12. Diagnosis of airgap eccentricity fault in the inverter driven induction motor drives by transformative techniques

    Directory of Open Access Journals (Sweden)

    Khadim Moin Siddiqui

    2016-09-01

    Full Text Available In the present paper, the airgap eccentricity fault of the induction motor has been diagnosed by digital signal processing transformative techniques in the inverter driven induction motor drives. The airgap eccentricity fault has been diagnosed in the transient condition by time domain as well as time-frequency domain techniques with the help of a proposed dynamic simulation model. In the past, many signal processing techniques had been used for various induction motor fault detection purpose such as fast Fourier transform, Hilbert transform, short term Fourier transform, etc. But, all techniques faced some sort of disadvantages. Therefore, in this paper, all shortcomings of the previous used signal processing techniques have been solved by newly wavelet transform's approximation signal. The low frequency approximation signal has been used to diagnose the eccentricity fault in the transient condition. Therefore, early fault diagnosis of the motor is possible and averted the motor before reaching in the ruinous conditions. As a result, the industries may save large revenues and unexpected failure conditions. The obtained results clearly demonstrate that the developed diagnostic technique may reliably separate airgap eccentricity fault in many stages.

  13. Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing

    Science.gov (United States)

    Lv, Yong; Yuan, Rui; Song, Gangbing

    2016-12-01

    Rolling bearings are widely used in rotary machinery systems. The measured vibration signal of any part linked to rolling bearings contains fault information when failure occurs, differing only by energy levels. Bearing failure will cause the vibration of other components, and therefore the collected bearing vibration signals are mixed with vibration signal of other parts and noise. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can avoid the loss of local information. Subsequently using the multivariate empirical mode decomposition (multivariate EMD) to simultaneously analyze the multivariate signal is beneficial to extract fault information, especially for weak fault characteristics during the period of early failure. This paper proposes a novel method for fault feature extraction of rolling bearing based on multivariate EMD. The nonlocal means (NL-means) denoising method is used to preprocess the multivariate signal and the correlation analysis is employed to calculate fault correlation factors to select effective intrinsic mode functions (IMFs). Finally characteristic frequencies are extracted from the selected IMFs by spectrum analysis. The numerical simulations and applications to bearing monitoring verify the effectiveness of the proposed method and indicate that this novel method is promising in the field of signal decomposition and fault diagnosis.

  14. Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process.Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study.Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

  15. Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

    Science.gov (United States)

    Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

    1998-01-01

    Control of air contaminants is a crucial factor in the safety considerations of crewed space flight. Indoor air quality needs to be closely monitored during long range missions such as a Mars mission, and also on large complex space structures such as the International Space Station. This work mainly pertains to the detection and simulation of air contaminants in the space station, though much of the work is easily extended to buildings, and issues of ventilation systems. Here we propose a method with which to track the presence of contaminants using an accurate physical model, and also develop a robust procedure that would raise alarms when certain tolerance levels are exceeded. A part of this research concerns the modeling of air flow inside a spacecraft, and the consequent dispersal pattern of contaminants. Our objective is to also monitor the contaminants on-line, so we develop a state estimation procedure that makes use of the measurements from a sensor system and determines an optimal estimate of the contamination in the system as a function of time and space. The real-time optimal estimates in turn are used to detect faults in the system and also offer diagnoses as to their sources. This work is concerned with the monitoring of air contaminants aboard future generation spacecraft and seeks to satisfy NASA's requirements as outlined in their Strategic Plan document (Technology Development Requirements, 1996).

  16. Quantitative NDE thermography for fault diagnosis of ball bearings with micro-foreign substances

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Won Tae [Div. of Mechanical and Automotive Engineering, Kongju NationalUniversity, Cheonan (Korea, Republic of); Hong, Dong Pyo [Dept. of Mechanical System Engineering, Chonbuk National Univerity, Jeonju (Korea, Republic of)

    2014-08-15

    In this study, a non-destructive evaluation (NDE) method is proposed for ball bearings contaminated with micro foreign substances, which were inserted into a ball bearing to create a defective specimen. The non-contact quantitative infrared thermographic technique was applied for NDE condition monitoring. Passive thermographic experiments were conducted to perform early fault diagnosis, for bearings operated at optimized torque status under a dynamic load condition. The temperature profiles for normal and defective specimens were quantitatively compared, and the thermographic data analyzed. Based on the NDE results, the temperature characteristics and abnormal fault detection of the ball bearing were quantitatively analyzed according to the rise in temperature.

  17. The Marshall Space Flight Center Fault Detection Diagnosis and Recovery Laboratory

    Science.gov (United States)

    Burchett, Bradley T.; Gamble, Jonathan; Rabban, Michael

    2008-01-01

    The Fault Detection Diagnosis and Recovery Lab (FDDR) has been developed to support development of,fault detection algorithms for the flight computer aboard the Ares I and follow-on vehicles. It consists of several workstations using Ethernet and TCP/IP to simulate communications between vehicle sensors, flight computers, and ground based support computers. Isolation of tasks between workstations was set up intentionally to limit information flow and provide a realistic simulation of communication channels within the vehicle and between the vehicle and ground station.

  18. Diagnosis method based on wavelet coefficient scale relativity correlation dimension for fault

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Correlation dimension as a tool to describe machinery condition is introduced.Vibration signals of the fan under different working conditions are analyzed using a threshold filtering algorithm based on the region relativity of the wavelet coefficients for reducing noise.The result shows that the characteristics of the signal could be preserved completely.The correlation dimension is able to identify conditions of the fan with faults compared with the normal condition,thereby providing an effective technology for condition monitoring and fault diagnosis of mechanical equipment.

  19. On-line Dynamic Model Correction Based Fault Diagnosis in Chemical Processes

    Institute of Scientific and Technical Information of China (English)

    田文德; 孙素莉

    2007-01-01

    A novel fault detection and diagnosis method was proposed,using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur.It aims at parameter failures,and the parameters are updated via on-line correction.As it can predict the trend of process and determine the existence of malfunctions simultaneously,this method does not need to design problem-specific observer to estimate unmeasured state variables.Application of the proposed method is presented on one water tank and one aromatization reactor,and the results are compared with those from the traditional method.

  20. Forward and backward models for fault diagnosis based on parallel genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Yi LIU; Ying LI; Yi-jia CAO; Chuang-xin GUO

    2008-01-01

    In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.

  1. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

    Science.gov (United States)

    Al-Bugharbee, Hussein; Trendafilova, Irina

    2016-05-01

    This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.

  2. Research of Earthquake Potential from Active Fault Observation in Taiwan

    Science.gov (United States)

    Chien-Liang, C.; Hu, J. C.; Liu, C. C.; En, C. K.; Cheng, T. C. T.

    2015-12-01

    We utilize GAMIT/GLOBK software to estimate the precise coordinates for continuous GPS (CGPS) data of Central Geological Survey (CGS, MOEA) in Taiwan. To promote the software estimation efficiency, 250 stations are divided by 8 subnets which have been considered by station numbers, network geometry and fault distributions. Each of subnets include around 50 CGPS and 10 international GNSS service (IGS) stations. After long period of data collection and estimation, a time series variation can be build up to study the effect of earthquakes and estimate the velocity of stations. After comparing the coordinates from campaign-mode GPS sites and precise leveling benchmarks with the time series from continuous GPS stations, the velocity field is consistent with previous measurement which show the reliability of observation. We evaluate the slip rate and slip deficit rate of active faults in Taiwan by 3D block model DEFNODE. First, to get the surface fault traces and the subsurface fault geometry parameters, and then establish the block boundary model of study area. By employing the DEFNODE technique, we invert the GPS velocities for the best-fit block rotate rates, long term slip rates and slip deficit rates. Finally, the probability analysis of active faults is to establish the flow chart of 33 active faults in Taiwan. In the past two years, 16 active faults in central and northern Taiwan have been assessed to get the recurrence interval and the probabilities for the characteristic earthquake occurred in 30, 50 and 100 years.

  3. Can cosmic ray exposure dating reveal the normal faulting activity of the Cordillera Blanca Fault, Peru?

    Directory of Open Access Journals (Sweden)

    L.L. Siame

    2006-12-01

    Full Text Available The build-up of in situ-produced cosmogenic 10Be within bedrock scarps and escarpments associated to the Cordillera Blanca Normal Fault, Peru, was measured to evaluate, through Cosmic Ray Exposure dating, its normal faulting activity. The highest mountain peaks in Peru belong to the 210 km-long, NW- striking, Cordillera Blanca. Along its western border, the Cordillera Blanca Normal Fault is responsible for a vertical relief over 4.4 km, whose prominent 2 km high escarpment is characterized by ~1 km-high triangular facets corresponding to vertical displacements cumulated during the last 1-2 million years. At a more detailed scale, this fault system exhibits continuous geomorphic evidence of repeated displacements, underlined by 2 to 70 m-high scarps, corresponding to vertical displacements cumulated since Late Pleistocene and Holocene periods. Although microseismicity occurs along the Cordillera Blanca Normal Fault, no major historical or instrumental earthquake has been recorded since the beginning of the Spanish settlement in the 16th century. To evaluate the vertical slip rate along the major 90 km-long central segment of the Cordillera Blanca Normal Fault, the Quaternary fault escarpment (i.e., triangular facet, as well as the bedrock fault scarp, have been sampled for 10Be Cosmic Ray Exposure dating. Even if the uppermost part of the triangular facets have been resurfaced by the Last Glacial Maximum glaciers, our results allow to estimate a vertical slip-rate of 3±1 mm/yr, and suggest at least 2 seismic events during the last 3000 years.

  4. Neural Network Based Fault Detection and Diagnosis System for Three-Phase Inverter in Variable Speed Drive with Induction Motor

    Directory of Open Access Journals (Sweden)

    Furqan Asghar

    2016-01-01

    Full Text Available Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.

  5. Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

    Science.gov (United States)

    Wang, Yingmin; Zhang, Fujun; Cui, Tao; Zhou, Jinlong

    2016-03-01

    Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015-0.017 5 and sample error is controlled within 0-0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis; the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

  6. Fault Diagnosis for Manifold Absolute Pressure Sensor(MAP) of Diesel Engine Based on Elman Neural Network Observer

    Institute of Scientific and Technical Information of China (English)

    WANG Yingmin; ZHANG Fujun; CUI Tao; ZHOU Jinlong

    2016-01-01

    Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can’t be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015-0.017 5 and sample error is controlled within 0-0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis;the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

  7. Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Xinyi Yang

    2016-01-01

    Full Text Available A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.

  8. Identification of Active Faults by Aerial Photograph Interpretation and Case

    Energy Technology Data Exchange (ETDEWEB)

    Lee, J.R.; Chang, C.J.; Choi, W.H.; Yun, K.H.; Park, D.H.; Shin, S.H. [Korea Electric Power Research Institute, Taejon (Korea)

    2002-07-01

    This report is the technical memo of the research project entitled ''Development of Technology of Advanced Seismic Safety Assessment for NPP sites''. The purposes of this report are to describe analysis methods of photographic characteristics related with active faults, to identify active faults by aerial photograph interpretation and to review case studies. (author). 27 refs., 165 figs., 8 tabs.

  9. Recent activity of Chihe segment of Tanlu fault zone

    Institute of Scientific and Technical Information of China (English)

    姚大全; 刘加灿

    2004-01-01

    By means of differentiation of remote sensing image, field seismo-geological survey, analysis on drilling exploration materials, sampling and dating of rock samples, combined with seismicity and microscopic tectonic analysis, this paper studies the recent activity of Chihe segment of the Tanlu fault zone. The result indicates that the Chihe fault segment undergoes the deformation alternately in the mode of stick slip and creep during Late Quaternary, and its recent activity is mainly creep.

  10. Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2006-01-01

    A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor

  11. Diagnosis of Intermittent Faults in IGBTs Using the Latent Nestling Method with Hybrid Coloured Petri Nets

    Directory of Open Access Journals (Sweden)

    Leonardo Rodriguez-Urrego

    2015-01-01

    Full Text Available This paper presents a fault diagnosis application of the Latent Nestling Method to IGBTs. The paper extends the Latent Nestling Method based in Coloured Petri Nets (CPNs to hybrid systems in such a manner that IGBTs performance can be modeled. CPNs allow for an enhanced capability for synthesis and modeling in contrast to the classical phenomena of combinational state explosion when Finite State Machine methods are applied. We present an IGBT model with different fault modes including those of intermittent nature that can be used advantageously as predictive symptoms within a predictive maintenance strategy. Ageing stress tests have been experimentally applied to the IGBTs modules and intermittent faults are diagnosed as precursors of permanent failures. In addition, ageing is validated with morphological analysis (Scanning Electron Microscopy and semiqualitative analysis (Energy Dispersive Spectrometry.

  12. Study on NDT Fault Diagnosis of the Ball Bearing under Stage of Abrasion by Infrared Thermography

    Energy Technology Data Exchange (ETDEWEB)

    Seo, Jin Ju; Hong, Dong Pyo [Chonbuk National University, Jeonju (Korea, Republic of); Kim, Won Tae [Kongju National University, Gongju (Korea, Republic of)

    2012-02-15

    For fault detection about the abrasion stage of rotational machineries under the dynamic loading conditions unlike the traditional diagnosis method used in the past decade, the infrared thermographic method with its distinctive advantages in non-contact, non-destructive, and visible aspects is proposed. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiments were conducted as an alternative way to proceeding the traditional fault monitoring on spectrum analyzer. As results, the thermographic experiment was compared with the traditional vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results obtained as NDT, the temperature characteristics and abnormal fault detections of the ball bearing according to the abrasion stage were analyzed.

  13. Fault Diagnosis Method Based on Fractal Theory and Its Application in Wind Power Systems

    Institute of Scientific and Technical Information of China (English)

    赵玲; 黄大荣; 宋军

    2012-01-01

    The non-linear dynamic theory brought a new method for recognizing and predicting complex non-linear dynamic behaviors. The non-linear behavior of vibration signals can be described by using fractal dimension quantitatively. In this paper, a fractal dimension calculation method for discrete signals in the fractal theory was applied to extract the fractal di- mension feature vectors and classified various fault types. Based on the wavelet packet transform, the energy feature vectors were extracted after the vibration signal was decomposed and reconstructed. Then, a wavelet neural network was used to recognize the mechanical faults. Finally, the fault diagnosis for a wind power system was taken as an example to show the method' s feasibility.

  14. Application of wavelet analysis to fault diagnosis of angular measuring system

    Institute of Scientific and Technical Information of China (English)

    邓辉宇; 苏宝库; 邹明杰

    2003-01-01

    For fault diagnosis, signal singularity and irregularity discontinuity fraction are very significant characteristics of signal. The discontinuity of output signal represents a system fault . In an angular measuring system, function transformer uses two D/A convertors, output circuit fault of a D/A convertor brings about discontinuity of one phase input voltage amplitude of inductosyn, results in a system error exceeding the allowable error and reduces the system accuracy. This is the reason why discontinuity is detected. Fourier transform has no resolution ability in angular-domain, but wavelet can analyse signal in angular and frequency-domains. So we decompose the error signal of angular measuring system by wavelet, detect the signal singularity at high frequency layer and find out the accurate position of it.

  15. Phase Space Similarity as a Signature for Rolling Bearing Fault Diagnosis and Remaining Useful Life Estimation

    Directory of Open Access Journals (Sweden)

    Fang Liu

    2016-01-01

    Full Text Available Feature extraction from vibration signal is still a challenge in the area of fault diagnosis and remaining useful life (RUL estimation of rotary machine. In this paper, a novel feature called phase space similarity (PSS is introduced for health condition monitoring of bearings. Firstly, the acquired signal is transformed to the phase space through the phase space reconstruction (PSR. The similar vibration always exists in the phase space due to the comparable evolution of the dynamics that are characteristic of the system state. Secondly, the normalized cross-correlation (NCC is employed to calculate the PSS between bearing data with different states. Based on the PSS, a fault pattern recognition algorithm, a bearing fault size prediction algorithm, and a RUL estimation algorithm are introduced to analyze the experimental signal. Results have shown the effectiveness of the PSS as it can better grasp the nature and regularity of the signals.

  16. Diagnosis of stator faults in induction motor based on zero sequence voltage after switch-off

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    To improve the accuracy of the stator winding fault diagnosis in induction motor, a new diagnostic method based on the Hilbert-Huang transform (HHT) was proposed. The ratio of fundamental zero sequence voltage to positive sequence voltage after switch-offwas selected as the stator fault characteristic, which could effectively avoid the influence of the supply unbalance and the load fluctuation, and directly represent the asymmetry in the stator. Using the empirical mode decomposition (EMD) based on HHT, the zero sequence voltage after switch-off was decomposed and the fundamental component was extracted. Then, the fault characteristic can be acquired. Experimental results on a 4-kW induction motor demonstrate the feasibility and effectiveness of this method.

  17. HYBRID WAVELET PACKET-TEAGER ENERGY OPERATOR ANALYSIS AND ITS APPLICATION FOR GEARBOX FAULT DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Based on wavelet packet decomposition (WPD) algorithm and Teager energy operator (TEO), a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and TEO modulation are introduced respectively. The preprocessed signal is interpolated with the cubic spline function, then expanded over the selected basis wavelets. Grouping its wavelet packet components of the signal based on the minimum entropy criterion, the interpolated signal can be decomposed into its dominant components with nearly distinct fault frequency contents. To extract the demodulation information of each dominant component, TEO is used. The performance of the proposed method is assessed by means of several tests on vibration signals collected from the gearbox mounted on a heavy truck. It is proved that hybrid WPD-TEO method is effective and robust for detecting and diagnosing localized gearbox faults.

  18. Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis

    Science.gov (United States)

    Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yan, Ruqiang

    2016-12-01

    The bearing failure, generating harmful vibrations, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing fault diagnosis is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing fault diagnosis could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, fault information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates fault information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of fault information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing faults), a novel weighted sparse model and its corresponding framework for bearing fault diagnosis is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the fault information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family

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

    Science.gov (United States)

    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

  20. A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings

    Directory of Open Access Journals (Sweden)

    Huaqing Wang

    2016-09-01

    Full Text Available The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples.

  1. State Tracking and Fault Diagnosis for Dynamic Systems Using Labeled Uncertainty Graph

    Directory of Open Access Journals (Sweden)

    Gan Zhou

    2015-11-01

    Full Text Available Cyber-physical systems such as autonomous spacecraft, power plants and automotive systems become more vulnerable to unanticipated failures as their complexity increases. Accurate tracking of system dynamics and fault diagnosis are essential. This paper presents an efficient state estimation method for dynamic systems modeled as concurrent probabilistic automata. First, the Labeled Uncertainty Graph (LUG method in the planning domain is introduced to describe the state tracking and fault diagnosis processes. Because the system model is probabilistic, the Monte Carlo technique is employed to sample the probability distribution of belief states. In addition, to address the sample impoverishment problem, an innovative look-ahead technique is proposed to recursively generate most likely belief states without exhaustively checking all possible successor modes. The overall algorithms incorporate two major steps: a roll-forward process that estimates system state and identifies faults, and a roll-backward process that analyzes possible system trajectories once the faults have been detected. We demonstrate the effectiveness of this approach by applying it to a real world domain: the power supply control unit of a spacecraft.

  2. A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings

    Science.gov (United States)

    Wang, Huaqing; Ke, Yanliang; Song, Liuyang; Tang, Gang; Chen, Peng

    2016-01-01

    The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples. PMID:27657063

  3. Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

    Science.gov (United States)

    Liu, Hongmei; Wang, Xuan; Lu, Chen

    2015-08-01

    A rolling bearing vibration signal is nonlinear and non-stationary and has multiple components and multifractal properties. A rolling-bearing fault-diagnosis method based on Local Characteristic-scale Decomposition-Teager Energy Operator (LCD-TEO) and multifractal detrended fluctuation analysis (MF-DFA) is first proposed in this paper. First, the vibration signal was decomposed into several intrinsic scale components (ISCs) by using LCD, which is a newly developed signal decomposition method. Second, the instantaneous amplitude was obtained by applying the TEO to each major ISC for demodulation. Third, the intrinsic multifractality features hidden in each major ISC were extracted by using MF-DFA, among which the generalized Hurst exponents are selected as the multifractal feature in this paper. Finally, the feature vectors were obtained by applying principal components analysis (PCA) to the extracted multifractality features, thus reducing the dimension of the multifractal features and obtaining the fault feature insensitive to variation in working conditions, further enhancing the accuracy of diagnosis. According to the extracted feature vector, rolling bearing faults can be diagnosed under variable working conditions. The experimental results demonstrate its desirable diagnostic performance under both different working conditions and different fault severities. Simultaneously, the results of comparison show that the performance of the proposed diagnostic method outperforms that of Hilbert-Huang transform (HHT) combined with MF-DFA or LCD-TEO combined with mono-fractal analysis.

  4. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

    Science.gov (United States)

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

  5. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

    Directory of Open Access Journals (Sweden)

    Huaqing Wang

    2009-04-01

    Full Text Available This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to.

  6. Simultaneous State and Parameter Estimation Based Actuator Fault Detection and Diagnosis for an Unmanned Helicopter

    Directory of Open Access Journals (Sweden)

    Wu Chong

    2015-03-01

    Full Text Available Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD for single-rotor unmanned helicopters (UHs is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on actuator healthy coefficients (AHCs, which are introduced to represent actuator faults, a combined dynamic model is established with the augmented state containing both the flight state and AHCs. Then the actuator fault detection and diagnosis problem is transformed into a general nonlinear estimation one: given control inputs and the measured flight state contaminated by measurement noises, estimate both the flight state and AHCs recursively in each time-step, which is also known as the simultaneous state and parameter estimation problem. The estimated AHCs can further be used for fault tolerant control (FTC. Based on the existing widely used nonlinear estimation methods such as the unscented Kalman filter (UKF and the extended set-membership filter (ESMF, three kinds of adaptive schemes (KF-UKF, MIT-UKF and MIT-ESMF are proposed by our team to improve the actuator FDD performance. A comprehensive comparative study on these different estimation methods is given in detail to illustrate their advantages and disadvantages when applied to unmanned helicopter actuator FDD.

  7. An Expert Fault Diagnosis System for Vehicle Air Conditioning Product Development

    Science.gov (United States)

    Tan, C. F.; Tee, B. T.; Khalil, S. N.; Chen, W.; Rauterberg, G. W. M.

    2015-09-01

    The paper describes the development of the vehicle air-conditioning fault diagnosis system in automotive industries with expert system shell. The main aim of the research is to diagnose the problem of new vehicle air-conditioning system development process and select the most suitable solution to the problems. In the vehicle air-conditioning manufacturing industry, process can be very costly where an expert and experience personnel needed in certain circumstances. The expert of in the industry will retire or resign from time to time. When the expert is absent, their experience and knowledge is difficult to retrieve or lost forever. Expert system is a convenient method to replace expert. By replacing the expert with expert system, the accuracy of the processes will be increased compared to the conventional way. Therefore, the quality of product services that are produced will be finer and better. The inputs for the fault diagnosis are based on design data and experience of the engineer.

  8. Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    TAN Yanghong; HE Yigang; FANG Gefeng

    2007-01-01

    A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.

  9. A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

    Institute of Scientific and Technical Information of China (English)

    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.

  10. A Parallel Decision Model Based on Support Vector Machines and Its Application to Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    Yan Weiwu(阎威武); Shao Huihe

    2004-01-01

    Many industrial process systems are becoming more and more complex and are characterized by distributed features. To ensure such a system to operate under working order, distributed parameter values are often inspected from subsystems or different points in order to judge working conditions of the system and make global decisions. In this paper, a parallel decision model based on Support Vector Machine (PDMSVM) is introduced and applied to the distributed fault diagnosis in industrial process. PDMSVM is convenient for information fusion of distributed system and it performs well in fault diagnosis with distributed features. PDMSVM makes decision based on synthetic information of subsystems and takes the advantage of Support Vector Machine. Therefore decisions made by PDMSVM are highly reliable and accurate.

  11. FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    LIU Guanjun; LIU Xinmin; QIU Jing; HU Niaoqing

    2007-01-01

    Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

  12. Fault Estimation

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, H.

    2002-01-01

    This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis prob-lems are reformulated in the so-called standard problem setup introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis pr...... problems can be solved by standard optimization tech-niques. The proposed methods include: (1) fault diagnosis (fault estimation, (FE)) for systems with model uncertainties; (2) FE for systems with parametric faults, and (3) FE for a class of nonlinear systems.......This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis prob-lems are reformulated in the so-called standard problem setup introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis...

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

    CERN Document Server

    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...

  14. Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Silvio Simani

    2014-11-01

    Full Text Available In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution.

  15. Fault Diagnosis of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method

    Directory of Open Access Journals (Sweden)

    Jinglong Chen

    2015-10-01

    Full Text Available The demountable disk-drum aero-engine rotor is an important piece of equipment that greatly impacts the safe operation of aircraft. However, assembly looseness or crack fault has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and fault diagnosis technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured vibration data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then vibration signal is processed by customized ensemble multiwavelet transform. Next, normalized information entropy of multiwavelet decomposition coefficients is computed to directly reflect and evaluate the condition. The proposed approach is first applied to fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack fault of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault detection of aero-engine rotor. Moreover, the robustness of the multiwavelet method against noise is also tested and verified by simulation and field experiments.

  16. EXPERIMENT BASED FAULT DIAGNOSIS ON BOTTLE FILLING PLANT WITH LVQ ARTIFICIAL NEURAL NETWORK ALGORITHM

    Directory of Open Access Journals (Sweden)

    Mustafa DEMETGÜL

    2008-01-01

    Full Text Available In this study, an artificial neural network is developed to find an error rapidly on pneumatic system. Also the ANN prevents the system versus the failure. The error on the experimental bottle filling plant can be defined without any interference using analog values taken from pressure sensors and linear potentiometers. The sensors and potentiometers are placed on different places of the plant. Neural network diagnosis faults on plant, where no bottle, cap closing cylinder B is not working, bottle cap closing cylinder C is not working, air pressure is not sufficient, water is not filling and low air pressure faults. The fault is diagnosed by artificial neural network with LVQ. It is possible to find an failure by using normal programming or PLC. The reason offing Artificial Neural Network is to give a information where the fault is. However, ANN can be used for different systems. The aim is to find the fault by using ANN simultaneously. In this situation, the error taken place on the pneumatic system is collected by a data acquisition card. It is observed that the algorithm is very capable program for many industrial plants which have mechatronic systems.

  17. Auditory-model-based Feature Extraction Method for Mechanical Faults Diagnosis

    Institute of Scientific and Technical Information of China (English)

    LI Yungong; ZHANG Jinping; DAI Li; ZHANG Zhanyi; LIU Jie

    2010-01-01

    It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory(EA) model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.

  18. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

    Science.gov (United States)

    Gu, F.; Shao, Y.; Hu, N.; Naid, A.; Ball, A. D.

    2011-01-01

    This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor based on bispectrum analysis. The theoretical basis is developed to understand the nonlinear characteristics of current signals when the motor undertakes a varying load under different faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is then adopted to combine both lower sidebands and higher sidebands simultaneously and hence characterise the current signal more accurately. Based on this new bispectrum analysis a more effective diagnostic feature, namely normalised bispectral peak, is developed for fault classification. In association with the kurtosis value of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from the other fault cases and different degrees of discharge valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.

  19. Structural Analysis Approach to Fault Diagnosis with Application to Fixed-wing Aircraft Motion

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh

    2001-01-01

    The paper presents a structural analysis based method for fault diagnosis purposes. The method uses the structural model of the system and utilizes the matching idea to extract system's inherent redundant information. The structural model is represented by a bipartite directed graph. FDI Possibil...... Possibilities are examined by further analysis of the obtained information. The method is illustrated by applying on the LTI model of motion of a fixed-wing aircraft....

  20. Structural Analysis Approach to Fault Diagnosis with Application to Fixed-wing Aircraft Motion

    DEFF Research Database (Denmark)

    Izadi-Zamanabadi, Roozbeh

    2002-01-01

    The paper presents a structural analysis based method for fault diagnosis purposes. The method uses the structural model of the system and utilizes the matching idea to extract system's inherent redundant information. The structural model is represented by a bipartite directed graph. FDI Possibil...... Possibilities are examined by further analysis of the obtained information. The method is illustrated by applying on the LTI model of motion of a fixed-wing aircraft....

  1. CONDITION MONITORING AND FAULT DIAGNOSIS FOR TENSION UNBALANCE OF ROPES IN MULTI-ROPE FRICTION WINDER

    Institute of Scientific and Technical Information of China (English)

    杨兆建; 王勤贤; 任芳

    1997-01-01

    This paper analyzes the reasons of the tension unbalance of the ropes in multi-rope friction winder, introduces the method of an on-line monitoring rope tensions with a testing device developed by authors, and proposes the criteria of the fault diagnosis and the method of adjustment for the tension unbalance of the ropes, which is important to the theoretical study on the tension unbalance of the ropes and the maintenance of multi-rope winder.

  2. Diagnosis of constant faults in read-once contact networks over finite bases

    KAUST Repository

    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.

  3. Diagnosis of constant faults in iteration-free circuits over monotone basis

    KAUST Repository

    Alrawaf, Saad Abdullah

    2014-03-01

    We show that for each iteration-free combinatorial circuit S over a basis B containing only monotone Boolean functions with at most five variables, there exists a decision tree for diagnosis of constant faults on inputs of gates with depth at most 7L(S) where L(S) is the number of gates in S. © 2013 Elsevier B.V. All rights reserved.

  4. A Fuzzy-Neuro Scheme for Fault Diagnosis and Life Consumption of Rotordynamic Systems

    Science.gov (United States)

    1996-04-01

    O6B DTIC Information For The Defense CommunrtY 000MWPP A FUZZY-NEURO SCHEME FOR FAULT DIAGNOSIS AND LIFE CONSUMPTION OF ROTORDYNAMIC SYSTEMS Michael J... rotordynamic , finite-etement modeling. A rotor demonstration rig is used as a proof of concept tool. The approach integrates rotor shaft vibration...measurements with detailed, rotordynamic , finite-element models through a fuzzy-neuro scheme which is specifically developed to respond to the rotor system

  5. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    Science.gov (United States)

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.

  6. A Study on the Quaternary Activity of the Tianjin Fault

    Institute of Scientific and Technical Information of China (English)

    Shao Yongxin; Li Zhenhai; Chen Yukun; Ren Feng; Yao Zhengquan

    2010-01-01

    The Tianjin fault includes South Tianjin fault and North Tianjin fault.Based on the results of artiffcial seismic exploration,fonr borehole profiles were laid out respectively west of Jinghai county town,Chaomidian village of Xiqing district,Xiaonanhe village of Xiqing district and Zhutoudian village of Ninghe county.to implement the exploration of these faults.Through identification of microfossils.the locations of marine beds in boreholes were obtained in this work,and through stratigraphic dating,the ages of the first,second and third marine beds were determined.Through strata correlation with the marine beds as key marker beds and integrating with the test results of paleo geomagnetism of boreholes BZ2 and TN3,the activity in the North and South Tianjin faults was analyzed and studied.The results indicate that there is no evidence of movement of the South Tianjin fault since the Late Pleistocene,but may have had weak activity before the Middle Pleistocene.No evidence of activity in the North Tianjin fanit was found since the Late Pleistocene either,but might have been active in the early stage of the Early Pleistocene.These show that the activity of the South Tianjin fault is stronger than that of the North Tianjin fault.At the same time,we find that the second,third and fourth marine beds are lacking to some extent in difierent areas.So.before they are used in strata correlation,the age of marine beds must be determined,otherwise the results of strata correlation may lead to errors.For the second marine bed,where there has been dispute about its age,we consider the age to be about 70ka.

  7. Active fault research in India: achievements and future perspective

    Directory of Open Access Journals (Sweden)

    Mithila Verma

    2016-01-01

    Full Text Available This paper provides a brief overview of the progress made towards active fault research in India. An 8 m high scarp running for more than 80 km in the Rann of Kachchh is the classical example of the surface deformation caused by the great earthquake (1819 Kachchh earthquake. Integration of geological/geomorphic and seismological data has led to the identification of 67 active faults of regional scale, 15 in the Himalaya, 17 in the adjoining foredeep with as many as 30 neotectonic faults in the stable Peninsular India. Large-scale trenching programmes coupled with radiometric dates have begun to constraint the recurrence period of earthquakes; of the order of 500–1000 years for great earthquakes in the Himalaya and 10,000 years for earthquakes of >M6 in the Peninsular India. The global positioning system (GPS data in the stand alone manner have provided the fault parameters and length of rupture for the 2004 Andaman Sumatra earthquakes. Ground penetration radar (GPR and interferometric synthetic aperture radar (InSAR techniques have enabled detection of large numbers of new active faults and their geometries. Utilization of modern technologies form the central feature of the major programme launched by the Ministry of Earth Sciences, Government of India to prepare geographic information system (GIS based active fault maps for the country.

  8. Acoustic diagnosis of mechanical fault feature based on reference signal frequency domain semi-blind extraction

    Directory of Open Access Journals (Sweden)

    Zeguang YI

    2015-08-01

    Full Text Available Aiming at fault diagnosis problems caused by complex machinery parts, serious background noises and the application limitations of traditional blind signal processing algorithm to the mechanical acoustic signal processing, a failure acoustic diagnosis based on reference signal frequency domain semi-blind extraction is proposed. Key technologies are introduced: Based on frequency-domain blind deconvolution algorithm, the artificial fish swarm algorithm which is good for global optimization is used to construct improved multi-scale morphological filters which is applicable to mechanical failure in order to weaken the background noises; combining the structural parameters of parts to build a reference signal, complex components blind separation is carried out on the signals after noise reduction paragraph by paragraph by reference signal unit semi-blind extraction algorithm; then the improved KL-distance of complex independent components is employed as distance measure to resolve the permutation, and finally the mechanical fault characteristic signals are extracted and separated. The actual acoustic diagnosis of rolling bearing fault in sound field environment results proves the effectiveness of this algorithm.

  9. Iterative generalized time-frequency reassignment for planetary gearbox fault diagnosis under nonstationary conditions

    Science.gov (United States)

    Chen, Xiaowang; Feng, Zhipeng

    2016-12-01

    Planetary gearboxes are widely used in many sorts of machinery, for its large transmission ratio and high load bearing capacity in a compact structure. Their fault diagnosis relies on effective identification of fault characteristic frequencies. However, in addition to the vibration complexity caused by intricate mechanical kinematics, volatile external conditions result in time-varying running speed and/or load, and therefore nonstationary vibration signals. This usually leads to time-varying complex fault characteristics, and adds difficulty to planetary gearbox fault diagnosis. Time-frequency analysis is an effective approach to extracting the frequency components and their time variation of nonstationary signals. Nevertheless, the commonly used time-frequency analysis methods suffer from poor time-frequency resolution as well as outer and inner interferences, which hinder accurate identification of time-varying fault characteristic frequencies. Although time-frequency reassignment improves the time-frequency readability, it is essentially subject to the constraints of mono-component and symmetric time-frequency distribution about true instantaneous frequency. Hence, it is still susceptible to erroneous energy reallocation or even generates pseudo interferences, particularly for multi-component signals of highly nonlinear instantaneous frequency. In this paper, to overcome the limitations of time-frequency reassignment, we propose an improvement with fine time-frequency resolution and free from interferences for highly nonstationary multi-component signals, by exploiting the merits of iterative generalized demodulation. The signal is firstly decomposed into mono-components of constant frequency by iterative generalized demodulation. Time-frequency reassignment is then applied to each generalized demodulated mono-component, obtaining a fine time-frequency distribution. Finally, the time-frequency distribution of each signal component is restored and superposed to

  10. Secondary Fault Activity of the North Anatolian Fault near Avcilar, Southwest of Istanbul: Evidence from SAR Interferometry Observations

    Directory of Open Access Journals (Sweden)

    Faqi Diao

    2016-10-01

    Full Text Available Strike-slip faults may be traced along thousands of kilometers, e.g., the San Andreas Fault (USA or the North Anatolian Fault (Turkey. A closer look at such continental-scale strike faults reveals localized complexities in fault geometry, associated with fault segmentation, secondary faults and a change of related hazards. The North Anatolian Fault displays such complexities nearby the mega city Istanbul, which is a place where earthquake risks are high, but secondary processes are not well understood. In this paper, long-term persistent scatterer interferometry (PSI analysis of synthetic aperture radar (SAR data time series was used to precisely identify the surface deformation pattern associated with the faulting complexity at the prominent bend of the North Anatolian Fault near Istanbul city. We elaborate the relevance of local faulting activity and estimate the fault status (slip rate and locking depth for the first time using satellite SAR interferometry (InSAR technology. The studied NW-SE-oriented fault on land is subject to strike-slip movement at a mean slip rate of ~5.0 mm/year and a shallow locking depth of <1.0 km and thought to be directly interacting with the main fault branch, with important implications for tectonic coupling. Our results provide the first geodetic evidence on the segmentation of a major crustal fault with a structural complexity and associated multi-hazards near the inhabited regions of Istanbul, with similarities also to other major strike-slip faults that display changes in fault traces and mechanisms.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  12. Application of Analytic Redundancy-based Fault Diagnosis of Sensors to Onboard Maintenance System

    Institute of Scientific and Technical Information of China (English)

    CHI Chengzhi; ZHANG Weiguo; LIU Xiaoxiong

    2012-01-01

    Analytic redundancy-based fault diagnosis technique (ARFDT) is applied to onboard maintenance system (OMS).The principle of the proposed ARFDT scheme is to design a redundancy configuration using ARFDT to enhance the functions of redundancy management and built in test equipment (BITE) monitor.Redundancy configuration for dual-redundancy and analytic redundancy is proposed,in which,the fault diagnosis includes detection and isolation.In order to keep the balance between rapid diagnosis and binary hypothesis,a filter together with an elapsed time limit is designed for sequential probability ratio test (SPRT) in the process of isolation.Diagnosis results would be submitted to central maintenance computer (CMC) together with BITE information.Moreover,by adopting reconstruction,the designed method not only provides analytic redundancy to help redundancy management,but also compensates the output when both of the sensors of the same type are faulty.Our scheme is applied to an aircraft's sensors in a simulation experiment,and the results show that the proposed filter SPRT (FSPRT) saves at least 50% of isolation time than Wald SPRT (WSPRT).Also,effectiveness,practicability and rapidity of the proposed scheme can be successfully achieved in OMS.

  13. Diagnosis and Tolerant Strategy of an Open-Switch Fault for T-type Three-Level Inverter Systems

    DEFF Research Database (Denmark)

    Choi, Uimin; Lee, Kyo Beum; Blaabjerg, Frede

    2014-01-01

    -tolerant strategy is explained by dividing into two cases: the faulty condition of half-bridge switches and the neutral-point switches. The performance of the T-type inverter system improves considerably by the proposed fault tolerant algorithm when a switch fails. The roposed method does not require additional......This paper proposes a new diagnosis method of an open-switch fault and fault-tolerant control strategy for T-type three-level inverter systems. The location of faulty switch can be identified by the average of normalized phase current and the change of the neutral-point voltage. The proposed fault...

  14. A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    AKTAS, M.

    2012-11-01

    Full Text Available The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits in the driving systems with Field Oriented Controlled Permanent Magnet Synchro?nous Motors (PMSM of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.

  15. Aero-engine fault diagnosis applying new fast support vector algorithm

    Institute of Scientific and Technical Information of China (English)

    XU Qi-hua; GENG Shuai; SHI Jun

    2012-01-01

    A new fast learning algorithm was presented to solve the large-scale support vector machine ( SVM ) training problem of aero-engine fault diagnosis.The relative boundary vectors ( RBVs ) instead of all the original training samples were used for the training of the binary SVM fault classifiers.This pruning strategy decreased the number of final training sample significantly and can keep classification accuracy almost invariable.Accordingly , the training time was shortened to 1 / 20compared with basic SVM classifier.Meanwhile , owing to the reduction of support vector number , the classification time was also reduced.When sample aliasing existed , the aliasing sample points which were not of the same class were eliminated before the relative boundary vectors were computed.Besides , the samples near the relative boundary vectors were selected for SVM training in order to prevent the loss of some key sample points resulted from aliasing.This can improve classification accuracy effectively.A simulation example to classify 5classes of combination fault of aero-engine gas path components was finished and the total fault classification accuracy reached 96.1%.Simulation results show that this fast learning algorithm is effective , reliable and easy to be implemented for engineering application.

  16. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

    Directory of Open Access Journals (Sweden)

    HungLinh Ao

    2014-01-01

    Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

  17. Study on nature of crossover phenomena with application to gearbox fault diagnosis

    Science.gov (United States)

    Jiang, Xingxing; Li, Shunming; Wang, Yong

    2017-01-01

    Detrended Fluctuation Analysis (DFA) is a robust tool for uncovering long-range correlations hidden in the non-stationary data. Recently, crossover properties of the scaling-law curve obtained by DFA have been applied to diagnose gearbox faults. However, the nature of the crossover phenomena has not been well- explained. In this paper, an explanation for the nature of crossover phenomena is specifically given, which is conducive to discovering novel features for gearbox fault diagnosis. Firstly, an explicit exposition of the crossover phenomena is provided by analyzing the gearbox vibration signal. Secondly, the nature of crossover phenomena is specifically disclosed. Thirdly, the features with clear physical meaning are proposed to describe operating conditions of a gearbox. Then, to overcome the deficiency of feature extraction through visual observation, a piecewise-linear regression model is utilized to extract the features automatically. Lastly, several combinations of these features are used to classify the fault types. As a consequence, the proposed novel features are verified that they can well- distinguish the gearbox operating conditions with different fault types and severities, and deliver a better performance than the existing method depending on the sensitive index (SI).

  18. Online Test and Fault Diagnosis of Yarn Quality Using Wavelet Analysis And FFT

    Institute of Scientific and Technical Information of China (English)

    HONG Xi-jun; QiU Hao-bo; LI Yu-ming; LI Cong-xin

    2002-01-01

    A new online system of monitoring yarn quality and fault diagnosis is presented. This system integrates the technologies of sensor, signal process, commuunication,network, computer, control, instrument structure and mass knowledge of expert. Comparing with conventional off. Line yarn test, the new systemcan find the quality defects of yarn online in time and compensate for the lack of expert knowledge in manual analysis. It can save a lot of yarn wasted in off- line test and improve product quality. By using laser sensor to sample the diameter signal of yarn and doing wavelet analysis and FFT to extract fault characteristics, a set of reasoning mechanism is established to aralyze yarn quality and locate the fault origination. The experimental results show that new system can do well in monitoring yarn quality online comparing with conventional off-line yarn test. It can test the quality of yarn in real-time with high efficiency and analyze the fault reason accurately. It is very useful to apply this new system to upgrade yarn quality in cotton textile industry at present.

  19. Active faulting in apparently stable peninsular India: Rift inversion and a Holocene-age great earthquake on the Tapti Fault

    Science.gov (United States)

    Copley, Alex; Mitra, Supriyo; Sloan, R. Alastair; Gaonkar, Sharad; Reynolds, Kirsty

    2014-08-01

    We present observations of active faulting within peninsular India, far from the surrounding plate boundaries. Offset alluvial fan surfaces indicate one or more magnitude 7.6-8.4 thrust-faulting earthquakes on the Tapti Fault (Maharashtra, western India) during the Holocene. The high ratio of fault displacement to length on the alluvial fan offsets implies high stress-drop faulting, as has been observed elsewhere in the peninsula. The along-strike extent of the fan offsets is similar to the thickness of the seismogenic layer, suggesting a roughly equidimensional fault rupture. The subsiding footwall of the fault is likely to have been responsible for altering the continental-scale drainage pattern in central India and creating the large west flowing catchment of the Tapti river. A preexisting sedimentary basin in the uplifting hanging wall implies that the Tapti Fault was active as a normal fault during the Mesozoic and has been reactivated as a thrust, highlighting the role of preexisting structures in determining the rheology and deformation of the lithosphere. The slip sense of faults and earthquakes in India suggests that deformation south of the Ganges foreland basin is driven by the compressive force transmitted between India and the Tibetan Plateau. The along-strike continuation of faulting to the east of the Holocene ruptures we have studied represents a significant seismic hazard in central India.

  20. The relationship of near-surface active faulting to megathrust splay fault geometry in Prince William Sound, Alaska

    Science.gov (United States)

    Finn, S.; Liberty, L. M.; Haeussler, P. J.; Northrup, C.; Pratt, T. L.

    2010-12-01

    We interpret regionally extensive, active faults beneath Prince William Sound (PWS), Alaska, to be structurally linked to deeper megathrust splay faults, such as the one that ruptured in the 1964 M9.2 earthquake. Western PWS in particular is unique; the locations of active faulting offer insights into the transition at the southern terminus of the previously subducted Yakutat slab to Pacific plate subduction. Newly acquired high-resolution, marine seismic data show three seismic facies related to Holocene and older Quaternary to Tertiary strata. These sediments are cut by numerous high angle normal faults in the hanging wall of megathrust splay. Crustal-scale seismic reflection profiles show splay faults emerging from 20 km depth between the Yakutat block and North American crust and surfacing as the Hanning Bay and Patton Bay faults. A distinct boundary coinciding beneath the Hinchinbrook Entrance causes a systematic fault trend change from N30E in southwestern PWS to N70E in northeastern PWS. The fault trend change underneath Hinchinbrook Entrance may occur gradually or abruptly and there is evidence for similar deformation near the Montague Strait Entrance. Landward of surface expressions of the splay fault, we observe subsidence, faulting, and landslides that record deformation associated with the 1964 and older megathrust earthquakes. Surface exposures of Tertiary rocks throughout PWS along with new apatite-helium dates suggest long-term and regional uplift with localized, fault-controlled subsidence.

  1. An improved particle filtering algorithm for aircraft engine gas-path fault diagnosis

    Directory of Open Access Journals (Sweden)

    Qihang Wang

    2016-07-01

    Full Text Available In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure, which adjusts the position of the particles through simulating attraction–repulsion mechanism between charged particles of the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the diversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement information. Comparison of the proposed method with three different filter algorithms is carried out on a univariate nonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal particle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period and the root mean square error of parameters estimation is reduced.

  2. Rotating machine fault diagnosis through enhanced stochastic resonance by full-wave signal construction

    Science.gov (United States)

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

    2017-02-01

    This study proposes a full-wave signal construction (FSC) strategy for enhancing rotating machine fault diagnosis by exploiting stochastic resonance (SR). The FSC strategy is utilized to transform a half-wave signal (e.g., an envelope signal) into a full-wave one by conducting a Mirror-Cycle-Add (MCA) operation. The constructed full-wave signal evenly modulates the bistable potential and makes the potential tilt back and forth smoothly. This effect provides the equivalent transition probabilities of particle bounce between the two potential wells. A stable SR output signal with better periodicity, which is beneficial to periodic signal detection, can be obtained. In addition, the MCA operation can improve the input signal-to-noise ratio by enhancing the periodic component while attenuating the noise components. These two advantages make the proposed FSCSR method surpass the traditional SR method in fault signal processing. Performance evaluation is conducted by numerical analysis and experimental verification. The proposed MCA-based FSC strategy has the potential to be a universal signal pre-processing technique. Moreover, the proposed FSCSR method can be used in rotating machine fault diagnosis and other areas related to weak signal detection.

  3. Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes

    Science.gov (United States)

    Lei, Yaguo; Liu, Zongyao; Lin, Jing; Lu, Fanbo

    2016-05-01

    Condition monitoring and fault diagnosis of epicyclic gearboxes using vibration signals are not as straightforward as that of fixed-axis gearboxes since epicyclic gearboxes behave quite differently from fixed-axis gearboxes in many aspects, like spectral structures. Aiming to present the spectral structures of vibration signals of epicyclic gearboxes, phenomenological models of vibration signals of epicyclic gearboxes are developed by algebraic equations and spectral structures of these models are deduced using Fourier series analysis. In the phenomenological models, all the possible vibration transfer paths from gear meshing points to a fixed transducer and the effects of angular shifts of planet gears on the spectral structures are considered. Accordingly, time-varying vibration transfer paths from sun-planet/ring-planet gear meshing points to the fixed transducer due to carrier rotation are given by window functions with different amplitudes. And an angular shift in one planet gear position is introduced in the process of modeling. After the theoretical derivations, three experiments are conducted on an epicyclic gearbox test rig and the spectral structures of collected vibration signals are analyzed. As a result, the effects of angular shifts of planet gears are verified, and the phenomenological models of vibration signals when a local fault occurs on the sun gear and the planet gear are validated, respectively. The experiment results demonstrate that the established phenomenological models in this paper are helpful to the condition monitoring and fault diagnosis of epicyclic gearboxes.

  4. From Fault-Diagnosis and Performance Recovery of a Controlled System to Chaotic Secure Communication

    Science.gov (United States)

    Hsu, Wen-Teng; Tsai, Jason Sheng-Hong; Guo, Fang-Cheng; Guo, Shu-Mei; Shieh, Leang-San

    Chaotic systems are often applied to encryption on secure communication, but they may not provide high-degree security. In order to improve the security of communication, chaotic systems may need to add other secure signals, but this may cause the system to diverge. In this paper, we redesign a communication scheme that could create secure communication with additional secure signals, and the proposed scheme could keep system convergence. First, we introduce the universal state-space adaptive observer-based fault diagnosis/estimator and the high-performance tracker for the sampled-data linear time-varying system with unanticipated decay factors in actuators/system states. Besides, robustness, convergence in the mean, and tracking ability are given in this paper. A residual generation scheme and a mechanism for auto-tuning switched gain is also presented, so that the introduced methodology is applicable for the fault detection and diagnosis (FDD) for actuator and state faults to yield a high tracking performance recovery. The evolutionary programming-based adaptive observer is then applied to the problem of secure communication. Whenever the tracker induces a large control input which might not conform to the input constraint of some physical systems, the proposed modified linear quadratic optimal tracker (LQT) can effectively restrict the control input within the specified constraint interval, under the acceptable tracking performance. The effectiveness of the proposed design methodology is illustrated through tracking control simulation examples.

  5. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  6. Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

    Institute of Scientific and Technical Information of China (English)

    孙玉山; 李岳明; 张国成; 张英浩; 吴海波

    2016-01-01

    Autonomous underwater vehicles (AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model (estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

  7. Static-deformation based fault diagnosis for damping spring of large vibrating screen

    Institute of Scientific and Technical Information of China (English)

    彭利平; 刘初升; 李珺; 王宏

    2014-01-01

    Based on the statics theory, a novel and feasible twice-suspended-mass method (TSMM) was proposed to deal with the seldom-studied issue of fault diagnosis for damping springs of large vibrating screen (LVS). With the static balance characteristic of the screen body/surface as well as the deformation compatibility relation of springs considered, static model of the screen surface under a certain load was established to calculate compression deformation of each spring. Accuracy of the model was validated by both an experiment based on the suspended mass method and the properties of the 3D deformation space in a numerical simulation. Furthermore, by adopting the Taylor formula and the control variate method, quantitative relationship between the change of damping spring deformation and the change of spring stiffness, defined as the deformation sensitive coefficient (DSC), was derived mathematically, from which principle of the TSMM for spring fault diagnosis is clarified. In the end, an experiment was carried out and results show that the TSMM is applicable for diagnosing the fault of single spring in a LVS.

  8. Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

    Directory of Open Access Journals (Sweden)

    Yuning Qian

    2016-01-01

    Full Text Available This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA, for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA. The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA and empirical mode decomposition (EMD, as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis.

  9. Fault Diagnosis of Automobile Crane Power Steering System Aided by ICP-AES

    Directory of Open Access Journals (Sweden)

    Lidan Chen

    2013-01-01

    Full Text Available The objective of this paper is to evaluate an innovative application of inductively coupled plasma atomic emission spectroscopy (ICP-AES on the fault diagnosis of automobile crane hydraulic power steering (HPS system. Contents of Fe, Cu and Al were examined by ICP-AES in the oil samples of HPS system for four different mileages of Puyuan QY50H. The mileages were 2000-9000 km, 11000-19000 km, 21000-28000 km and 32000-40000 km separately. Database of major mental contents in automobile crane HPS system of Puyuan QY50H with different mileage were calibrated. Results showed that, major mental contents were increased with the increasing of driving mileage and the normal contents laid between two trend lines. Through the determination of mental contents in HPS oil sample and further compared them with the values in their database, we could not only evaluate the wear condition of automobile crane HPS system, but also helped to diagnose the faults without dissembled the problematic vehicle. The results further indicated that, in time maintenance, high quality and low cost reparation could be realized by the application of ICP-AES technology on fault diagnosis of automobile crane power steering system.

  10. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  11. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zappala, D.; Tavner, P.; Crabtree, C.; Sheng, S.

    2013-01-01

    Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data represent one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.

  12. Rotating machinery fault diagnosis based on multiple fault manifolds%基于多故障流形的旋转机械故障诊断

    Institute of Scientific and Technical Information of China (English)

    苏祖强; 汤宝平; 赵明航; 秦毅

    2015-01-01

    针对旋转机械不同故障可能分布于不同故障流形,提出了基于多故障流形的旋转机械故障诊断方法。该方法分别提取每一类故障对应的故障流形,并在多故障流形上进行新增样本的故障识别。针对所需解决的低维流形提取、流形内蕴维数选取和多故障流形上的故障识别问题,分别采用线性局部切空间排列算法和免疫遗传算法来进行低维故障流形提取和流形内蕴维数选取,并通过故障样本重构误差这一新的判别准则进行故障识别。齿轮箱故障模拟实验的结果验证了此方法的有效性。%The existing fault diagnosis methods based on manifold learning assume that all the faults distribute on a single mani-fold,however the faults may distribute on different manifolds in practical applications.Aiming at this problem,rotating ma-chinery fault diagnosis method based on multiple fault manifolds is proposed.Firstly,mixed-domain features are extracted from the vibration signals to characterize the property of the faults,and the vibration signals are also preprocessed by empirical model decomposition before feature extraction.Then,the corresponding fault manifold of each fault is extracted from the high-dimensional fault samples.In the method,linear local tangent space alignment is applied to solve the problem of low-dimen-sional manifold extraction,and immune genetic algorithm is used to select the intrinsic dimensionality of fault manifold.At last,the test samples are respectively projected to all the fault manifolds,and the projection errors are used as the criterion to determine the fault types of the test samples.In order to verify the effectiveness of the proposed fault diagnosis method,the method is applied to diagnose the faults of the gear box.The experimental results indicate that feature compression can remove the redundant information between features,and moreover fault diagnosis method based on multiple fault

  13. Research Artillery Automatic Machine Fault Diagnosis%火炮自动机故障诊断研究综述

    Institute of Scientific and Technical Information of China (English)

    武春林; 潘宏侠

    2013-01-01

    In the paper the authors narrated the significance of the automatic mechanism of artillery fault diagnosis, analyzed the normal faults , and introduced the methods about automatic mechanism of artillery work status monitoring and fault diagnosis in common use, and its principle and characteristics. The difficulties and the developing direction of the automatic mechanism of artillery fault diagnosis were put for⁃ward in the end.%  叙述了火炮自动机故障诊断的意义,对火炮自动机常见的故障进行了分析,并介绍了国内外一些常见的火炮自动机状态监测与故障诊断的方法及其原理和特点,最后提出了火炮自动机的故障诊断的难点和发展方向。

  14. Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC

    Directory of Open Access Journals (Sweden)

    Chin-Tsung Hsieh

    2013-01-01

    Full Text Available This study proposes a method based on the cerebellar model arithmetic controller (CMAC for fault diagnosis of large-scale permanent-magnet wind power generators and compares the results with Error Back Propagation (EBP. The diagnosis is based on the short-circuit faults in permanent-magnet wind power generators, magnetic field change, and temperature change. Since CMAC is characterized by inductive ability, associative ability, quick response, and similar input signals exciting similar memories, it has an excellent effect as an intelligent fault diagnosis implement. The experimental results suggest that faults can be diagnosed effectively after only training CMAC 10 times. In comparison to training 151 times for EBP, CMAC is better than EBP in terms of training speed.

  15. Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Junsheng Cheng

    2009-01-01

    Full Text Available Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD techniques, the SVD technique based on empirical mode decomposition (EMD is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

  16. Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2015-01-01

    Full Text Available Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs by using local characteristic-scale decomposition (LCD method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM classifier based on Chemical Reaction Optimization (CRO algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

  17. A Method for Incipient Fault Diagnosis of Roller Bearings Based on the Wavelet Transform Correlation Filter and Hilbert Transform

    Institute of Scientific and Technical Information of China (English)

    ZENG Qing-hu; QIU Jing; LIU Guan-jun

    2007-01-01

    Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.

  18. High-Speed Spindle Fault Diagnosis with the Empirical Mode Decomposition and Multiscale Entropy Method

    Directory of Open Access Journals (Sweden)

    Nan-Kai Hsieh

    2015-04-01

    Full Text Available The root mean square (RMS value of a vibration signal is an important indicator used to represent the amplitude of vibrations in evaluating the quality of high-speed spindles. However, RMS is unable to detect a number of common fault characteristics that occur prior to bearing failure. Extending the operational life and quality of spindles requires reliable fault diagnosis techniques for the analysis of vibration signals from three axes. This study used empirical mode decomposition to decompose signals into intrinsic mode functions containing a zero-crossing rate and energy to represent the characteristics of rotating elements. The MSE curve was then used to identify a number of characteristic defects. The purpose of this research was to obtain vibration signals along three axes with the aim of extending the operational life of devices included in the product line of an actual spindle manufacturing company.

  19. Fault diagnosis of a mine hoist using PCA and SVM techniques

    Institute of Scientific and Technical Information of China (English)

    CHANG Yan-wei; WANG Yao-cai; LIU Tao; WANG Zhi-jie

    2008-01-01

    A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.

  20. Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jianwei Cui

    2014-01-01

    Full Text Available This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB with support vector machine (SVM. The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT- based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

  1. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis.

    Science.gov (United States)

    Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

    2016-04-16

    Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods.

  2. Electro-pump Fault Diagnosis of Marine Ship by Vibration Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Payman Salami

    2010-05-01

    Full Text Available The objective of this research is to investigate the correlation between vibration analysis and fault diagnosis. This was achieved by vibration analysis of an electro-pump of marine ship. The vibration analysis was initially run under regular interval during electro-pump life. Some series of tests were then conducted under the operating hours of stone crasher. Vibration data was regularly collected. The overall vibration data produced by vibration analysis was compared with previous data, in order to quantify the effectiveness of the results of vibration condition monitoring technique. Numerical data produced by vibration analysis were compared with vibration spectra in standard condition of healthy machine, in order to quantify the effectiveness of the vibration condition monitoring technique. The results of this paper have given more understanding on the dependent roles of vibration analysis in predicting and diagnosing machine faults.

  3. A brief status on condition monitoring and fault diagnosis in wind energy conversion systems

    Energy Technology Data Exchange (ETDEWEB)

    Amirat, Y. [University of Brest, EA 4325 LBMS, 29238 Brest (France); University of Annaba, Electrical Engineering Department, 23000 Annaba (Algeria); Benbouzid, M.E.H.; Turri, S. [University of Brest, EA 4325 LBMS, 29238 Brest (France); Al-Ahmar, E. [University of Brest, EA 4325 LBMS, 29238 Brest (France); Holy Spirit University of Kaslik, Faculty of Sciences and Computer Engineering, BP 446 Jounieh (Lebanon); Bensaker, B. [University of Annaba, Electrical Engineering Department, 23000 Annaba (Algeria)

    2009-12-15

    There is a constant need for the reduction of operational and maintenance costs of Wind Energy Conversion Systems (WECSs). The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the degeneration of the generator health, facilitating a proactive response, minimizing downtime, and maximizing productivity. Wind generators are also inaccessible since they are situated on extremely high towers, which are normally 20 m or more in height. There are also plans to increase the number of offshore sites increasing the need for a remote means of WECS monitoring that eliminates some of the difficulties faced due to accessibility problems. Therefore and due to the importance of condition monitoring and fault diagnosis in WECS (blades, drive trains, and generators), and keeping in mind the need for future research, this paper is intended as a brief status describing different types of faults, their generated signatures, and their diagnostic schemes. (author)

  4. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

    Directory of Open Access Journals (Sweden)

    Jonguk Lee

    2016-04-01

    Full Text Available Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods.

  5. Fault Diagnosis with Multi-State Alarms in a Nuclear Power Control Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Stuart A. Ragsdale; Roger Lew; Ronald L. Boring

    2014-09-01

    This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effects of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized the use of three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. Sensitivity and criterion based on the Signal Detection Theory were used to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed better and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.

  6. FAULT DIAGNOSIS WITH MULTI-STATE ALARMS IN A NUCLEAR POWER CONTROL SIMULATOR

    Energy Technology Data Exchange (ETDEWEB)

    Austin Ragsdale; Roger Lew; Brian P. Dyre; Ronald L. Boring

    2012-10-01

    This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effect of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. We used sensitivity and criterion based on Signal Detection Theory to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed better and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.

  7. Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    Downhole abnormal incidents during oil and gas drilling causes costly delays, any may also potentially lead to dangerous scenarios. Dierent incidents willcause changes to dierent parts of the physics of the process. Estimating thechanges in physical parameters, and correlating these with changes...... expectedfrom various defects, can be used to diagnose faults while in development.This paper shows how estimated friction parameters and ow rates can de-tect and isolate the type of incident, as well as isolating the position of adefect. Estimates are shown to be subjected to non......-Gaussian,t-distributednoise, and a dedicated multivariate statistical change detection approach isused that detects and isolates faults by detecting simultaneous changes inestimated parameters and ow rates. The properties of the multivariate di-agnosis method are analyzed, and it is shown how detection and false alarmprobabilities...

  8. FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    LI Ru-qiang; CHEN Jin; WU Xing

    2006-01-01

    A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery.Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

  9. Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms.

    Science.gov (United States)

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-06-16

    Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

  10. Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

    Directory of Open Access Journals (Sweden)

    Chang Liu

    2014-06-01

    Full Text Available Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM and a differential evolution (DE algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

  11. Active faulting on the Wallula fault within the Olympic-Wallowa Lineament (OWL), eastern Washington State

    Science.gov (United States)

    Sherrod, B. L.; Lasher, J. P.; Barnett, E. A.

    2013-12-01

    Several studies over the last 40 years focused on a segment of the Wallula fault exposed in a quarry at Finley, Washington. The Wallula fault is important because it is part of the Olympic-Wallowa lineament (OWL), a ~500-km-long topographic and structural lineament extending from Vancouver Island, British Columbia to Walla Walla, Washington that accommodates Basin and Range extension. The origin and nature of the OWL is of interest because it contains potentially active faults that are within 50 km of high-level nuclear waste facilities at the Hanford Site. Mapping in the 1970's and 1980's suggested the Wallula fault did not offset Holocene and late Pleistocene deposits and is therefore inactive. New exposures of the Finley quarry wall studied here suggest otherwise. We map three main packages of rocks and sediments in a ~10 m high quarry exposure. The oldest rocks are very fine grained basalts of the Columbia River Basalt Group (~13.5 Ma). The next youngest deposits include a thin layer of vesicular basalt, white volcaniclastic deposits, colluvium containing clasts of vesicular basalt, and indurated paleosols. A distinct angular unconformity separates these vesicular basalt-bearing units from overlying late Pleistocene flood deposits, two colluvium layers containing angular clasts of basalt, and Holocene tephra-bearing loess. A tephra within the loess likely correlates to nearby outcrops of Mazama ash. We recognize three styles of faults: 1) a near vertical master reverse or oblique fault juxtaposing very fine grained basalt against late Tertiary-Holocene deposits, and marked by a thick (~40 cm) vertical seam of carbonate cemented breccia; 2) subvertical faults that flatten upwards and displace late Tertiary(?) to Quaternary(?) soils, colluvium, and volcaniclastic deposits; and 3) flexural slip faults along bedding planes in folded deposits in the footwall. We infer at least two Holocene earthquakes from the quarry exposure. The first Holocene earthquake deformed

  12. Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jianyu; GAO Lixin; CUI Lingli; LI Xianghui; WANG Yingwang

    2006-01-01

    Based on the internet technology, it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine. In order to capture the micro-shock signal induced by the incipient fault on the rotating parts, the resonance demodulation technology is utilized in the system. As a subsystem of the remote monitoring system, the embedded data acquisition instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines. Furthermore, through connecting to the internet, the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database. At the same time, the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology. Finally, the remote diagnosis software developed on the LabVIEW platform can analyze the monitoring data from manufacturing field. The research results have indicated that the equipment status can be monitored by the system effectively.

  13. Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model

    Directory of Open Access Journals (Sweden)

    Jiming Ma

    2015-01-01

    Full Text Available The diagnosis procedure is performed by integrating three steps: multidomain modeling, event identification, and failure event classification. Multidomain model can describe the normal and fault behaviors of hybrid systems efficiently and can meet the diagnosis requirements of hybrid systems. Then the multidomain model is used to simulate and obtain responses under different failure events; the responses are further utilized as a priori information when training the event identification library. Finally, a brushless DC motor is selected as the study case. The experimental result indicates that the proposed method could identify the known and unknown failure events of the studied system. In particular, for a system with less response information under a failure event, the accuracy of diagnosis seems to be higher. The presented method integrates the advantages of current quantitative and qualitative diagnostic procedures and can distinguish between failures caused by parametric and abrupt structure faults. Another advantage of our method is that it can remember unknown failure types and automatically extend the adaptive resonance theory neural network library, which is extremely useful for complex hybrid systems.

  14. Stochastic Resonance with a Joint Woods-Saxon and Gaussian Potential for Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2014-01-01

    Full Text Available This work aims for a new stochastic resonance (SR model which performs well in bearing fault diagnosis. Different from the traditional bistable SR system, we realize the SR based on the joint of Woods-Saxon potential (WSP and Gaussian potential (GP instead of a reflection-symmetric quartic potential. With this potential model, all the parameters in the Woods-Saxon and Gaussian SR (WSGSR system are not coupled when compared to the traditional one, so the output signal-to-noise ratio (SNR can be optimized much more easily by tuning the system parameters. Besides, a smoother potential bottom and steeper potential wall lead to a stable particle motion within each potential well and avoid the unexpected noise. Different from the SR with only WSP which is a monostable system, we improve it into a bistable one as a general form offering a higher SNR and a wider bandwidth. Finally, the proposed model is verified to be outstanding in weak signal detection for bearing fault diagnosis and the strategy offers us a more effective and feasible diagnosis conclusion.

  15. Fault activation by hydraulic fracturing in western Canada

    Science.gov (United States)

    Bao, Xuewei; Eaton, David W.

    2016-12-01

    Hydraulic fracturing has been inferred to trigger the majority of injection-induced earthquakes in western Canada, in contrast to the Midwestern United States, where massive saltwater disposal is the dominant triggering mechanism. A template-based earthquake catalog from a seismically active Canadian shale play, combined with comprehensive injection data during a 4-month interval, shows that earthquakes are tightly clustered in space and time near hydraulic fracturing sites. The largest event [moment magnitude (MW) 3.9] occurred several weeks after injection along a fault that appears to extend from the injection zone into crystalline basement. Patterns of seismicity indicate that stress changes during operations can activate fault slip to an offset distance of >1 km, whereas pressurization by hydraulic fracturing into a fault yields episodic seismicity that can persist for months.

  16. Sliding mode fault detection and fault-tolerant control of smart dampers in semi-active control of building structures

    Science.gov (United States)

    Yeganeh Fallah, Arash; Taghikhany, Touraj

    2015-12-01

    Recent decades have witnessed much interest in the application of active and semi-active control strategies for seismic protection of civil infrastructures. However, the reliability of these systems is still in doubt as there remains the possibility of malfunctioning of their critical components (i.e. actuators and sensors) during an earthquake. This paper focuses on the application of the sliding mode method due to the inherent robustness of its fault detection observer and fault-tolerant control. The robust sliding mode observer estimates the state of the system and reconstructs the actuators’ faults which are used for calculating a fault distribution matrix. Then the fault-tolerant sliding mode controller reconfigures itself by the fault distribution matrix and accommodates the fault effect on the system. Numerical simulation of a three-story structure with magneto-rheological dampers demonstrates the effectiveness of the proposed fault-tolerant control system. It was shown that the fault-tolerant control system maintains the performance of the structure at an acceptable level in the post-fault case.

  17. Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

    Science.gov (United States)

    Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)

    1994-01-01

    When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be

  18. Geophysical survey on trench excavation of active faults; Butsuri tansa to katsudanso trench chosa

    Energy Technology Data Exchange (ETDEWEB)

    Kanayama, S.; Hasegawa, S.; Tsuruta, S. [Shikoku Research Inst. Inc., Kagawa (Japan); Kawakami, H. [Yonden Consultants Co. Inc., Kagawa (Japan)

    1997-12-01

    This paper describes cases of geophysical survey used for investigation on a few active faults, and future requirements thereof to help develop active fault surveys. Seismic exploration using the reflection method on the Nagao fault revealed distinct existence of a reverse fault with southward inclination of about 50 degrees. A crush zone caused by this fault was recognized also in the granite base. A few small crush zones in reverse direction to the main fault were found in granite in upper base of the fault, which were thought secondary to activities of the main fault. Seismic exploration using the reflection method was performed on the Iyo fault in the central tectonic line to identify underground structures of the Iyo fault and the Gunchu fault, by which the location of the Iyo fault was verified. The Chichio fault in the central tectonic line was explored by using the {rho}a-{rho}u method, and the Okamura fault in the central tectonic line by using the specific resistance imaging method. The length of a fault per action, which is always a problem, or the problem of fault groups acting associatively could not be discussed if structural analysis of ground of great depths is omitted, when estimating scales of earthquakes from active faults. 18 refs., 10 figs.

  19. Robust and Active Fault-tolerant Control for a Class of Nonlinear Uncertain Systems

    Institute of Scientific and Technical Information of China (English)

    You-Qing Wang; Dong-Hua Zhou; Li-Heng Liu

    2006-01-01

    A novel integrated design strategy for robust fault diagnosis and fault-tolerant control (FTC) of a class of nonlinear uncertain systems is proposed. The uncertainties considered in this paper are more general than those in other existing works, and faults are described in a new formulation. It is proven that the states of a closed-loop system converge asymptotically to zero even if there are uncertainties and faults in a system. Simulation results on a simple pendulum are presented for illustration.

  20. Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace%Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

    Institute of Scientific and Technical Information of China (English)

    LIU Li-mei; WANG An-na; SHA Mo; ZHAO Feng-yun

    2011-01-01

    Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine (LS-SVM) is proposed to solve this problem. Firstly, modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Secondly, cost-con- scious formula is presented for fitness function and it contains in detail training time, recognition accuracy and the feature selection. The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier. The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general- ization performance in the application of fault diagnosis of the blast furnace. Thirdly, a gradual change binary tree is established for blast furnace faults diagnosis. It is a multi-class classification method based on center-of-gravity formula distance of cluster. A gradual change classification percentage ia used to select sample randomly. The proposed new metbod raises the sped of diagnosis, optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace.