Sample records for vibration fault diagnosis

  1. Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals

    Microsoft Academic Search

    Jian-Da Wu; Chao-Qin Chuang

    2005-01-01

    An investigation of the fault diagnosis technique in internal combustion engines based on the visual dot pattern of acoustic and vibration signals is presented in this paper. Acoustic emissions and vibration signals are well known as being able to be used for monitoring the conditions of rotating machineries. Most of the conventional methods for fault diagnosis using acoustic and vibration

  2. A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and

    E-print Network

    Yang, Zhenyu

    A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and Current Signatures detection and diagnosis for a class of rolling-element bearings using signal-based methods based the powerful capability of vibration analysis in the bearing point-defect fault diagnosis under stationary

  3. A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and Current Signatures

    Microsoft Academic Search

    Zhenyu Yang; Uffe C. Merrild; Morten T. Runge; Gerulf Pedersen; Hakon Børsting

    This paper investigates the fault detection and diagnosis for a class of rolling-element bearings using signal-based methods based on the motor's vibration and phase current measurements, respectively. The envelope detection method is employed to preprocess the measured vibration data before the FFT algorithm is used for vibration analysis. The average of a set of Short- Time FFT (STFFT) is used

  4. Enhanced eigenvector algorithm for recovering multiple sources of vibration signals in machine fault diagnosis

    NASA Astrophysics Data System (ADS)

    Tse, P. W.; Gontarz, S.; Wang, X. J.

    2007-10-01

    Many advanced techniques have been developed for vibration-based machine fault diagnosis. One of the prerequisites to use vibration for fault diagnosis is the vibration signal measured from a machine component must be well isolated from other vibrations that are generated by adjacent components. Many machines have numerous and small components that are closely packed together. Due to limited space or accessibility for installing sensors on the inspected machine component, sometimes only one sensor is allowed to be installed. An aggregated source of vibrations could be collected rather than just the vibration generated by the inspected component. Hence, an effective algorithm must be employed to recover the desired vibration out of the aggregated source of vibrations. The blind equalization-(BE)based eigenvector algorithm (EVA) has proven its effectiveness in recovering the overwhelmed vibration signal in the application of machine fault diagnosis. However, the conventional type of EVA can recover only one dominant source from the aggregated vibration. This dominant vibration may belong to the larger vibration generated by the inspected component or a nearby component. Hence, the ability of EVA in recovering signals besides the dominant signal is deemed necessary. In this paper, we proposed an enhanced EVA that consists of channel extension and a post-processing method to recover multiple sources of vibrations. The post-processing method includes the use of correlation and higher order statistics. With the help of these proposed algorithms, the enhanced EVA can recover other vibrations that are less dominant but highly relevant to existing faults. To verify its effectiveness, the ability of recovering the overwhelmed bearing faulty vibration is demonstrated. The results of the experiments using simulated signals and real machine vibrations have proven the effectiveness of the method. Hence, the enhanced EVA is suitable for vibration-based fault diagnosis on machines that have many closely packed components.

  5. Vibration analysis with lifting scheme and generalized cross validation in fault diagnosis of water hydraulic system

    Microsoft Academic Search

    H. X. Chen; Patrick S. K. Chua; G. H. Lim

    2007-01-01

    This paper presents a novel method to analyze the vibration signals in the fault diagnosis of water hydraulic motor. The method of feature extraction from the vibration signals of the water hydraulic motor based on the second-generation wavelet is investigated. The second-generation wavelet consists of a lifting scheme. The algorithm and method of multi-decomposition based on the lifting scheme for

  6. Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

    Microsoft Academic Search

    N. R. Sakthivel; V. Sugumaran; S. Babudevasenapati

    2010-01-01

    Monoblock centrifugal pumps are widely used in a variety of applications. In many applications the role of monoblock centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly artificial neural networks, fuzzy logic were employed for continuous monitoring and fault diagnosis. This paper presents the use of

  7. USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—I. BASIC CONCEPTS

    Microsoft Academic Search

    David Logan; Joseph Mathew

    1996-01-01

    There is a wide variety of condition monitoring techniques currently in use for the diagnosis and prediction of machinery faults, but little attention has been paid to the occurrence and detection of chaotic behaviour in time series vibration signals. This paper introduces some of the basic concepts of chaos theory, then details a method for quantifying a fractal dimension from

  8. Diagnosis of Centrifugal Pump Faults Using Vibration Methods

    NASA Astrophysics Data System (ADS)

    Albraik, A.; Althobiani, F.; Gu, F.; Ball, A.

    2012-05-01

    Pumps are the largest single consumer of power in industry. This means that faulty pumps cause a high rate of energy loss with associated performance degradation, high vibration levels and significant noise radiation. This paper investigates the correlations between pump performance parameters including head, flow rate and energy consumption and surface vibration for the purpose of both pump condition monitoring and performance assessment. Using an in-house pump system, a number of experiments have been carried out on a centrifugal pump system using five impellers: one in good condition and four others with different defects, and at different flow rates for the comparison purposes. The results have shown that each defective impeller performance curve (showing flow, head, efficiency and NPSH (Net Positive Suction Head) is different from the benchmark curve showing the performance of the impeller in good condition. The exterior vibration responses were investigated to extract several key features to represent the healthy pump condition, pump operating condition and pump energy consumption. In combination, these parameter allow an optimal decision for pump overhaul to be made [1].

  9. Fault diagnosis

    NASA Technical Reports Server (NTRS)

    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 examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

  10. Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms.

    PubMed

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-01-01

    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. PMID:24936949

  11. Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

    PubMed Central

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-01-01

    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. PMID:24936949

  12. Using the Correlation Dimension for Vibration Fault Diagnosis of Rolling Element BEARINGS—I. Basic Concepts

    NASA Astrophysics Data System (ADS)

    Logan, David; Mathew, Joseph

    1996-05-01

    There is a wide variety of condition monitoring techniques currently in use for the diagnosis and prediction of machinery faults, but little attention has been paid to the occurrence and detection of chaotic behaviour in time series vibration signals. This paper introduces some of the basic concepts of chaos theory, then details a method for quantifying a fractal dimension from a time series, the correlation dimension. Some of the practical difficulties encountered in measuring the correlation dimension from the correlation integral algorithm are also outlined. Finally, some experimental results from a rolling element bearing test rig are presented.

  13. Application of ANN, Fuzzy Logic and Wavelet Transform in machine fault diagnosis using vibration signal analysis

    Microsoft Academic Search

    Pratesh Jayaswal; S. N. Verma; A. K. Wadhwani

    2010-01-01

    Purpose – The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition-monitoring system. Design\\/methodology\\/approach – A detailed

  14. Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis.

    PubMed

    Saidi, Lotfi; Ali, Jaouher Ben; Fnaiech, Farhat

    2014-09-01

    Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. PMID:24975564

  15. Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique

    Microsoft Academic Search

    N. Saravanan; S. Cholairajan; K. I. Ramachandran

    2009-01-01

    To determine the condition of an inaccessible gear in an operating machine the vibration signal of the machine can be continuously monitored by placing a sensor close to the source of the vibrations. These signals can be further processed to extract the features and identify the status of the machine. The vibration signal acquired from the operating machine has been

  16. Wavelet packet feature extraction for vibration monitoring and fault diagnosis of turbo-generator

    Microsoft Academic Search

    Jun Zhang; Rui-Xin Li; Pu Han; Dong-Feng Wang; Xi-Chao Yin

    2003-01-01

    Condition monitoring of turbo-generator systems based on vibration signatures has generally relied upon Fourier-based analysis as a means of translating vibration signals in the time domain into the frequency domain. However, Fourier analysis provided a poor representation of signals well localized in time. In this case, it is difficult to detect and identify the signal pattern from the expansion coefficients

  17. Acoustic Emission, Cylinder Pressure and Vibration: A Multisensor Approach to Robust Fault Diagnosis \\Lambda

    E-print Network

    Sharkey, Amanda

    in the combustion process of an internal combus­ tion piston engine, malfunctions, this malfunction may be reflected, University of Sheffield, U.K. amanda@dcs.shef.ac.uk Abstract: When an engine component participating of a diesel engine and 4 fault conditions, Artificial Neural Nets based on data from any one of these three

  18. Bearing fault diagnosis based on rough set

    Microsoft Academic Search

    Chen Xin; Yuhua Chen; Guofeng Wang; Hu Dong

    2010-01-01

    Bearing defects were categorized as localized and distributed. For on-line bearing fault diagnosis, in this paper, the time-domain kurtosis calculation and the frequency domain wavelet analysis were used to extract the transitory features of non-stationary vibration signal produced by the bearing distributed defects. To distributed defects, bearing fault diagnosis was built on the reducing decision based on rough set. According

  19. APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION

    Microsoft Academic Search

    W. J. Wang; P. D. McFadden

    1996-01-01

    The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gearbox. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal from a helicopter gearbox is used to demonstrate the application of the suggested wavelet by a simple computer algorithm. The

  20. Fault diagnosis for bearing based on Mahalanobis-Taguchi system

    Microsoft Academic Search

    Zhipeng Wang; Zili Wang; Laifa Tao; Jian Ma

    2012-01-01

    This paper presents a method for fault diagnosis based on Mahalanobis-Taguchi system (MTS), which is applied to practical fault diagnosis for rolling element bearing. Firstly, this method utilizes time\\/frequency domain analysis for feature extraction from the vibration data. Then, a computational scheme based on Mahalanobis distance (MD) is used for fault clustering. In addition, Taguchi methods are employed to reduce

  1. Review of fault diagnosis in control systems

    Microsoft Academic Search

    Aishe Shui; Weimin Chen; Peng Zhang; Shunren Hu; Xiaowei Huang

    2009-01-01

    In this paper, we review the major achievements on the research of fault diagnosis in control systems (FDCS) from three aspects which including fault detection, fault isolation and hybrid intelligent fault diagnosis. Fault detection and isolation (FDI) are two important stages in the diagnosis process while hybrid intelligent fault diagnosis is the hot issue in current research field. The particular

  2. Applications of Fault Detection in Vibrating Structures

    NASA Technical Reports Server (NTRS)

    Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

    2012-01-01

    Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

  3. Wavelet Decomposition for the Detection and Diagnosis of Faults in Rolling Element Bearings

    Microsoft Academic Search

    J. Chebil; G. Noel; M. Mesbah; M. Deriche

    2009-01-01

    Condition monitoring and fault diagnosis of equipment and processes are of great concern in industries. Early fault detection in machineries can save millions of dollars in emergency maintenance costs. This paper presents a wavelet-based analysis technique for the diagnosis of faults in rotating machinery from its mechanical vibrations. The choice between the discrete wavelet transform and the discrete wavelet packet

  4. Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

    Microsoft Academic Search

    Jian-Da Wu; Jien-Chen Chen

    2006-01-01

    A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for fault diagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and

  5. Fault Diagnosis with Dynamic Observers

    E-print Network

    Cassez, Franck

    2010-01-01

    In this paper, we review some recent results about the use of dynamic observers for fault diagnosis of discrete event systems. Fault diagnosis consists in synthesizing a diagnoser that observes a given plant and identifies faults in the plant as soon as possible after their occurrence. Existing literature on this problem has considered the case of fixed static observers, where the set of observable events is fixed and does not change during execution of the system. In this paper, we consider dynamic observers: an observer can "switch" sensors on or off, thus dynamically changing the set of events it wishes to observe. It is known that checking diagnosability (i.e., whether a given observer is capable of identifying faults) can be solved in polynomial time for static observers, and we show that the same is true for dynamic ones. We also solve the problem of dynamic observers' synthesis and prove that a most permissive observer can be computed in doubly exponential time, using a game-theoretic approach. We furt...

  6. Remote fault diagnosis system based on DSP for rolling bearing

    Microsoft Academic Search

    Pan Hongxia; Gao Yang

    2010-01-01

    A online rolling bearing fault diagnosis system is designed using TI Company's digital signal processor DSP-TMS320F2812 as the core processor. The hardware is built by the various peripheral modules of F2812, which includes the acquisition of vibration signal, rotating-speed signal, digital signal, and communicates with the upper monitor by RS-232 protocol and TCP\\/IP protocol. According to the common faults occurred

  7. Vibration diagnosis and vibration source analysis of aircraft engine

    NASA Astrophysics Data System (ADS)

    Li, Xifa; Qiu, Lun; Yi, Juan; Meng, Zhaobing

    1990-07-01

    This paper reviews recent advances in aircraft-engine vibration monitoring and diagnosis in flight. An airborne vibration data acquisition unit, the ground analysis equipment, and the method for analyzing vibration signals are given. Applications prove that it is feasible to perform vibration signal recording and frequency spectrum analysis in flight.

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

    PubMed

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

    2015-01-01

    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. PMID:26153771

  9. Multiple manifolds analysis and its application to fault diagnosis

    Microsoft Academic Search

    Min Li; Jinwu Xu; Jianhong Yang; Debin Yang; Dadong Wang

    2009-01-01

    A novel approach to fault diagnosis is proposed using multiple manifolds analysis (MMA) to extract manifold information from the vibration signals collected from a mechanical system. The basic idea of MMA is to reconstruct a manifold by embedding time series into a high-dimensional phase space. The tangent direction of the neighborhood for each point is then used to approximate its

  10. Rolling element bearing fault diagnosis based on support vector machine

    Microsoft Academic Search

    Hong Zheng; Lei Zhou

    2012-01-01

    Rolling element bearings are widely used in industrial applications. This paper presents a fault diagnosis method for rolling element bearings based on support vector machine (SVM). Firstly, the features are extracted from the vibration signals by the five-level wavelet packet decomposition algorithm using db2 wavelet. Then, the principal component analysis (PCA) is performed for feature reduction. Secondly, the multiclass SVM

  11. Sensor Fault Diagnosis Using Principal Component Analysis

    E-print Network

    Sharifi, Mahmoudreza

    2010-07-14

    of sensor faults 3. A stochastic method for the decision process 4. A nonlinear approach to sensor fault diagnosis. In this study, first a geometrical approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction...

  12. Underground distribution cable incipient fault diagnosis system 

    E-print Network

    Jaafari Mousavi, Mir Rasoul

    2007-04-25

    This dissertation presents a methodology for an efficient, non-destructive, and online incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they become catastrophic. The system provides ...

  13. Immune Memory Network-Based Fault Diagnosis

    Microsoft Academic Search

    Lin Liang; Guanghua Xu; Tao Sun

    2006-01-01

    In this paper, based on artificial immune network, a novel approach to immune memory network-based fault diagnosis methodology for on-line fault diagnosis system is presented. The diagnosis scheme consists of the memory cell network and the antibody network. They are employed to work together for network establishment, immune identification and antibody learning. Meanwhile, the key parameters of the approach are

  14. Fault Diagnosis of Rolling Bearing Based on Wavelet Packet Transform and Support Vector Machine

    Microsoft Academic Search

    Yang Zhengyou; Peng Tao; Li Jianbao; Yang Huibin; Jiang Haiyan

    2009-01-01

    In this paper, fault diagnosis approach to rolling bearing based on wavelet packet transform and support vector machine is proposed. At first, feature vectors are extracted from the non-stationary vibration signals by means of wavelet packet transform. Then support vector machine algorithm is used to fault identification and classification of rolling bearing. The experiments show that, as for limited fault

  15. DIPLOMARBEIT Fault Injection for Diagnosis and Maintenance

    E-print Network

    view over the system, and analysis in order to assess the health state of the system. A fault injection- ficient for meaningful statistical analysis. An embedded application synchronizes the fault injectionDIPLOMARBEIT Fault Injection for Diagnosis and Maintenance in the Time-Triggered Architecture

  16. Sensor Fault Diagnosis Using Principal Component Analysis 

    E-print Network

    Sharifi, Mahmoudreza

    2010-07-14

    The purpose of this research is to address the problem of fault diagnosis of sensors which measure a set of direct redundant variables. This study proposes: 1. A method for linear senor fault diagnosis 2. An analysis of isolability and detectability...

  17. Detection of stator winding faults in induction machines using flux and vibration analysis

    NASA Astrophysics Data System (ADS)

    Lamim Filho, P. C. M.; Pederiva, R.; Brito, J. N.

    2014-01-01

    This work aims at presenting the detection and diagnosis of electrical faults in the stator winding of three-phase induction motors using magnetic flux and vibration analysis techniques. A relationship was established between the main electrical faults (inter-turn short circuits and unbalanced voltage supplies) and the signals of magnetic flux and vibration, in order to identify the characteristic frequencies of those faults. The experimental results showed the efficiency of the conjugation of these techniques for detection, diagnosis and monitoring tasks. The results were undoubtedly impressive and can be adapted and used in real predictive maintenance programs in industries.

  18. Fault diagnosis of analog circuits

    Microsoft Academic Search

    J. W. Bandler; A. E. Salama

    1985-01-01

    In this paper, various fault location techniques in analog networks are described and compared. The emphasis is on the more recent developments in the subject. Four main approaches for fault location are addressed, examined, and illustrated using simple network examples. In particular, we consider the fault dictionary approach, the parameter identification approach, the fault verification approach, and the approximation approach.

  19. Intelligent diagnosis method for a centrifugal pump using features of vibration signals

    Microsoft Academic Search

    Huaqing Wang; Peng Chen

    2009-01-01

    In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because\\u000a the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because\\u000a definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis\\u000a method for a centrifugal pump system

  20. Measurement selection for parametric IC fault diagnosis

    NASA Technical Reports Server (NTRS)

    Wu, A.; Meador, J.

    1991-01-01

    Experimental results obtained with the use of measurement reduction for statistical IC fault diagnosis are described. The reduction method used involves data pre-processing in a fashion consistent with a specific definition of parametric faults. The effects of this preprocessing are examined.

  1. On-line diagnosis of unrestricted faults

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.; Sundstrom, R. J.

    1974-01-01

    A formal model for the study of on-line diagnosis is introduced and used to investigate the diagnosis of unrestricted faults. A fault of a system S is considered to be a transformation of S into another system S' at some time tau. The resulting faulty system is taken to be the system which looks like S up to time tau, and like S' thereafter. Notions of fault tolerance error are defined in terms of the resulting system being able to mimic some desired behavior as specified by a system similar to S. A notion of on-line diagnosis is formulated which involves an external detector and a maximum time delay within which every error caused by a fault in a prescribed set must be detected. It is shown that if a system is on-line diagnosable for the unrestricted set of faults then the detector is at least as complex, in terms of state set size, as the specification. The use of inverse systems for the diagnosis of unrestricted faults is considered. A partial characterization of those inverses which can be used for unrestricted fault diagnosis is obtained.

  2. On-line diagnosis of unrestricted faults

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.; Sundstrom, R. J.

    1975-01-01

    Attention is given to the formal development of the notion of a discrete-time system and the associated concepts of fault, result of a fault, and error. The considered concept of on-line diagnosis is formalized and a diagnosis using inverse machines is discussed. The case of an inverse which is lossless is investigated. It is found that in such a case the class of unrestricted faults can be diagnosed with a delay equal to the delay of losslessness of the inverse system.

  3. Cooperative human-machine fault diagnosis

    NASA Technical Reports Server (NTRS)

    Remington, Roger; Palmer, Everett

    1987-01-01

    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.

  4. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

    Microsoft Academic Search

    Wensheng Su; Fengtao Wang; Hong Zhu; Zhixin Zhang; Zhenggang Guo

    2010-01-01

    The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy

  5. Fault diagnosis of bearing using wavelet packet transform and PSO-DV based neural network

    Microsoft Academic Search

    Bo Liu; Hongxia Pan

    2010-01-01

    In this paper, a fault diagnosis system is proposed for rolling bearing using wavelet packet transform (WPT), particle swarm optimization (PSO) algorithm with differential operator named PSO-DV and back-propagation neural network (BPNN) techniques. In the preprocessing of vibration signals, WPT coefficients are used for evaluating their entropy and treated as the features to distinguish the fault conditions of bearing. In

  6. Fault diagnosis of rolling element bearing using time-domain features and neural networks

    Microsoft Academic Search

    B. Sreejith; A. K. Verma; A. Srividya

    2008-01-01

    Rolling element bearings are critical mechanical components in rotating machinery. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper presents an algorithm using feed forward neural

  7. Fault diagnosis of rolling element bearing using time-domain features and neural networks

    Microsoft Academic Search

    B. Sreejith; A. K. Verm; A. Srividya

    2009-01-01

    Rolling element bearings are critical mechanical components in rotating machinery. Fault detection and diagnosis in the early stages of damage is necessary to prevent their malfunctioning and failure during operation. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper presents an algorithm using feed forward neural

  8. Automatic feature definition and selection in fault diagnosis of oil rig motor pumps

    Microsoft Academic Search

    E. D. Wandekokem; T. W. Rauber; R. J. Batista

    2009-01-01

    We present a collection of pattern recognition techniques applied to fault detection and diagnosis of motor pumps. Vibrational patterns are the basis for describing the condition of the process. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Our work is motivated by the diversity of the studied defects, the

  9. Diagnosis of subharmonic faults of large rotating machinery based on EMD

    Microsoft Academic Search

    Fangji Wu; Liangsheng Qu

    2009-01-01

    The vibration signals always carry the abundant dynamic information of a machine and are very useful for the feature extraction and fault diagnosis. In practice, most subharmonic signals have a close relationship to time variables and can manifest large amplitude fluctuation, transient vibration, or modulation signals in time domain. In view of this, this paper describes an effective method to

  10. Vibration-based fault detection of sharp bearing faults in helicopters

    E-print Network

    Paris-Sud XI, Université de

    Vibration-based fault detection of sharp bearing faults in helicopters Victor Girondin , Herve the characteristic symptoms of sharp bearing faults (like localized spalling) from vibratory analysis. However mainly in identifying fault frequencies. Local bearing faults induce temporal periodic and impulsive

  11. Vibration-based fault detection of accelerometers in helicopters

    E-print Network

    Paris-Sud XI, Université de

    than standard indicators. Keywords: accelerometers; vibration; helicopter; monitoring; skewness; HUMSVibration-based fault detection of accelerometers in helicopters Victor Girondin , Mehena Loudahi LAGIS - UMR CNRS 8219 Universit´e Lille 1 Boulevard Langevin 59655 Villeneuve d'Ascq Abstract: Vibration

  12. System-level fault diagnosis and reconfiguration

    SciTech Connect

    Gupta, R.

    1987-01-01

    The classical fault-diagnosis model assumes that faults are permanent and each test, administered by a unit, is complete for the unit being tested. These two assumptions may restrict the applicability of the model. The author introduces a new deterministic fault model for system-level fault diagnosis. Unlike earlier attempts, his model intermittent faults, incomplete testing by units, and fault masking in a uniform manner. He obtains necessary and sufficient conditions for a system to be diagnosable using the new fault model. The complexity of the diagnosability problem in the model is shown to be co-NP-complete. He then examines the problem of system reconfiguration following identification of faulty components. In particular, reconfigurability of multipipelines is considered in detail. He alternates the pipeline stages with testing and reconfiguring circuitry. The pipelines are reconfigured by programming the switches in a distributed manner. The switch programming algorithm is optimal in the sense that it recovers the maximum number of pipelines under any fault pattern. A proof of its optimality is also presented.

  13. Completing fault models for abductive diagnosis

    SciTech Connect

    Knill, E. [Los Alamos National Lab., NM (United States); Cox, P.T.; Pietrzykowski, T. [Technical Univ., NS (Canada)

    1992-11-05

    In logic-based diagnosis, the consistency-based method is used to determine the possible sets of faulty devices. If the fault models of the devices are incomplete or nondeterministic, then this method does not necessarily yield abductive explanations of system behavior. Such explanations give additional information about faulty behavior and can be used for prediction. Unfortunately, system descriptions for the consistency-based method are often not suitable for abductive diagnosis. Methods for completing the fault models for abductive diagnosis have been suggested informally by Poole and by Cox et al. Here we formalize these methods by introducing a standard form for system descriptions. The properties of these methods are determined in relation to consistency-based diagnosis and compared to other ideas for integrating consistency-based and abductive diagnosis.

  14. Fault diagnosis in sparse multiprocessor systems

    NASA Technical Reports Server (NTRS)

    Blough, Douglas M.; Sullivan, Gregory F.; Masson, Gerald M.

    1988-01-01

    The problem of fault diagnosis in multiprocessor systems is considered under a uniformly probabilistic model in which processors are faulty with probability p. This work focuses on minimizing the number of tests that must be conducted in order to correctly diagnose the state of every processor in the system with high probability. A diagnosis algorithm that can correctly diagnose the state of every processor with probability approaching one in a class of systems performing slightly greater than a linear number of tests is presented. A nearly matching lower bound on the number of tests required to achieve correct diagnosis in arbitrary systems is also proven. The number of tests required under this probabilistic model is shown to be significantly less than under a bounded-size fault set model. Because the number of tests that must be conducted is a measure of the diagnosis overhead, these results represent a dramatic improvement in the performance of system-level diagnosis technique.

  15. An Intelligent Combined Method Based on Power Spectral Density, Decision Trees and Fuzzy Logic for Hydraulic Pumps Fault Diagnosis

    Microsoft Academic Search

    Kaveh Mollazade; Hojat Ahmadi; Mahmoud Omid; Reza Alimardani

    2008-01-01

    Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. The aim of this work is to investigate the effectiveness of a new fault diagnosis method based on power spectral density (PSD) of vibration

  16. A method of multi-class faults classification based-on Mahalanobis-Taguchi system using vibration signals

    Microsoft Academic Search

    Jiangtao Ren; Yuanwen Cai; Xiaochen Xing; Jing Chen

    2011-01-01

    In this paper, an improved Mahalanobis-Taguchi system based fault diagnosis scheme is presented, vibration signals are used as the signal resource. Mahalanobis-Taguchi System is utilized for fault clustering method in order to classify faults into different categories, Lipschitz Exponents are used to extract characteristic vectors. Firstly, the procedure of implementing Mahalanobis-Taguchi System is introduced, a multi-class faults classification method is

  17. Fault diagnosis system based on Dynamic Fault Tree Analysis of power transformer

    Microsoft Academic Search

    Jiang Guo; Kefei Zhang; Lei Shi; Kaikai Gu; Weimin Bai; Bing Zeng; Yajin Liu

    2012-01-01

    Firstly, this research paper introduced the process of transformer fault diagnosis and the theory of DFTA and then we attempt to apply DFTA to the field of transformer faults diagnosis. By establishing the fault tree of transformer, a practical, easily-extended, interactive and self-learning enabled fault diagnosis system based on DFTA for transformer is designed and developed. With the implementation and

  18. Low-cost motor drive embedded fault diagnosis systems 

    E-print Network

    Akin, Bilal

    2009-05-15

    cost incipient fault detection of inverter-fed driven motors. Basically, low order inverter harmonics contributions to fault diagnosis, a motor drive embedded condition monitoring method, analysis of motor fault signatures in noisy line current, and a...

  19. A Fault Diagnosis Approach for Rolling Bearings Based on EMD Method and Eigenvector Algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Jinyu; Huang, Xianxiang

    Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

  20. Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors — A Comparative Study

    PubMed Central

    Qu, Yongzhi; He, David; Yoon, Jae; Van Hecke, Brandon; Bechhoefer, Eric; Zhu, Junda

    2014-01-01

    In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance. PMID:24424467

  1. A fuzzy diagnosis approach using dynamic fault trees

    Microsoft Academic Search

    Sheng-Yung Chang; Cheng-Ren Lin; Chuei-Tin Chang

    2002-01-01

    By incorporating digraph models, fault trees and fuzzy inference mechanisms in a unified framework, a novel approach for fault diagnosis is developed in this work. To relieve the on-line computation load, the fault origins considered in diagnosis are limited to the basic events in the cut sets of a given fault tree. The symptom occurrence order associated with each root

  2. Hidden Markov models in bearing fault diagnosis and prognosis

    Microsoft Academic Search

    Zhang Xing-hui; Kang Jian-she

    2010-01-01

    Hidden Markov model (HMM) have powerful capability of pattern classification. It can be used for fault diagnosis. Hierarchical Hidden Markov model (HHMM) can exactly represent the full life process of bearing. It can be used for fault prognosis. A framework for fault diagnosis based on HMM and fault prognosis based on HHMM was presented. Unfortunately, the original inference algorithm of

  3. Fault diagnosis using hybrid artificial intelligent methods

    Microsoft Academic Search

    Yann-Chang Huang; Chao-Ming Huang; Huo-Ching Sun; Yi-Shi Liao

    2010-01-01

    This paper presents genetic-based neural networks (GNNs) for fault diagnosis of power transformers. The GNNs automatically tune the network parameters, connection weights and bias terms of the neural networks, to yield the best model according to the proposed genetic algorithm. Due to the global search capabilities of the genetic algorithm and the highly nonlinear mapping nature of the neural networks,

  4. A Dynamic Integrated Fault Diagnosis Method for Power Transformers

    PubMed Central

    Gao, Wensheng; Liu, Tong

    2015-01-01

    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. PMID:25685841

  5. Diagnosis of subharmonic faults of large rotating machinery based on EMD

    NASA Astrophysics Data System (ADS)

    Wu, Fangji; Qu, Liangsheng

    2009-02-01

    The vibration signals always carry the abundant dynamic information of a machine and are very useful for the feature extraction and fault diagnosis. In practice, most subharmonic signals have a close relationship to time variables and can manifest large amplitude fluctuation, transient vibration, or modulation signals in time domain. In view of this, this paper describes an effective method to search the features of subharmonic faults of large rotating machinery based on empirical mode decomposition (EMD). Case study on some actual vibration signals of machine parts shows that EMD is an adaptive and unsupervised method in feature extraction and it provides an attractive alternative to the traditional diagnostic methods.

  6. A model-based fault diagnosis of powered wheelchair

    Microsoft Academic Search

    Fumihiro Itaba; Masafumi Hashimoto; Kazuhiko Takahashi

    2007-01-01

    This paper describes a method of fault diagnosis of internal sensors and actuators for a powered wheelchair. We handle hard fault and scale fault of three sensors (two wheel- resolvers and one gyro) as well as hard fault of two wheel- motors. The hard fault of the gyro is diagnosed based on mode probability estimated with interacting multi-model estimator. The

  7. Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data

    Microsoft Academic Search

    Malik S. Yilmaz; Emine Ayaz

    2009-01-01

    In this study the features for bearing fault diagnosis is investigated based on the analysis of temperature, vibration and current measurements of a 3 phase, 4 poles, 5 HP induction motors which are chemically, thermally and electrically aged by artificial aging methods. Then three adaptive neuro-fuzzy inference systems which takes the temperature, current and vibration measurements as inputs and the

  8. Condition Monitoring and Fault Diagnosis of Wet-Shift Clutch Transmission Based on Multi-technology

    NASA Astrophysics Data System (ADS)

    Chen, Man; Wang, Liyong; Ma, Biao

    Based on the construction feature and operating principle of the wet-shift clutch transmission, the condition monitoring and fault diagnosis for the transmission of the tracklayer with wet-shift clutch were implemented with using the oil analysis technology, function parameter test method and vibration analysis technology. The new fault diagnosis methods were proposed, which are to build the gray modeling with the oil analysis data, and to test the function parameter of the clutch press, the rotate speed of each gear, the oil press of the steer system and lubrication system and the hydraulic torque converter. It's validated that the representative function signals were chosen to execute the condition monitoring analysis, when the fault symptoms were found, and the oil analysis data were used to apply the gray modeling to forecast the fault occurs time can satisfy the demand of the condition monitoring and fault diagnosis for the transmission regular work.

  9. Monitoring and Diagnosis of Multiple Incipient Faults Using Fault Tree Induction

    E-print Network

    Madden, Michael

    Monitoring and Diagnosis of Multiple Incipient Faults Using Fault Tree Induction Michael G. M Abstract This paper presents DE/IFT, a new fault diagnosis engine which is based on the authors' IFT algorithm for induction of fault trees. It learns from an examples database comprising sensor recordings

  10. Gear faults diagnosis based on wavelet-AR model and PCA

    NASA Astrophysics Data System (ADS)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao

    2010-08-01

    Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient faults detection and accurate faults diagnosis are therefore critical to machinery normal operation. The use of mechanical vibration signals for fault diagnosis is significant and effective due to advances in the progress of digital signal processing techniques. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-faults diagnosis was presented in this paper based on the wavelet-Autoregressive (AR) model and Principal Component Analysis (PCA) method. The virtual prototype simulation and the experimental test were firstly carried out and the comparison results prove that the traditional Fast Fourier Transform Algorithm (FFT) analysis is not appropriate for the gear fault detection and identification. Then the wavelet-AR model was applied to extract the feature sets of the gear fault vibration data. In this procedure, the wavelet transform was used to decompose and de-noise the original signal to obtain fault signals, and the fault type information was extracted by the AR parameters. In order to eliminate the redundant fault features, the PCA was furthermore adopted to fuse the AR parameters into one characteristic to enhance the fault defection and identification. The experimental results indicate that the proposed method based on the wavelet-AR model and PCA is feasible and reliable in the gear multi-faults signal diagnosis, and the isolation of different gear conditions, including normal, single crack, single wear, compound fault of wear and spalling etc., has been effectively accomplished.

  11. Advanced Fault Diagnosis Methods in Molecular Networks

    PubMed Central

    Habibi, Iman; Emamian, Effat S.; Abdi, Ali

    2014-01-01

    Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally. PMID:25290670

  12. Fault-Trajectory Approach for Fault Diagnosis on Analog Circuits Carlos Eduardo Savioli,

    E-print Network

    Paris-Sud XI, Université de

    Fault-Trajectory Approach for Fault Diagnosis on Analog Circuits Carlos Eduardo Savioli, Claudio C Mesquita@coe.ufrj.br Abstract This issue discusses the fault-trajectory approach suitability for fault on this concept for ATPG for diagnosing faults on analog networks. Such method relies on evolutionary techniques

  13. The Rotational Mechanic Equipment Fault Diagnosis Based on the Wavelet Packet Analysis

    NASA Astrophysics Data System (ADS)

    Zhang, S. Q.; Zhang, L. G.; Gu, Z. P.; Lv, J. T.; Huang, T.

    2006-10-01

    The gears is a main driving device in the modern rotational machine equipments at the present time. In this paper, the Mallat algorithm of wavelet and wavelet packet is studied and is used in the fault diagnosis of the rotational machine equipment. The complicated mechanical vibration signals are analyzed by it. The weak signal is extracted effectively and the signal of the fault symptom is located in the time domain. The testing results proved the value of the analytic method.

  14. GEAR FAULT DIAGNOSIS BASED ON CONTINUOUS WAVELET TRANSFORM

    Microsoft Academic Search

    H. Zheng; Z. Li; X. Chen

    2002-01-01

    A new approach of gear fault diagnosis based on continuous wavelet transform is presented. Continuous wavelet transform can provide a finer scale resolution than orthogonal wavelet transform. It is more suitable for extracting mechanical fault information. In this paper, the concept of time-averaged wavelet spectrum (TAWS) based on Morlet continuous wavelet transform is proposed. Two fault diagnosis methods named spectrum

  15. Inverse Scattering for Soft Fault Diagnosis in Electric Transmission Lines

    Microsoft Academic Search

    Qinghua Zhang; Michel Sorine; Mehdi Admane

    2011-01-01

    Today's advanced reflectometry methods provide an efficient solution for the diagnosis of hard faults (open and short circuits) in electric transmission lines, but they are much less efficient for soft faults (spatially smooth variations of characteristic impedance). This paper completes an important missing piece for the application of the inverse scattering transform to the diagnosis of soft faults in electric

  16. Learning approach to nonlinear fault diagnosis: detectability analysis

    Microsoft Academic Search

    Marios M. Polycarpou; Alexander B. Trunov

    2000-01-01

    The learning approach to fault diagnosis provides a methodology for designing monitoring architectures which can be used for detection, identification and accommodation of failures in dynamical systems. This paper considers the issues of detectability conditions and detection time in a nonlinear fault diagnosis scheme based on the learning approach. First, conditions are derived to characterize the range of detectable faults.

  17. Fault diagnosis of ball bearings using machine learning methods

    Microsoft Academic Search

    P. K. Kankar; Satish C. Sharma; S. P. Harsha

    2011-01-01

    Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on

  18. Diagnosis of bearing incipient faults using fuzzy logic based methodology

    Microsoft Academic Search

    Jihong Yan; Lei Lu; Debin Zhao; Gang Wang

    2010-01-01

    With the increasing demand of stable running condition of mechanical products and low maintenance costs of machinery devices, fault detection and diagnosis attracted considerable interests, early fault diagnosis is desirable for accuracy and appropriate assessment, due to the fact that it could provide fault information as soon as possible and prevent fast deteriorating of the failure. In this paper, a

  19. A wavelet approach to fault diagnosis of a gearbox under varying load conditions

    NASA Astrophysics Data System (ADS)

    Wang, Xiyang; Makis, Viliam; Yang, Ming

    2010-04-01

    Varying load can cause changes in a measured gearbox vibration signal. However, conventional techniques for fault diagnosis are based on the assumption that changes in vibration signal are only caused by deterioration of the gearbox. There is a need to develop a technique to provide accurate state indicator of gearbox under fluctuating load conditions. This paper presents an approach to gear fault diagnosis based on complex Morlet continuous wavelet transform under this condition. Gear motion residual signal, which represents the departure of time synchronously averaged signal from the average tooth-meshing vibration, is analyzed as source data due to its lower sensitiveness to the alternating load condition. A fault growth parameter based on the amplitude of wavelet transform is proposed to evaluate gear fault advancement quantitatively. We found that this parameter is insensitive to varying load and can correctly indicate early gear fault. For a comparison, the advantages and disadvantages of other measures such as kurtosis, mean, variance, form factor and crest factor, both of residual signal and mean amplitude of continuous wavelet transform waveform, are also discussed. The effectiveness of the proposed fault indicator is demonstrated using a full lifetime vibration data history obtained under sinusoidal varying load.

  20. Fault diagnosis of roller bearing using feedback EMD and decision tree

    Microsoft Academic Search

    Jia Guifeng; Yuan Shengfa; Tang Chengwen; Xiong Jie

    2011-01-01

    This paper proposed a method for roller bearing fault diagnosis using Empirical Mode Decomposition (EMD) algorithm and decision tree. First, to obtain the Intrinsic Mode Functions (IMFs) of bearing vibration signal processed by EMD and processing the IMFs with autocorrelation for noise elimination, then extract the principal frequency as features in frequency domain. Second, build up decision tree with C4.5

  1. Research on early fault diagnosis for rolling bearing based on permutation entropy algorithm

    Microsoft Academic Search

    Fuzhou Feng; Guoqiang Rao; Pengcheng Jiang; Aiwei Si

    2012-01-01

    Permutation Entropy (PE) is a new subject which talks about the scrambling and non-linearity of complex system, which has been widely studied in recent years. This paper aims to introduce the basic algorithm of PE firstly, then verify PE using simulated signal, which shows that PE is feasible for fault diagnosis. Finally, the whole life vibration data of a rolling

  2. Beyond the Byzantine Generals: Unexpected Behavior and Bridging Fault Diagnosis

    E-print Network

    Larrabee, Tracy

    Beyond the Byzantine Generals: Unexpected Behavior and Bridging Fault Diagnosis David B. Lavo Tracy­ dicted by a chosen fault model and fault simulator. As identified by Aitken and Maxwell [4], the problem has been approached in two ways: via a simple fault model (almost always the single stuck­at model

  3. Actuator Fault Diagnosis and Accommodation for Flight Safety

    Microsoft Academic Search

    Xiaodong Zhang; Marios M. Polycarpou; Roger Xu; Chiman Kwan

    2005-01-01

    This paper presents an adaptive fault diagnosis and accommodation scheme for aerodynamic actuators. The fault-tolerant control architecture consists of three main components: an online nonlinear fault detection and isolation scheme, a controller suite, and a reconfiguration supervisor which performs controller reconfiguration and control reallocation using online diagnostic information. The proposed scheme provides a unified architecture for fault detection, isolation and

  4. Diagnosis by approximate reasoning on dynamic fuzzy fault trees

    Microsoft Academic Search

    M. Ulieru

    1994-01-01

    An approximate-reasoning model for diagnosis of continuous dynamic systems is introduced based on a previously developed fuzzy extension of the fault tree analysis and synthesis approach. The concept of dynamic fuzzy fault tree naturally emerges from the act of matching the fuzzified fault tree with the dynamic symptoms. Management of the incipient fault dynamics via fuzzy information processing is illustrated

  5. Data-driven fault diagnosis of oil rig motor pumps applying automatic definition and selection of features

    Microsoft Academic Search

    E. D. Wandekokem; F. T. de Aquino Franzosi; T. W. Rauber; R. J. Batista

    2009-01-01

    We report about fault diagnosis experiments to improve the maintenance quality of motor pumps installed on oil rigs. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Features are extracted from the vibration signals to detect and diagnose misalignment and mechanical looseness problems. We show the results of automatic pattern

  6. SSME fault monitoring and diagnosis expert system

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

    1989-01-01

    An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

  7. Two Similarity Measure Approaches to Whole Building Fault Diagnosis 

    E-print Network

    Lin, G.; Claridge, D.

    2012-01-01

    similarity are defined and the methodology for implementing the proposed whole building fault diagnosis approaches is presented. Cosine similarity and Euclidean distance similarity are applied to two field observed fault test cases, and both the cosine...

  8. Similarity Matching Techniques for Fault Diagnosis in Automotive Infotainment Electronics

    E-print Network

    Kabir, Mashud

    2009-01-01

    Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching tec...

  9. Automated fault location and diagnosis on electric power distribution feeders

    SciTech Connect

    Zhu, J. [Advanced Control Systems, Inc., Norcross, GA (United States); Lubkeman, D.L.; Girgis, A.A. [Clemson Univ., SC (United States). Dept. of Electrical and Computer Engineering

    1997-04-01

    This paper presents new techniques for locating and diagnosing faults on electric power distribution feeders. The proposed fault location and diagnosis scheme is capable of accurately identifying the location of a fault upon its occurrence, based on the integration of information available from disturbance recording devices with knowledge contained in a distribution feeder database. The developed fault location and diagnosis system can also be applied to the investigation of temporary faults that may not result in a blown fuse. The proposed fault location algorithm is based on the steady-state analysis of the faulted distribution network. To deal with the uncertainties inherent in the system modeling and the phasor estimation, the fault location algorithm has been adapted to estimate fault regions based on probabilistic modeling and analysis. Since the distribution feeder is a radial network, multiple possibilities of fault locations could be computed with measurements available only at the substation. To identify the actual fault location, a fault diagnosis algorithm has been developed to prune down and rank the possible fault locations by integrating the available pieces of evidence. Testing of the developed fault location and diagnosis system using field data has demonstrated its potential for practical use.

  10. Supervision, fault-detection and fault-diagnosis methods — An introduction

    Microsoft Academic Search

    R. Isermann

    1997-01-01

    The operation of technical processes requires increasingly advanced supervision and fault diagnosis to improve reliability, safety and economy. This paper gives an introduction to the field of fault detection and diagnosis. It begins with a consideration of a knowledge-based procedure that is based on analytical and heuristic information. Then different methods of fault detection are considered, which extract features from

  11. Automated Diagnosis Of Faults In Antenna-Aiming Systems

    NASA Technical Reports Server (NTRS)

    Smyth, Patrick J.; Mellstrom, Jeffrey A.

    1993-01-01

    Report discusses research directed toward automated diagnosis of faults in complicated electromechanical and hydraulic systems aiming 70-m and 34-m antennas of Deep Space Network communication system.

  12. Envelope extraction based dimension reduction for independent component analysis in fault diagnosis of rolling element bearing

    NASA Astrophysics Data System (ADS)

    Guo, Yu; Na, Jing; Li, Bin; Fung, Rong-Fong

    2014-06-01

    A robust feature extraction scheme for the rolling element bearing (REB) fault diagnosis is proposed by combining the envelope extraction and the independent component analysis (ICA). In the present approach, the envelope extraction is not only utilized to obtain the impulsive component corresponding to the faults from the REB, but also to reduce the dimension of vibration sources included in the sensor-picked signals. Consequently, the difficulty for applying the ICA algorithm under the conditions that the sensor number is limited and the source number is unknown can be successfully eliminated. Then, the ICA algorithm is employed to separate the envelopes according to the independence of vibration sources. Finally, the vibration features related to the REB faults can be separated from disturbances and clearly exposed by the envelope spectrum. Simulations and experimental tests are conducted to validate the proposed method.

  13. Rule-based fault diagnosis of hall sensors and fault-tolerant control of PMSM

    NASA Astrophysics Data System (ADS)

    Song, Ziyou; Li, Jianqiu; Ouyang, Minggao; Gu, Jing; Feng, Xuning; Lu, Dongbin

    2013-07-01

    Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

  14. Fault diagnosis of bearings based on a sensitive feature decoupling technique

    NASA Astrophysics Data System (ADS)

    Li, Wei; Jiang, Fan; Zhu, Zhencai; Zhou, Gongbo; Chen, Guoan

    2013-03-01

    Bearings are commonly used in machine industry, and their faults may result in unexpected vibration and even cause breakdown of a whole rotating machine. This paper proposes a novel fault diagnosis approach for bearings by using a sensitive feature decoupling technique. This approach does not require a training procedure as in machine learning methods and can classify the occurred faults by a simple algebraic computation. Firstly, the features of vibration signals which show the most significant difference under different bearing health conditions are selected and defined as sensitive features. Then those sensitive features under different health conditions are used to construct a feature matrix, and its left null space is computed to obtain the so-called feature decoupling vectors. The bearing faults are finally classified with the help of the decoupling vectors according to a simple decision logic. Since the obtained decoupling vectors may not be unique, we also propose an algorithm to select the optimal ones in order to improve the performance of fault diagnosis. Experiments are carried out to test the proposed approach and the results show that the approach is feasible and effective for the fault diagnosis of bearings.

  15. A Hybrid Model Based and Statistical Fault Diagnosis System for Industrial Process 

    E-print Network

    Lin, Chen-Han

    2014-11-21

    This thesis presents a hybrid model based and statistical fault diagnosis system, which applied on the nonlinear three-tank model. The purpose of fault diagnosis is generating and analyzing the residual to find out the fault occurrence. This fault...

  16. Fault diagnosis in a plant using Fisher discriminant analysis

    Microsoft Academic Search

    M. J. Fuente; G. Garcia; G. I. Sainz

    2008-01-01

    In this paper Fisher's discriminant analysis (FDA) is used for detecting and diagnosing faults in a real plant. FDA provides an optimal lower dimensional representation in terms of discriminating between classes of data, where, in this context of fault diagnosis, each class corresponds to data collected during a specific, known fault. A discriminant function is applied to detect and diagnose

  17. Fault progression modeling: An application to bearing diagnosis and prognosis

    Microsoft Academic Search

    Bin Zhang; Chris Sconyers; Marcos Orchard; Romano Patrick; George Vachtsevanos

    2010-01-01

    The successful implementation of fault diagnosis and failure prognosis algorithms to safety critical systems requires the definitions and applications of mathematically rigorous modules. These modules, including data preprocessing, feature extraction, diagnostic and prognostic algorithms, performance metrics definition, and a fault progression model, form an integrated architecture for system health monitoring and management. In these modules, the fault progression model is

  18. A Probabilistic Approach to Fault Diagnosis in Linear Lightwave Networks

    Microsoft Academic Search

    Robert Huijie Deng; Aurel A. Lazar; Weiguo Wang

    1993-01-01

    The application of probabilistic reasoning to fault diagnosis in linear lightwave networks (LLNs) is investigated. The LLN inference model is represented by a Bayesian network (or causal network). An inference algorithm is proposed that is capable of con- ducting fault diagnosis (inference) with incomplete evidence and on an interactive basis. Two belief updating algorithms are presented which are used by

  19. Approach to Fault Diagnosis in Linear Lightwave Networks

    Microsoft Academic Search

    Robert H. Deng; Aurel A. Lazar; Weiguo Wang

    The application of probabilistic reasoning to fault diagnosis in linear lightwave networks (LLNs) is investigated. The LLN inference model is represented by a Bayesian network (or causal network). An inference algorithm is proposed that is capable of con- ducting fault diagnosis (inference) with incomplete evidence and on an interactive basis. Two belief updating algorithms are presented which are used by

  20. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

    NASA Astrophysics Data System (ADS)

    Su, Wensheng; Wang, Fengtao; Zhu, Hong; Zhang, Zhixin; Guo, Zhenggang

    2010-07-01

    The fault diagnosis of rolling element bearing is important for improving mechanical system reliability and performance. When localized fault occurs in a bearing, the periodic impulsive feature of the vibration signal appears in time domain, and the corresponding bearing characteristic frequencies (BCFs) emerge in frequency domain. However, in the early stage of bearing failures, the BCFs contain very little energy and are often overwhelmed by noise and higher-level macro-structural vibrations, an effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized by genetic algorithm. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. In the enhanced autocorrelation envelope power spectrum, only several single spectrum lines would be left, which is very simple for operator to identify the bearing fault type. Moreover, the proposed method can be conducted in an almost automatic way. The results obtained from simulated and practical experiments prove that the proposed method is very effective for bearing faults diagnosis.

  1. Probability based vehicle fault diagnosis: Bayesian network method

    Microsoft Academic Search

    Yingping Huang; Ross McMurran; Gunwant Dhadyalla; R. Peter Jones

    2008-01-01

    Fault diagnostics are increasingly important for ensuring vehicle safety and reliability. One of the issues in vehicle fault\\u000a diagnosis is the difficulty of successful interpretation of failure symptoms to correctly diagnose the real root cause. This\\u000a paper presents an innovative Bayesian Network based method for guiding off-line vehicle fault diagnosis. By using a vehicle\\u000a infotainment system as a case study,

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

    NASA Astrophysics Data System (ADS)

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

    2011-07-01

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

  3. Sensor selection in neuro-fuzzy modelling for fault diagnosis

    Microsoft Academic Search

    Yimin Zhou; Argyrios Zolotas

    2010-01-01

    In this paper, sensor selection relating to neuro-fuzzy modeling for the purpose of fault diagnosis is discussed. The input\\/output selection in fuzzy modelling plays an important role in the performance of the derived model. In addition, with respect to fault tolerant issues, the impact of the faults on the system, i.e. possible incipient and abrupt faults, should be detected in

  4. Cyclostationarity analysis and diagnosis method of bearing faults

    Microsoft Academic Search

    Bin Wu; Minjie Wang; Yuegang Luo; Changjian Feng

    2009-01-01

    The vibration signal of rotating machine is typical amplitude-modulated signal. In that case, the method of cyclostationary analysis is very effective for extracting and demodulating the modulating signal that usually contains some fault message. However, duo to a good many factors, such as the special construction, the amalgamation of multiple faults and the fluctuation of rotating speed etc., the cyclostationary

  5. Hybrid intelligent fault diagnosis based on granular computing

    Microsoft Academic Search

    Zhaowen Hou; Zhousuo Zhang

    2009-01-01

    To solve the problem of lacking hybrid modes and common algorithms in hybrid intelligent diagnosis, this paper presents a new approach to hybrid intelligent fault diagnosis of the mechanical equipment based on granular computing. The hybrid intelligent diagnosis model based on neighborhood rough set is constructed in different granular levels, and the results of support vector machines (SVMS) and artificial

  6. Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform

    Microsoft Academic Search

    Hui Li; Yuping Zhang; Haiqi Zheng

    2010-01-01

    The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach\\u000a to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented.\\u000a This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique.\\u000a The nonstationary

  7. Using bispectral distribution as a feature for rotating machinery fault diagnosis

    Microsoft Academic Search

    Lingli Jiang; Yilun Liu; Xuejun Li; Siwen Tang

    2011-01-01

    The vibration signals of rotating machinery present a strongly non-linear and non-Gaussian behavior, and bispectrum is well suitable to analyze this kind of signals. Due to modulation or smearing, it is hard to extract the accurate frequency-based features from the bispectrum. A bispectral distribution for machinery fault diagnosis is developed in this paper. The binary images extracted from the bispectra

  8. Application of the Teager-Kaiser energy operator in bearing fault diagnosis.

    PubMed

    Henríquez Rodríguez, Patricia; Alonso, Jesús B; Ferrer, Miguel A; Travieso, Carlos M

    2013-03-01

    Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal. PMID:23352553

  9. Fault diagnosis based on continuous simulation models

    NASA Technical Reports Server (NTRS)

    Feyock, Stefan

    1987-01-01

    The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.

  10. Robust model-based fault diagnosis for chemical process systems

    E-print Network

    Rajaraman, Srinivasan

    2006-08-16

    of the process. Finally the proposed methodology for fault diagnosis has been applied in numerical simulations to a non-isothermal CSTR (continuous stirred tank reactor), an industrial melter process, and a debutanizer plant....

  11. Robust model-based fault diagnosis for chemical process systems 

    E-print Network

    Rajaraman, Srinivasan

    2006-08-16

    Fault detection and diagnosis have gained central importance in the chemical process industries over the past decade. This is due to several reasons, one of them being that copious amount of data is available from a large number of sensors...

  12. Basis pursuit-based intelligent diagnosis of bearing faults

    Microsoft Academic Search

    Hongyu Yang; Joseph Mathew; Lin Ma

    2007-01-01

    Purpose – The purpose of this article is to present a new application of pursuit-based analysis for diagnosing rolling element bearing faults. Design\\/methodology\\/approach – Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification. This paper presents a comparative study of

  13. Intelligent fault isolation and diagnosis for communication satellite systems

    NASA Technical Reports Server (NTRS)

    Tallo, Donald P.; Durkin, John; Petrik, Edward J.

    1992-01-01

    Discussed here is a prototype diagnosis expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous diagnosis capability. The system, the Fault Isolation and Diagnosis EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and fault states. This overall frame architecture integrates the hierarchical structures into a lattice that provides a flexible representation scheme and facilitates system maintenance. FIDEX uses an inexact reasoning technique based on the incrementally acquired evidence approach developed by Shortliffe. The system is designed with a primitive learning ability through which it maintains a record of past diagnosis studies.

  14. Non-cooperative Diagnosis of Submarine Cable Faults

    E-print Network

    Chang, Rocky Kow-Chuen

    Non-cooperative Diagnosis of Submarine Cable Faults Edmond W. W. Chan, Xiapu Luo, Waiting W. T. Fok|csxluo|cswtfok|csweicli|csrchang}@comp.polyu.edu.hk Abstract. Submarine cable faults are not uncommon events in the In- ternet today. However, their impacts of the performance degradation. 1 Introduction Submarine cables are critical elements of the Internet today, because

  15. Fault Detection and Diagnosis Method for VAV Terminal Units 

    E-print Network

    Miyata, M.; Yoshida, H.; Asada, M.; Wang, F.; Hashiguchi, S.

    2004-01-01

    This paper proposes two fault detection and diagnosis methods for VAV units without a sensor of supply air volume, and the results of applying these methods to a real building are presented. One method detects faults by applying a statistical method...

  16. Fault detection and diagnosis capabilities of test sequence selection

    E-print Network

    Thulsiraman, Krishnaiyan

    Review Fault detection and diagnosis capabilities of test sequence selection methods based on the FSM model T Ramalingam*, Anindya Dast and K ThuIasiraman* Different test sequence selection methods resolution in diagnosing the fault. The test sequence selection methods are then compared based on the length

  17. Real-time fault diagnosis for propulsion systems

    NASA Technical Reports Server (NTRS)

    Merrill, Walter C.; Guo, Ten-Huei; Delaat, John C.; Duyar, Ahmet

    1991-01-01

    Current research toward real time fault diagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations.

  18. Wavelet Neural Networks for Fault Diagnosis and Prognosis

    Microsoft Academic Search

    Hamid R. Berenji; Yan Wang

    2006-01-01

    Wavelet Neural Networks have been developed for fault diagnosis and prognosis with unique capabilities in addressing identification and classification problems. A fault diagnostic and prognostic system is presented by using Wavelet Neural Networks and Dynamic Wavelet Neural Networks. We used a Matlab Simulink model of a chiller system and applied the Wavelet Neural Network to detect and recover sensor errors.

  19. Catastrophic fault diagnosis in dynamic systems using bond graph methods

    SciTech Connect

    Yarom, Tamar.

    1990-01-01

    Detection and diagnosis of faults has become a critical issue in high performance engineering systems as well as in mass-produced equipment. It is particularly helpful when the diagnosis can be made at the initial design level with respect to a prospective fault list. A number of powerful methods have been developed for aiding in the general fault analysis of designs. Catastrophic faults represent the limit case of complete local failure of connections or components. They result in the interruption of energy transfer between corresponding points in the system. In this work the conventional approach to fault detection and diagnosis is extended by means of bond-graph methods to a wide variety of engineering systems. Attention is focused on catastrophic fault diagnosis. A catastrophic fault dictionary is generated from the system model based on topological properties of the bond graph. The dictionary is processed by existing methods to extract a catastrophic fault report to aid the engineer in performing a design analysis.

  20. Fault diagnosis using rough sets and BP networks

    Microsoft Academic Search

    Weihua Li; Wei Pan; Shenggang Zhang

    2010-01-01

    This research presents a rough set and back propagation neural network based scheme for rolling bearings fault diagnosis. The scheme is designed to classify the fault type. Experiments results indicate that rough set is helpful to reduce dimensionality, discard deceptive features and extract an optimal subset from the raw feature set, and the proposed rough sets combined with BP neural

  1. FAULT DIAGNOSIS FOR ROLLING ELEMENT BEARINGS USING SLIDING MODE TECHNIQUES

    Microsoft Academic Search

    C. Kitsos; S. K. Spurgeon; J. A. Twiddle; N. B. Jones

    A fault diagnosis method based on sliding mode techniques is described. Experiments were conducted on a dry vacuum pump to evaluate the effectiveness of the approach in monitoring and detecting overheated bearings using low-cost sensors. The equivalent injection signal necessary to maintain a sliding motion is used for parameter estimation and hence fault detection in the system. A second order

  2. Power transformer multi-parameter fault fusion diagnosis method

    Microsoft Academic Search

    Lin Du; Lei Yuan; Youyuan Wang

    2010-01-01

    The fault diagnosis for large oil-immersed power transformer is generally carried out through preventive test data. However, the preventive test data must be obtained until power-off time, and the amounts and accuracy of the field data is limited. When a fault occurs in the running time, it always accompanies by the variations of the appearance such as color, sound, temperature

  3. Learning of model parameters for fault diagnosis in wireless networks

    Microsoft Academic Search

    Raquel Barco; Volker Wille; Luis Díez; Matias Toril

    2010-01-01

    Self-management is essential for Beyond 3G (B3G) systems, where the existence of multiple access technologies (GSM, GPRS,\\u000a UMTS, WLAN, etc.) will complicate network operation. Diagnosis, that is, fault identification, is the most difficult task\\u000a in automatic fault management. This paper presents a probabilistic system for auto-diagnosis in the radio access part of wireless\\u000a networks, which comprises a model and a

  4. Fault diagnosis of motor bearing using self-organizing maps

    Microsoft Academic Search

    Fei Zhong; Tieiin Shi; Tao He

    2005-01-01

    This paper focuses on the application of self-organizing maps (SOM) in motor bearing fault diagnosis and presents an approach for motor rolling bearing fault diagnosis using SOM neural networks and time\\/frequency-domain bearing analysis. The SOM is a neural network algorithm which is based on unsupervised learning and combines the tasks of vector quantization and data projection. The objective of this

  5. The present status and development of acoustic emission technique in diagnosis of rotating machinery faults

    NASA Astrophysics Data System (ADS)

    Yu, Yang; Yang, Ping

    2011-10-01

    In this paper, AE source, propagation characteristic, AE parameters, the noise and the advantages of acoustic emission method for the diagnosis are expounded. The last ten years research conclusions of many important experiments and the signal processing methods which were used for the diagnosis of rolling equipment faults are summarized. The results come from UK, USA and China, etc. The signal processing methods consist of Wavelet Transform, Wavelet Packets and Neural Network, etc. The contradictions of the results are discussed. These summaries are helpful to the new researchers. Through analyzing these results, we could conclude that although there is a lot of work to do, the diagnosis of early rolling equipment faults will be resolved by Acoustic Emission technology not vibration analysis.

  6. Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    LIN, JING; QU, LIANGSHENG

    2000-06-01

    The vibration signals of a machine always carry the dynamic information of the machine. These signals are very useful for the feature extraction and fault diagnosis. However, in many cases, because these signals have very low signal-to-noise ratio (SNR), to extract feature components becomes difficult and the applicability of information drops down. Wavelet analysis in an effective tool for signal processing and feature extraction. In this paper, a denoising method based on wavelet analysis is applied to feature extraction for mechanical vibration signals. This method is an advanced version of the famous “soft-thresholding denoising method” proposed by Donoho and Johnstone. Based on the Morlet wavelet, the time-frequency resolution can be adapted to different signals of interest. In this paper, this denoising method is introduced in detail. The results of the application in rolling bearing diagnosis and gear-box diagnosis are satisfactory.

  7. Fault diagnosis of rolling element bearings using an EMRAN RBF neural network- demonstrated using real experimental data

    Microsoft Academic Search

    Ihab Samy; Ip-Shing Fan; Suresh Perinpanayagam

    2010-01-01

    Rolling element bearings are critical components of rotating machinery. Failure diagnosis of bearing faults is necessary and can often avoid more catastrophic failure consequences. Nowadays vibration condition monitoring is the most frequently used failure diagnostic method for rotating machinery. Several designs have been proposed in the literature and in this paper we propose a different approach using a radial basis

  8. Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification

    NASA Astrophysics Data System (ADS)

    Wang, Tianyang; Liang, Ming; Li, Jianyong; Cheng, Weidong; Li, Chuan

    2015-10-01

    The interfering vibration signals of a gearbox often represent a challenging issue in rolling bearing fault detection and diagnosis, particularly under unknown variable rotational speed conditions. Though some methods have been proposed to remove the gearbox interfering signals based on their discrete frequency nature, such methods may not work well under unknown variable speed conditions. As such, we propose a new approach to address this issue. The new approach consists of three main steps: (a) adaptive gear interference removal, (b) fault characteristic order (FCO) based fault detection, and (c) rotational-order-sideband (ROS) based fault type identification. For gear interference removal, an enhanced adaptive noise cancellation (ANC) algorithm has been developed in this study. The new ANC algorithm does not require an additional accelerometer to provide reference input. Instead, the reference signal is adaptively constructed from signal maxima and instantaneous dominant meshing multiple (IDMM) trend. Key ANC parameters such as filter length and step size have also been tailored to suit the variable speed conditions, The main advantage of using ROS for fault type diagnosis is that it is insusceptible to confusion caused by the co-existence of bearing and gear rotational frequency peaks in the identification of the bearing fault characteristic frequency in the FCO sub-order region. The effectiveness of the proposed method has been demonstrated using both simulation and experimental data. Our experimental study also indicates that the proposed method is applicable regardless whether the bearing and gear rotational speeds are proportional to each other or not.

  9. On the distributed fault diagnosis of computer networks

    Microsoft Academic Search

    Sailesh Chutani; Henri J. Nussbaumer

    1995-01-01

    We propose a general technique for the fault diagnosis of communication networks that is inspired by the theory of system-level diagnosis. This technique relies on the paradigm of comparison testing. A set of tasks, possibly implicit, is executed by the nodes in a network. The resulting agreements and disagreements in their results are used to diagnose all the faulty nodes

  10. Automated diagnosis of rolling bearing faults in electrical drives

    Microsoft Academic Search

    Henning Zoubek; Sebastian Villwock; Mario Pacas

    2007-01-01

    This paper deals with a new automated diagnosis method for detecting bearing faults. The diagnosis is carried out by frequency response analysis of the mechanical system of the drive. The detection of bearing damages of a ball bearing on motor and load side of the mechanical drive system is addressed. The mechanics is assumed to be a two-inertia-system with one

  11. Wind Energy Conversion Systems Fault Diagnosis Using Wavelet Analysis

    E-print Network

    Paris-Sud XI, Université de

    , induction generator, Discrete Wavelet Transform (DWT), failure diagnosis. I. Introduction Wind energy voltage, current and power and are used to detect turn faults, broken rotor bars, bearing failures, air transient technique suitable for electrical and mechanical failure diagnosis in an induction generator based

  12. Fault diagnosis with automata generated languages

    Microsoft Academic Search

    Chuei-Tin Chang; Chung Yang Chen

    2011-01-01

    A SDG-based simulation procedure is proposed in this study to qualitatively predict the effects of one or more fault propagating in a given process system. These predicted state evolution behaviors are characterized with an automaton model. By selecting a set of on-line sensors, the corresponding diagnoser can be constructed and the diagnosability of every fault origin can be determined accordingly

  13. Transmission line model influence on fault diagnosis

    Microsoft Academic Search

    N. S. D. Brito; W. L. A. Neves; B. A. Souza; K. M. C. Dantas; A. V. Fontes; A. B. Fernandes; S. S. B. Silva

    2004-01-01

    Artificial neural networks have been used to develop software applied to fault identification and classification in transmission lines with satisfactory results. The input data to the neural network are the sampled values of voltage and current waveforms. The values proceed from the digital fault recorders, which monitor the transmission lines and make the data available in their analog channels. It

  14. Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

    NASA Astrophysics Data System (ADS)

    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.

  15. A method of fault analysis for test generation and fault diagnosis

    Microsoft Academic Search

    Henry Cox; Janusz Rajski

    1988-01-01

    The authors present a fault coverage analysis method for test generation and fault diagnosis of large combinational circuits. Input vectors are analyzed in pairs in two steps using a 16-valued logic system, GEMINI. Forward propagation is performed to determine, for each line in the network, the set of all possible values it can take if the network contains any single

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

    NASA Astrophysics Data System (ADS)

    Jiang, Li; Shi, Tielin; Xuan, Jianping

    2012-05-01

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

  17. Development of a generic rotating machinery fault diagnosis approach insensitive to machine speed and support type

    NASA Astrophysics Data System (ADS)

    Nembhard, Adrian D.; Sinha, Jyoti K.; Yunusa-Kaltungo, A.

    2015-02-01

    Despite numerous difficulties that can be encountered when using trend monitoring of harmonic components from the simple amplitude spectra to aid diagnosis of rotor related faults on large multi-stage multi-bearing rotating machines, the technique continues to be the mainstay in industry. This is due in part to factors including a lack of adequate experimental validation of newly proposed techniques aimed at improving or replacing this traditional practice. Nevertheless, in recent studies, simple but robust Individual Speed Individual Foundation (ISIF) and Multi-Speed Individual Foundation (MSIF) fault diagnosis (FD) methods that both used a single vibration sensor per bearing without the use of phase information was applied with good results to fixed and variable speed machines respectively. A similar Individual Speed Multi-Foundation (ISMF) technique later enabled FD by direct comparison of vibration data between similarly configured machines with different dynamic characteristics operating at the same steady-state speed. However, the efficacy of these techniques was questioned as they were all applied to experimental rigs with the same few rotor related faults. Thus, the objective of this study is to test the transferability of these said techniques on a wider range of rotor related faults on different machines. A new Multi-Speed Multi-Foundation (MSMF) method which facilitates FD by the direct comparison of vibration data from similarly configured machines with different dynamic characteristics operating at different steady-state speeds is also proposed. It is observed that the previously proposed methods are indeed able to separate the range of conditions tested on machines with different dynamic characteristics. Analysis done with newly proposed MSMF approach gives improved isolation of fault conditions tested compared to the previously proposed techniques.

  18. Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Lu, Na; Xiao, Zhihuai; Malik, O. P.

    2015-02-01

    State identification to diagnose the condition of rotating machinery is often converted to a classification problem of values of non-dimensional symptom parameters (NSPs). To improve the sensitivity of the NSPs to the changes in machine condition, a novel feature extraction method based on adaptive multiwavelets and the synthetic detection index (SDI) is proposed in this paper. Based on the SDI maximization principle, optimal multiwavelets are searched by genetic algorithms (GAs) from an adaptive multiwavelets library and used for extracting fault features from vibration signals. By the optimal multiwavelets, more sensitive NSPs can be extracted. To examine the effectiveness of the optimal multiwavelets, conventional methods are used for comparison study. The obtained NSPs are fed into K-means classifier to diagnose rotor faults. The results show that the proposed method can effectively improve the sensitivity of the NSPs and achieve a higher discrimination rate for rotor fault diagnosis than the conventional methods.

  19. Fault diagnosis viewed as a left invertibility problem.

    PubMed

    Martínez-Guerra, R; Mata-Machuca, J L; Rincón-Pasaye, J J

    2013-09-01

    This work deals with the fault diagnosis problem, some new properties are found using the left invertibility condition through the concept of differential output rank. Two schemes of nonlinear observers are used to estimate the fault signals for comparison purposes, one of these is a proportional reduced order observer and the other is a sliding mode observer. The methodology is tested in a real time implementation of a three-tank system. PMID:23838257

  20. Roller bearings fault diagnosis based on LS-SVM

    Microsoft Academic Search

    Wentao Sui; Dan Zhang; Wilson Wang

    2009-01-01

    A new method of roller bearings fault diagnosis based on least squares support vector machines (LS-SVM) was presented. Feature selection method based on simulated annealing (SA) algorithm was discussed in this paper. LS-SVM classifier was constructed for bearing faults. Compared with the Artificial Neural Network based method, the LS-SVM based method possessed desirable advantages. Experiment shows that the presented method

  1. A Fault Diagnosis Prototype System Based on Causality Diagram

    Microsoft Academic Search

    Xinghua Fan; Feng Hu; Simon X. Yang

    2006-01-01

    \\u000a There exists a challenge, i.e., to diagnose failures of such a complex system that has the following characters: (1) it has\\u000a a causality loop structure; (2) system observed variables are discrete, or continuous, or mixed; and (3) system has time lag,\\u000a i.e., it has delay of fault effect. For the task, this paper proposes a fault diagnosis prototype system based

  2. A Fault Diagnosis Prototype System Based on Causality Diagram

    Microsoft Academic Search

    Xinghua Fan; Feng Hu; Simon X. Yang

    \\u000a There exists a challenge, i.e., to diagnose failures of such a complex system that has the following characters: (1) it has\\u000a a causality loop structure; (2) system observed variables are discrete, or continuous, or mixed; and (3) system has time lag,\\u000a i.e., it has delay of fault effect. For the task, this paper proposes a fault diagnosis prototype system based

  3. Fault Diagnosis of Roller Bearing Based on PCA and Multi-class Support Vector Machine

    Microsoft Academic Search

    Guifeng Jia; Shengfa Yuan; Chengwen Tang

    2010-01-01

    \\u000a This paper discusses the fault features selection using principal component analysis and using multi-class support vector\\u000a machine (MSVM) for bearing faults classification. The bearings vibration signal is obtained from experiment in accordance\\u000a with the following conditions: normal bearing, bearing with inner race fault, bearing with outer race fault and bearings with\\u000a balls fault. Statistical parameters of vibration signal such as

  4. Fault Prognosis and Diagnosis of an Automotive Rear Axle Gear Using a RBF-BP Neural Network

    NASA Astrophysics Data System (ADS)

    Shao, Yimin; Liang, Jie; Gu, Fengshou; Chen, Zaigang; Ball, Andrew

    2011-07-01

    The rear axle gear is one of the key parts of transmission system for automobiles. Its healthy state directly influences the security and reliability of the automotives. However, non-stationary and nonlinear characteristics of gear vibration due to load and speed fluctuations, makes it difficult to detect and diagnosis the faults from the transmission gear. To solve this problem a fault prognosis and diagnosis method based on a combination of radial basis function(RBF) and back-propagation (BP) neural networks is proposed in this paper. Firstly, a moving average pretreatment is used to suppress the time series fluctuation of vibration characteristic parameter tie series and reduce the interference of random noise. Then, the RBF network is applied to the pretreated parameter sequences for fault prognosis. Furthermore, based on self-learning ability of neural networks, characteristic parameters for different common faults are learned by a BP network. Then the trained BP neural network is utilized for fault diagnosis of the rear axle gear. The results show that the proposed method has a good performance in prognosing and diagnosing different faults from the rear axle gear.

  5. Software For Fault-Tree Diagnosis Of A System

    NASA Technical Reports Server (NTRS)

    Iverson, Dave; Patterson-Hine, Ann; Liao, Jack

    1993-01-01

    Fault Tree Diagnosis System (FTDS) computer program is automated-diagnostic-system program identifying likely causes of specified failure on basis of information represented in system-reliability mathematical models known as fault trees. Is modified implementation of failure-cause-identification phase of Narayanan's and Viswanadham's methodology for acquisition of knowledge and reasoning in analyzing failures of systems. Knowledge base of if/then rules replaced with object-oriented fault-tree representation. Enhancement yields more-efficient identification of causes of failures and enables dynamic updating of knowledge base. Written in C language, C++, and Common LISP.

  6. Object-oriented fault tree models applied to system diagnosis

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.

  7. Application of statistics filter method and clustering analysis in fault diagnosis of roller bearings

    NASA Astrophysics Data System (ADS)

    Song, L. Y.; Wang, H. Q.; Gao, J. J.; Yang, J. F.; Liu, W. B.; Chen, P.

    2012-05-01

    Condition diagnosis of roller bearings depends largely on the feature analysis of vibration signals. Spectrum statistics filter (SSF) method could adaptively reduce the noise. This method is based on hypothesis testing in the frequency domain to eliminate the identical component between the reference signal and the primary signal. This paper presents a statistical parameter namely similarity factor to evaluate the filtering performance. The performance of the method is compared with the classical method, band pass filter (BPF). Results show that statistics filter is preferable to BPF in vibration signal processing. Moreover, the significance level awould be optimized by genetic algorithms. However, it is very difficult to identify fault states only from time domain waveform or frequency spectrum when the effect of the noise is so strong or fault feature is not obvious. Pattern recognition is then applied to fault diagnosis in this study through system clustering method. This paper processes experiment rig data that after statistics filter, and the accuracy of clustering analysis increases substantially.

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

    PubMed

    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. PMID:24239987

  9. Advanced fault diagnosis techniques and their role in preventing cascading blackouts 

    E-print Network

    Zhang, Nan

    2007-04-25

    This dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two...

  10. A hybrid fault diagnosis method using morphological filter-translation invariant wavelet and improved ensemble empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Meng, Lingjie; Xiang, Jiawei; Wang, Yanxue; Jiang, Yongying; Gao, Haifeng

    2015-01-01

    Defective rolling bearing response is often characterized by the presence of periodic impulses, which are usually immersed in heavy noise. Therefore, a hybrid fault diagnosis approach is proposed. The morphological filter combining with translation invariant wavelet is taken as the pre-filter process unit to reduce the narrowband impulses and random noises in the original signal, then the purified signal will be decomposed by improved ensemble empirical mode decomposition (EEMD), in which a new selection method integrating autocorrelation analysis with the first two intrinsic mode functions (IMFs) having the maximum energies is put forward to eliminate the pseudo low-frequency components of IMFs. Applying the envelope analysis on those selected IMFs, the defect information is easily extracted. The proposed hybrid approach is evaluated by simulations and vibration signals of defective bearings with outer race fault, inner race fault, rolling element fault. Results show that the approach is feasible and effective for the fault detection of rolling bearing.

  11. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform

    NASA Astrophysics Data System (ADS)

    Kar, Chinmaya; Mohanty, A. R.

    2008-03-01

    This paper deals with an experimental investigation of fault diagnosis in a multistage gearbox under transient loads. An induction motor drives the multistage gearbox, which is connected to a DC generator for loading purpose. The signals studied are the vibration transients, recorded from an accelerometer fitted at the tail-end bearing of the gearbox; and the current transients drawn by the induction motor. Three defective cases and three transient load conditions are investigated. Advanced signal processing techniques such as discrete wavelet transform (DWT) and a corrected multiresolution Fourier transform (MFT) are applied to investigate the vibration and current transients. It is observed from the vibration transients that the load removal is a high-frequency phenomenon. With increase in defect severity, not only the defective gear mesh frequency gains energy, but also large impact energy appears in low-frequency regions. Whereas in the current transients, though load removal is a low-frequency phenomenon, a very small transient is observed at high-frequency regions for defective gears. With increase in defect severity, energy is distributed to the sidebands of the gear mesh frequency across supply line frequency. A statistical feature extraction technique is proposed in order to find a trend in detection of defects. A condition monitoring scheme is devised that can facilitate in monitoring vibration and current transients in the gearbox with simultaneous presence of transient loads and defects.

  12. SBT soft fault diagnosis in analog electronic circuits: a sensitivity-based approach by randomized algorithms

    Microsoft Academic Search

    Cesare Alippi; Marcantonio Catelani; Ada Fort; Marco Mugnaini

    2002-01-01

    This paper addresses the fault diagnosis issue based on a simulation before test philosophy in analog electronic circuits. Diagnosis, obtained by comparing signatures measured at the test nodes with those contained in a fault dictionary, allows for sub-systems testing and fault isolation within the circuit. A novel method for constructing the fault dictionary under the single faulty component\\/unit hypothesis is

  13. Multiple Fault Diagnosis in Complex Physical Systems Matthew Daigle, Xenofon Koutsoukos, and Gautam Biswas

    E-print Network

    Daigle, Matthew

    Multiple Fault Diagnosis in Complex Physical Systems Matthew Daigle, Xenofon Koutsoukos, and Gautam.j.daigle,xenofon.koutsoukos,gautam.biswas}@vanderbilt.edu Abstract Multiple fault diagnosis is a challenging problem because the number of candidates grows exponen- tially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect

  14. Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

    NASA Astrophysics Data System (ADS)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao; Li, Li

    2011-10-01

    Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies.

  15. Satellite fault diagnosis using a bank of interacting Kalman filters

    Microsoft Academic Search

    N. Tudoroiu; K. Khorasani

    2007-01-01

    The main objective of this work is development and testing of a detection, isolation, and diagnosis algorithm based on interacting multiple model (IMM) filters for both partial (soft) and total (hard) reaction wheels faults in a spacecraft. This is shown to be accomplished under a number of different faulty mode scenarios for these actuators associated with the attitude control system

  16. Simulation research on FLS_SVM in sensor fault diagnosis

    Microsoft Academic Search

    Sen-Yue Zhang; Yi-Bo Li

    2011-01-01

    The conception of fuzzy membership is introduced into the least square support vector machines (LSSVMs), which overcomes the disadvantage that LSSVMs are so sensitive to outliers in training samples and SVMs are time- consuming to solve quadratic programming problems. A sensor fault diagnosis system is designed by building the fuzzy least square vector machine (FLSSVM) model. FLSSVM is trained out

  17. A New Fault Diagnosis Model Based on AIR Scheme

    Microsoft Academic Search

    Gui-hong Zhou; Chun-cheng Zuo; Jia-zhong Wang; Shu-xia Liu

    2006-01-01

    A new model for fault diagnosis of mechanical facilities is proposed in this paper. The model synthesizes the structure of the neural network and the scheme of the artificial immune regulation (AIR). The training samples are clustered first by the immune algorithm based on AIR scheme. The centers of the clustering (memory B-cells) are saved as the nodes of the

  18. Hybrid intelligent fault diagnosis based on quotient space

    Microsoft Academic Search

    Jinfeng Zhang; Chuang Sun; Zhousuo Zhang; Zhengjia He

    2011-01-01

    Aiming at the problem that existing hybrid intelligent models do not take into account the advantages and limitations of different diagnostic methods and fail to achieve complementary advantages of different classifiers, a new model of hybrid intelligent fault diagnosis based on quotient space is proposed. In this model, samples are granulated and granular layers are constructed by calculating equivalence and

  19. Fault diagnosis system for tapped power transmission lines

    Microsoft Academic Search

    E. A. Mohamed; H. A. Talaat; E. A. Khamis

    2010-01-01

    This paper presents a design for a fault diagnosis system (FDS) for tapped HV\\/EHV power transmission lines. These lines have two different protection zones. The proposed approach reduces the cost and the complexity of the FDS for these types of lines. The FDS consists basically of fifteen artificial neural networks (ANNs). The FDS basic objectives are mainly: (1) the detection

  20. Online monitoring and fault diagnosis system of Power Transformer

    Microsoft Academic Search

    Li Weixuan; Xia Zixiang

    2010-01-01

    In this paper, a novel transformer monitoring system is presented. This system combines expert system and neural network system, which improves the accuracy of fault diagnosis, monitor the trends of the condition of power transformer, timely detect transformer failure, avoid power outages accidents, and improve supply reliability. A wireless communication method to transmit the signal collected from transformer was designed

  1. Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures

    E-print Network

    Harris, Ian G.

    Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures Ian Harris and Russell Tessier. Cluster-based FPGA architectures, in which several logic blocks are grouped together into a coarse-grained logic block, are rapidly becoming the architecture of choice for major FPGA manufacturers. The high

  2. Research on fault diagnosis expert system of automotive engine based on ontology

    Microsoft Academic Search

    Jianjun Yi; Shaoli Chen; Xu Geng; Yu Lin

    2011-01-01

    The concept of Ontology has been introduced into engineering fields, including the Fault Diagnosis Expert System with artificial intelligence. Ontology as expression of domain knowledge can provide a new method for building expert system. Focusing on fault diagnosis of automotive engine, Domain- ontology-building, Fault-Diagnosis-System-Construction?» and System- Reasoning-Description?» are discussed in this paper, based on these, a complete fault diagnosis expert

  3. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

    PubMed Central

    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

  4. The Broadcast Comparison Model for On-Line Fault Diagnosis in Multicomputer Systems

    E-print Network

    Blough, Douglas M.

    The Broadcast Comparison Model for On-Line Fault Diagnosis in Multicomputer Systems: Theory been considered to be a practical approach for on-line fault diagnosis in multicomputer systems then performs diagnosis using the outcomes of these comparisons. An alternative approach to on-line fault

  5. Sensor Minimization Problems with Static or Dynamic Observers for Fault Diagnosis

    E-print Network

    Paris-Sud XI, Université de

    Sensor Minimization Problems with Static or Dynamic Observers for Fault Diagnosis (Extended Abstract) Franck Cassez Stavros Tripakis Karine Altisen§ Abstract We study sensor minimization problems in the context of fault diagnosis. Fault diagnosis consists of synthesizing a diagnoser that observes a given

  6. Fault Diagnosis System for a Multilevel Inverter Using a Neural Network Surin Khomfoi Leon M. Tolbert

    E-print Network

    Tolbert, Leon M.

    Fault Diagnosis System for a Multilevel Inverter Using a Neural Network Surin Khomfoi Leon M, TN 37996-2100, USA surin@utk.edu tolbert@utk.edu Abstract ­ In this paper, a fault diagnosis system to the fault diagnosis of a MLID system. Five multilayer perceptron (MLP) networks are used to identify

  7. Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network Surin Khomfoi

    E-print Network

    Tolbert, Leon M.

    Fault Diagnosis System for a Multilevel Inverter Using a Principal Component Neural Network Surin Engineering 414 Ferris Hall, Knoxville, TN 37996-2100, USA Email: tolbert@utk.edu Abstract-- A fault diagnosis classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron (MLP) networks are used

  8. A Bayesian Approach to Efficient Diagnosis of Incipient Faults Indranil Roychoudhury, Gautam Biswas and Xenofon Koutsoukos

    E-print Network

    Roychoudhury, Indranil

    the algorithms for incipient fault diagnosis. Sec- tion 6 presents results of applying this approach to a two tank system and conclusions are presented in Section 7. 2 Incipient Fault Diagnosis A completeA Bayesian Approach to Efficient Diagnosis of Incipient Faults Indranil Roychoudhury, Gautam Biswas

  9. Electrical equipment fault diagnosis system based on the decomposition products of SF6

    Microsoft Academic Search

    Xin Ning; Liyan Tian; Xiaoguang Hu

    2009-01-01

    This paper presents electrical equipment fault diagnosis system based on the decomposition products of SF6, and makes an introduction of a method that electrical equipment fault diagnosis system of hardware and software implementation. The hardware uses ATmega128 series single-chip platform, and the software uses advanced wavelet neural network fault diagnosis method. To prove the superiority of this algorithm, we make

  10. A Fault Diagnosis Approach for Rolling Bearings Based on EMD Method and Eigenvector Algorithm

    Microsoft Academic Search

    Jinyu Zhang; Xianxiang Huang

    2008-01-01

    Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation

  11. A .NET framework for an integrated fault diagnosis and failure prognosis architecture

    Microsoft Academic Search

    Chaochao Chen; Douglas Brown; Chris Sconyers; George Vachtsevanos; Bin Zhang; Marcos E. Orchard

    2010-01-01

    This paper presents a .NET framework as the integrating software platform linking all constituent modules of the fault diagnosis and failure prognosis architecture. The inherent characteristics of the .NET framework provide the proposed system with a generic architecture for fault diagnosis and failure prognosis for a variety of applications. Functioning as data processing, feature extraction, fault diagnosis and failure prognosis,

  12. Application of Hilbert-Huang transform and Mahalanobis-Taguchi System in mechanical fault diagnostics using vibration signals

    Microsoft Academic Search

    Ren Jiangtao; Cai Yuanwen; Xing Xiaochen

    2011-01-01

    As for non-destructive condition monitoring method, vibration signals are measured and analyzed to diagnose mechanical faults. A method of mechanical fault levels identification is proposed in this paper using vibration signals. Firstly, Hilbert-Huang transform is used to analyze the signal. Fault information is obtained. And energy ratio of intrinsic mode functions is taken as fault characteristics. Secondly, different levels of

  13. A PARAMETER ESTIMATION METHOD FOR THE DIAGNOSIS OF SENSOR OR ACTUATOR ABRUPT FAULTS

    E-print Network

    Paris-Sud XI, Université de

    A PARAMETER ESTIMATION METHOD FOR THE DIAGNOSIS OF SENSOR OR ACTUATOR ABRUPT FAULTS P. Weber and S.Gentil@inpg.fr Keywords: Fault detection; fault isolation; sensor and actuator abrupt faults; parameter estimation. Abstract This paper describes a method for additive abrupt fault detection and isolation. Parameter

  14. Application of Genetic Algorithms and Possibility Theory in Rolling Bearing Compound Fault Diagnosis

    Microsoft Academic Search

    Luo Zhi-gao; Pang Chao-li; Chen Bao-lei; Chen Peng

    2010-01-01

    The characteristic parameters of mechanical fault are found, on the basis of characteristic component collection according to wavelet transform, through optimizing the commonly-used characteristic parameters reflecting rolling bearing fault by genetic algorithms theory. The relationship between the characteristic fault and the mode of fault is created based on the possibility theory. The article also studies the successive fault diagnosis method

  15. Current Issues in Vibration-Based Fault Diagnostics and Prognostics Victor Giurgiutiu

    E-print Network

    Giurgiutiu, Victor

    and then classification. Keywords: structural health monitoring, active sensors, vibrations, signal analysis; smart and prognostics is an important component of structural health monitoring (SHM). Two aspects of vibration1 Current Issues in Vibration-Based Fault Diagnostics and Prognostics Victor Giurgiutiu Mechanical

  16. Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Wang, Jun; He, Qingbo

    2014-05-01

    The vibration or acoustic signal from rotating machinery with localized fault usually behaves as the form of amplitude modulation (AM) and/or frequency modulation (FM). The demodulation techniques are conventional ways to reveal the fault characteristics from the analyzed signals. One of these techniques is the time-scale manifold (TSM) ridge demodulation method with the merits of good time-frequency localization and in-band noise suppression properties. However, due to the essential attribute of wavelet ridge, the survived in-band noise on the achieved TSM will still disturb the envelope extraction of fault-induced impulses. This paper presents an improved TSM ridge demodulation method, called exchanged ridge demodulation of TSM, by combining the benefits of the first two TSMs: the noise suppression of the first TSM and the noise separation of the second TSM. Specifically, the ridge on the second TSM can capture the fault-induced impulses precisely while avoiding the in-band noise smartly. By putting this ridge on the first TSM, the corresponding instantaneous amplitude (IA) waveform can represent the real envelope of pure faulty impulses. Moreover, an adaptive selection method for Morlet wavelet parameters is also proposed based on the smoothness index (SI) in the time-scale domain for an optimal time-scale representation of analyzed signal. The effectiveness of the proposed method is verified by means of a simulation study and applications to diagnosis of bearing defects and gear fault.

  17. Intelligent Joint Fault Diagnosis of Industrial Robots

    NASA Astrophysics Data System (ADS)

    Pan, M.-C.; Van Brussel, H.; Sas, P.

    1998-07-01

    The dynamic behaviour of high-performance mechanical systems such as robots is strongly influenced by the characteristics of the link joints. Joint backlash as a result of wear due to severe stress imposed on the transmission system degrades the robot performance. This paper presents a systematic methodology to diagnose the joint-backlash of a robot by monitoring its vibration response during normal operations. To indicate the reversal of motion of a robot link, and to characterise the spectral patterns of vibration signatures, non-stationary time-frequency analysis algorithms have been employed, which illustrate the signature in a simultaneous time-frequency plane. Significant features are extracted from time domain analysis (probability density moments), and from time-frequency domain analysis (local energy calculations). Artificial neural networks are used as tools for pattern recognition. Experimental results show that the proposed techniques can analyse single-joint backlash quantitatively. Moreover, the described methods also allow to single out backlash in the individual joints in case of multiple-joint backlash.

  18. A diagnosis system using object-oriented fault tree models

    NASA Technical Reports Server (NTRS)

    Iverson, David L.; Patterson-Hine, F. A.

    1990-01-01

    Spaceborne computing systems must provide reliable, continuous operation for extended periods. Due to weight, power, and volume constraints, these systems must manage resources very effectively. A fault diagnosis algorithm is described which enables fast and flexible diagnoses in the dynamic distributed computing environments planned for future space missions. The algorithm uses a knowledge base that is easily changed and updated to reflect current system status. Augmented fault trees represented in an object-oriented form provide deep system knowledge that is easy to access and revise as a system changes. Given such a fault tree, a set of failure events that have occurred, and a set of failure events that have not occurred, this diagnosis system uses forward and backward chaining to propagate causal and temporal information about other failure events in the system being diagnosed. Once the system has established temporal and causal constraints, it reasons backward from heuristically selected failure events to find a set of basic failure events which are a likely cause of the occurrence of the top failure event in the fault tree. The diagnosis system has been implemented in common LISP using Flavors.

  19. A distributed expert system for fault diagnosis

    SciTech Connect

    Cardozo, E.; Talukdar, S.N.

    1988-05-01

    This paper describes a hybrid approach to synthesizing solutions for diagnosis and set covering problems from the area of power system operations. The approach combines expert systems written in a rule-based language (OPS5) with algorithmic programs written in C and Lisp. An environment called DPSK has been developed to allow these programs to be run in parallel in a network of computers. Speeds sufficient for real-time applications can thereby be obtained.

  20. Fault Diagnosis of Power Systems Using Intelligent Systems

    NASA Technical Reports Server (NTRS)

    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 from the rest of the system. The benefit of these studies provides NASA with the ability to quickly restore the operating status of a space station from a critical state to a safe degraded mode, thereby saving costs in experimentation rescheduling, fault diagnostics, and prevention of loss-of-life.

  1. Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis

    NASA Astrophysics Data System (ADS)

    Wang, Tianyang; Liang, Ming; Li, Jianyong; Cheng, Weidong

    2014-03-01

    Order tracking based on time-frequency representation (TFR) is one of the most effective methods for gear fault detection under time-varying rotational speed without using a tachometer. However, for a rolling element bearing, the signal components related to rotational speed usually cannot be directly extracted from the TFR. As such, we propose a new method to solve this problem. This method consists of four main steps: (a) signal filtering via fast spectral kurtosis (SK) analysis - this together with the short time Fourier transform (STFT) leads to a TFR of the filtered signal with clear fault-revealing trend lines, (b) extraction of instantaneous fault characteristic frequency (IFCF) from the TFR using an amplitude-sum based spectral peak search algorithm, (c) signal resampling based on the extracted IFCF to convert the non-stationary time-domain signal into the stationary fault phase angle (FPA) domain signal, and (d) transform of the FPA domain signal into the domain of the fault characteristic order (FCO) and identification of fault type from the FCO spectrum. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals.

  2. On fault-tolerant structure, distributed fault-diagnosis, reconfiguration, and recovery of the array processors

    SciTech Connect

    Hosseini, S.H.

    1989-07-01

    The increasing need for the design of high-performance computers has led to the design of special purpose computers such as array processors. This paper studies the design of fault-tolerant array processors. First, it is shown how hardware redundancy can be employed in the existing structures in order to make them capable of withstanding the failure of some of the array links and processors. Then distributed fault-tolerance schemes are introduced for the diagnosis of the faulty elements, reconfiguration, and recovery of the array. Fault tolerance is maintained by the cooperation of processors in a decentralized form of control without the participation of any type of hardcore or fault-free central controller such as a host computer.

  3. Parametric fault diagnosis for electrohydraulic cylinder drive units

    Microsoft Academic Search

    Hong-Zhou Tan; Nariman Sepehri

    2002-01-01

    A novel model-based methodology for fault diagnosis (FD) of nonlinear hydraulic drive systems is presented in this paper. Due to its linear dependence upon parameters, a second-truncated Volterra nonlinear model is first used to characterize such systems. The versatile order-recursive estimation scheme is employed to determine the values of parameters in the Volterra model. The scheme also avoids separate determination

  4. Fault diagnosis of the ELVE Intelligent Electrical Control System

    Microsoft Academic Search

    Li Zhongnian; Zhou Jiaqi; He Chao

    2007-01-01

    AHDI (automotive high-energy dry ignition-coil) discussed in this paper is a new type production of modern automotive electrical equipment. The process of epoxy-resin liquid vacuum encapsulation is the important link of AHDI's manufacturing and shaping. Therefore, fault diagnosis setting based on BP-NN (back propagation-neural networks) is used in the intelligent pouring electrical control system in order to ensure processing quality.

  5. Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight

    Microsoft Academic Search

    Amitabh Barua; Khashayar Khorasani

    2011-01-01

    Current spacecraft health monitoring and fault- diagnosis practices involve around-the-clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multiplatform space missions due the size of the telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the operations team personnel. The need for efficient utilization of telemetry

  6. Entropy Measure and Energy Map in Machine Fault Diagnosis

    Microsoft Academic Search

    R. Tafreshi; F. Sassani; H. Ahmadi; G. Dumont

    \\u000a This paper presents a novel wavelet-based methodology for fault diagnosis and classification. To compare the performance of\\u000a the proposed approach with major existing methods, various sets of real-world machine data acquired by mounting accelerometer\\u000a sensors on the cylinder head have been extensively tested. The developed method not only avoids the demerits of the previous\\u000a techniques but also demonstrates superior performance.

  7. Multiscale morphology analysis and its application to fault diagnosis

    Microsoft Academic Search

    Lijun Zhang; Jinwu Xu; Jianhong Yang; Debin Yang; Dadong Wang

    2008-01-01

    A novel approach to fault diagnosis is proposed using multiscale morphology analysis to extract impulsive features from the signals with strong background noise. Multiscale morphology is applied to one-dimensional signal by defining both the length and height scales of structuring elements (SEs). A local-peak-value based adaptive algorithm is also introduced. The new approach makes the selection of SEs more transparent

  8. Semi-supervised learning and condition fusion for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Yuan, Jin; Liu, Xuemei

    2013-07-01

    Supervised learning has been developed to collect condition monitoring (CM) data for fault diagnosis and prognosis. However, labeling the condition monitoring data is expensive due to the use of field knowledge while unlabeled CM data contain significant information of normal conditions or faults, which cannot be explored by supervised learning. Manifold regularization (MR) based semi-supervised learning (SSL) is first introduced to fault detection by utilizing both labeled and unlabeled CM data, and then a new single-conditions labeled mode based on MR is proposed for SSL learning. This approach, leveraged by effectively exploiting the embedded intrinsic geometric manifolds, outperforms supervised learning in both single-conditions labeled and all-conditions labeled modes within the application of two real-life fault detection datasets. The experimental results also suggest that most effective classifier in practical application could be trained by the SSL approach and fault type representation with medium load condition. The improved predictive performance implies that the manifold assumption of MR has its inherent fundamentals. Finally, the manifold fundamental of single-conditions labeled mode is analyzed with dimensionality reduction.

  9. Reconfigurable control system design for fault diagnosis and accommodation.

    PubMed

    Ho, Liang-Wei; Yen, Gary G

    2002-12-01

    The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on the type of system dynamical structure or failure scenarios. The sufficient conditions for system on-line stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon the existing control theory and the modern computational intelligence techniques, an on-line fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Theoretical analysis indicates that the control problem of interest can be solved on-line without a complete realization of the unknown failure dynamics provided an on-line estimator satisfies certain conditions. Through the on-line estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. Several on-line simulation studies have been presented to demonstrate the effectiveness of the proposed strategy. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in hardware under the real-time environment, an on-line fault tolerant control test bed has been constructed to validate the proposed technology. Both on-line simulations and the real-time experiment show encouraging results and promising futures of on-line real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure dynamics. PMID:12528199

  10. A History-Based Diagnosis Technique for Static and Dynamic Faults in SRAMs

    Microsoft Academic Search

    A. Ney; A. Bosio; L. Dilillo; P. Girard; S. Pravossoudovitch; A. Virazel; M. Bastian

    2008-01-01

    The usual techniques for memory diagnosis are mainly based on signature analysis. They consist in creating a fault dictionary that is used to determine the correspondence between the signature and the fault models affecting the memory. The effectiveness of such diagnosis methods is therefore strictly related to the fault dictionary accuracy. To the best of our knowledge, most of existing

  11. Fault Diagnosis for Wireless Sensor Network's Node Based on Hamming Neural Network and Rough Set

    Microsoft Academic Search

    Lin Lei; Hou-jun Wang; Chuan-long Dai

    2008-01-01

    To accurately diagnose node fault in wireless sensor network (WSN) can improve long-distance service of nodes in WSN, assure reliability of information transfer and prolong lifetime of WSN. In this paper, a novel method of fault diagnosis for node of WSN was brought forward. First, attribute reduction for decision-making of fault diagnosis could be founded based discernibility matrix in rough

  12. Trends in the application of model-based fault detection and diagnosis of technical processes

    Microsoft Academic Search

    R. Isermann; P. Ballé

    1997-01-01

    After a short overview of the historical development of model-based fault detection, some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated, based on the work within the IFAC SAFEPROCESS Technical Committee. Some basic fault-detection and diagnosis methods are briefly considered. Then, an evaluation of publications during the last 5 years shows some trends

  13. Multiple Stuck-at Fault Diagnosis in Logic Circuits Youns KARKOURI, El Mostapha ABOULHAMID

    E-print Network

    Aboulhamid, El Mostapha

    ­ 1 ­ Multiple Stuck-at Fault Diagnosis in Logic Circuits Younès KARKOURI, El Mostapha ABOULHAMID" Montréal, (Québec), H3C-3J7, Canada. ABSTRACT A new method to fault diagnosis in combinational circuits is presented. We consider multiple stuck-at-(0/1) faults at the gate level. We introduce the concept

  14. An approach for Fault Diagnosis based on bio-inspired strategies

    Microsoft Academic Search

    Lídice Camps Echevarría; Orestes Lianes Santiago; Antônio José da Silva Neto

    2010-01-01

    In this work we present a study on the application of bio-inspired strategies for optimization to Fault Diagnosis in industrial systems. The principal aim is to establish a basis for the development of new and viable model-based Fault Diagnosis Methods which improve some difficulties that the current methods cannot avoid. These difficulties are related with fault sensitivity and robustness to

  15. Fault detection and diagnosis for three-tank system using robust residual generator

    Microsoft Academic Search

    A. Asokan; D. Sivakumar

    2009-01-01

    Fault detection and diagnosis (FDD) is a task to deduce from observed variable of the system if any component is faulty, to locate the faults and also to estimate the fault magnitude present in the system. The main goal when synthesizing robust residual generators, for diagnosis and supervision, is to attenuate influence from model uncertainty on the residuals while keeping

  16. A Fault Diagnosis Approach for Rolling Bearings Based on Enhanced Blind Equalization Theory

    Microsoft Academic Search

    Jinyu Zhang; Xianxiang Huang

    2008-01-01

    Fault diagnosis of rolling bearings remains a very important and difficult research task in engineering and technique. After analyzing the shortcoming of current bearings fault diagnosis technologies, a novel enhanced blind equalization (BE) technology based on wavelet packet (WP) analysis and eigenvector algorithm (EVA) was proposed to extract directly impacting features and diagnose bearings' faults in this paper. First, the

  17. Rolling Bearing Fault Diagnosis Based on the Hybrid Algorithm of Particle Swarm Optimization with Neighborhood Operator

    Microsoft Academic Search

    Jia-tang Cheng; Li Ai; Wei Xiong

    2012-01-01

    In order to improve the accuracy of rolling bearing fault diagnosis, a hybrid algorithm of particle swarm optimization with neighborhood operator is applied. According to the fault feature vectors, PSO with neighborhood operator is applied to optimize the weight of BP neural network, then the fault diagnosis is accomplished via the optimized neural network. The simulation results show that this

  18. Bearing fault diagnosis based on negative selection algorithm of feature extraction and neural network

    Microsoft Academic Search

    Xiaoping Ma; Xiaobin Wei; Fengshuan An; Peizhao Su

    2010-01-01

    As the fault diagnosis based on neural network needs typical features samples, the paper proposes a hybrid fault diagnosis method which integrates the RNSA (real-valued negative selection algorithm) and the radial basis function network. In this method, we choose typical fault samples (generated by Negative selection algorithm) as the inputs of the neural network, which solves the difficulty of obtaining

  19. Diagnosis of Incipient Sensor Faults in a Flight Control Actuation System

    Microsoft Academic Search

    Jayakumar MlandBijan; B. B. Das

    2006-01-01

    This paper presents a scheme for fault diagnosis in a flight control actuation system. The electromechanical control actuator considered here is based on a DC torque motor. The scheme utilizes the analytical redundancy that exists in the system between the linear actuator position, motor shaft angular velocity and motor current for diagnosis of incipient sensor faults in the system. Fault

  20. A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box

    Microsoft Academic Search

    N. Saravanan; V. N. S. Kumar Siddabattuni; K. I. Ramachandran

    2008-01-01

    The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. This paper deals with the effectiveness of wavelet-based features for fault diagnosis using support vector machines (SVM) and proximal support vector machines (PSVM). The statistical

  1. A new condition monitoring and fault diagnosis method of engine based on spectrometric oil analysis

    Microsoft Academic Search

    Jingwei Gao; Peilin Zhang; Guoquan Ren; Jianping Fu; Ji Li

    According to statistics, wear fault is about 60–80% of all the machinery faults. Spectrometric oil analysis is an important condition monitoring technique for machinery maintenance and fault diagnosis. Now, there are two existing mathematics analysis models based on spectrometric oil analysis, namely concentration model and gradient model. However, the above two models have respective disadvantages in condition monitoring and fault

  2. On-Line Fault Diagnosis of Dynamic Systems via Robust Parameter Identification Gerard Blocha

    E-print Network

    Paris-Sud XI, Université de

    On-Line Fault Diagnosis of Dynamic Systems via Robust Parameter Identification G´erard Blocha of faults, their isolation and their identification is presented. The systems considered are MISO systems knowledge of the faults which can occur is used. The faults modeled here are outliers, biases or drifts

  3. Exploitation of Built in test for diagnosis by using Dynamic Fault Trees: Implementation in Matlab Simulink

    E-print Network

    Paris-Sud XI, Université de

    Exploitation of Built in test for diagnosis by using Dynamic Fault Trees: Implementation in Matlab Fourier, Grenoble, FRANCE ABSTRACT: This paper presents the purpose of Dynamic Fault Tree (DFT) in Matlab dynamic rules as temporal and dy- namic fault trees. The fault tree (FT) method is a technique used

  4. An intelligent and efficient fault location and diagnosis scheme for radial distribution systems

    Microsoft Academic Search

    Seung-Jae Lee; Myeon-Song Choi; Sang-Hee Kang; Bo-Gun Jin; Duck-Su Lee; Bok-Shin Ahn; Nam-Seon Yoon; Ho-Yong Kim; Sang-Bong Wee

    2004-01-01

    In this paper, an effective fault location algorithm and intelligent fault diagnosis scheme are proposed. The proposed scheme first identifies fault locations using an iterative estimation of load and fault current at each line section. Then an actual location is identified, applying the current pattern matching rules. If necessary, comparison of the interrupted load with the actual load follows and

  5. Development of an Automated Fault Detection and Diagnosis tool for AHU's 

    E-print Network

    Bruton, K.; Raftery, P.; Aughney, N.; Keane, M.; O'Sullivan, D.

    2012-01-01

    -commissioning HVAC systems to rectify faulty operation with savings of over 20 percent of total energy cost possible by continuously commissioning. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults...

  6. GAS TURBINE FAULT IDENTIFICATION BY FUSING VIBRATION TRENDING AND GAS PATH ANALYSIS

    Microsoft Academic Search

    A. Kyriazis; A. Tsalavoutas; K. Mathioudakis; M. Bauer; O. Johanssen

    2009-01-01

    A fusion method that utilizes performance data and vibration measurements for gas turbine component fault identification is presented. The proposed method operates during the diagnostic processing of available data (process level) and adopts the principles of certainty factors theory. Both performance and vibration measurements are analyzed separately, in a first step, and their results are transformed into a common form

  7. Intelligent model-free diagnosis for multiple faults in a nonlinear dynamic system

    Microsoft Academic Search

    Paul P. Lin; Hardeep Singh

    2007-01-01

    In terms of fault diagnosis, there are two general approaches: model-based and model-free. This paper presents the fault diagnosis techniques for a nonlinear dynamic system with multiple faults using the model-free approach. A new concept for fault detection by means of a real-time tracker was employed to predict the system outputs from which the residuals could be quickly generated. To

  8. Attributes of the interface affect fault detection and fault diagnosis in supervisory control

    Microsoft Academic Search

    Torsten Heinbokel; Rainer H. Kluwe

    In this paper, a cognitive psychological framework of human-machine interaction, the methodological approach and the results\\u000a of two empirical studies, are reported. The research was directed at the goal of identifying interface attributes which are\\u000a assumed to be crucial for fault detection and diagnosis during supervisory control. In the first study, it was assumed that\\u000a due to attributes of standard

  9. Fault diagnosis for diesel valve trains based on non-negative matrix factorization and neural network ensemble

    NASA Astrophysics Data System (ADS)

    Qinghua, Wang; Youyun, Zhang; Lei, Cai; Yongsheng, Zhu

    2009-07-01

    It is well known that the vibration signals are unstable when there is some failure in machinery. So in this paper, the cone-shaped kernel distributions (CKD) of vibration acceleration signals acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images. Meanwhile, non-negative matrix factorization (NMF) was used to decompose multivariate data, and neural network ensemble (NNE), which is of better generalization capability for classification than a single neural network, was used to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from time-frequency distributions. It is shown by the experimental results that the faults of diesel valve trains can be accurately classified by the proposed method.

  10. An extended qualitative multi-faults diagnosis from first principles I: Theory and modelling

    Microsoft Academic Search

    He-xuan Hu; Anne-Lise Gehin; Mireille Bayart

    2009-01-01

    This paper is part I of a two part effort that is intended to present a framework of multi-faults diagnosis. Reiter has proposed a consistency-based approach for multi-faults diagnosis. We extend his theory to deal with the dynamic and continuous systems and offer a necessary assumption and a formal demonstration. Multi-faults diagnosis is a partially observable problem because there is

  11. Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis

    Microsoft Academic Search

    Sinan Altug; Mo-Yuen Chen; H. Joel Trussell

    1999-01-01

    Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection\\/diagnosis process and successful fault detection\\/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN\\/FZ) inference systems, namely, fuzzy adaptive learning control\\/decision

  12. Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis

    E-print Network

    Chen, Sheng

    component analysis Local structure analysis Fault diagnosis Fault detection Fault identification Nonlinear based on local structure analysis for nonlinear process fault diagnosis. In order to extract data are constructed based on the idea of sensitivity analysis to locate the fault variables. Simulation using

  13. From Modelica Models to Fault Diagnosis in Air Handling Units Raymond Sterling1

    E-print Network

    Cengarle, María Victoria

    , fault detection and diagnosis 1 Introduction Heating Ventilation and Air conditioning (HVAC) systems periods due to different factors: compensations made by the control algorithms of other elements belonging

  14. SOM Neural Network Fault Diagnosis Method of Polymerization Kettle Equipment Optimized by Improved PSO Algorithm

    PubMed Central

    Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

    2014-01-01

    For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective. PMID:25152929

  15. SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

    PubMed

    Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

    2014-01-01

    For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective. PMID:25152929

  16. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram

    NASA Astrophysics Data System (ADS)

    Zhang, Yongxiang; Randall, R. B.

    2009-07-01

    The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. However, much practical diagnostic equipment for carrying out the analysis gives little flexibility to change the analysis parameters for different working conditions, such as variation in rotating speed and different fault types. Because the signals from a flawed bearing have features of non-stationarity, wide frequency range and weak strength, it can be very difficult to choose the best analysis parameters for diagnosis. However, the kurtosis of the vibration signals of a bearing is different from normal to bad condition, and is robust in varying conditions. The fast kurtogram gives rough analysis parameters very efficiently, but filter centre frequency and bandwidth cannot be chosen entirely independently. Genetic algorithms have a strong ability for optimization, but are slow unless initial parameters are close to optimal. Therefore, the authors present a model and algorithm to design the parameters for optimal resonance demodulation using the combination of fast kurtogram for initial estimates, and a genetic algorithm for final optimization. The feasibility and the effectiveness of the proposed method are demonstrated by experiment and give better results than the classical method of arbitrarily choosing a resonance to demodulate. The method gives more flexibility in choosing optimal parameters than the fast kurtogram alone.

  17. Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

    PubMed

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

    2014-01-01

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

  18. Fault Detection and Diagnosis System for the Air-conditioning

    NASA Astrophysics Data System (ADS)

    Nakahara, Nobuo

    The fault detection and diagnosis system, the FDD system, for the HVAC was initiated around the middle of 1970s in Japan but it still remains at the elementary stage. The HVAC is really one of the most complicated and large scaled system for the FDD system. Besides, the maintenance engineering was never focussed as the target of the academic study since after the war, but the FDD system for some kinds of the components and subsystems has been developed for the sake of the practical industrial needs. Recently, international cooperative study in the IEA Annex 25 on the energy conservation for the building and community targetted on the BOFD, the building optimization, fault detection and diagnosis. Not a few academic peaple from various engineering field got interested and, moreover, some national projects seem to start in the European countries. The author has reviewed the state of the art of the FDD and BO as well based on the references and the experience at the IEA study.

  19. A distributed fault-detection and diagnosis system using on-line parameter estimation

    NASA Technical Reports Server (NTRS)

    Guo, T.-H.; Merrill, W.; Duyar, A.

    1991-01-01

    The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

  20. Fault tree analysis is widely used in industry for fault diagnosis. The diagnosis of incipient or `soft' faults is

    E-print Network

    Madden, Michael

    failure, it can predict what the most likely causes of the failure are by evaluating all combinations of basic events (e.g. component failures) which can lead to a top event (a particular fault formalised into a `struc- turing process' based on a functional model of the plant using schematics, piping

  1. Fault Diagnosis with Progressive Symptoms Based on Multi-Agent Approach

    Microsoft Academic Search

    Oleksandr Sokolov; Michael Wagenknecht; Ulrike Gocht

    2007-01-01

    The paper is devoted to fault diagnosis pro- blems using fuzzy decision making. We investigate dynamic diagnostic systems which can be represented by symptom-fault rule bases. The main question to be ans- wered is what faults produce observable symptoms in the first moments of their ap- pearance. To solve this task we propose to use a set of agents for

  2. Transient and Permanent Fault Diagnosis for FPGA-Based TMR Systems

    Microsoft Academic Search

    Sergio D'angelo; Giacomo R. Sechi; Cecilia Metra

    1999-01-01

    In this paper we propose a hardware scheme to allow the diagnosis of transient and permanent faults affecting a Triple Modular Redundancy (TMR) system implemented by means of Field Programmable Gate Arrays (FPGAs). Our scheme allows us to easily identify whether a fault affects one of the replicated modules, the voter, or the scheme itself; and whether such a fault

  3. Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process

    E-print Network

    Paris-Sud XI, Université de

    Fault Diagnosis with Bayesian Networks: Application to the Tennessee Eastman Process Sylvain VERRON problem: the Tennessee Eastman Process. Three kinds of faults are taken into account on this complex process. The objective is to obtain the minimal recognition error rate for these 3 faults. Results

  4. Fault diagnosis in nonlinear systems: An application to a three-tank system

    Microsoft Academic Search

    J. Juan Rincon-Pasaye; Rafael Martinez-Guerra; Alberto Soria-Lopez

    2008-01-01

    The fault diagnosis problem for nonlinear systems is treated, some results based on a differential algebraic approach are used in order to determine fault diagnosability with the minimum number of measurements from the system. Two schemes of nonlinear observers are used for reconstructing the fault signals for comparison purposes, one of them being a reduced order observer and the other

  5. A Bayesian Approach to Efficient Diagnosis of Incipient Faults Indranil Roychoudhury, Gautam Biswas and Xenofon Koutsoukos

    E-print Network

    Koutsoukos, Xenofon D.

    A Bayesian Approach to Efficient Diagnosis of Incipient Faults Indranil Roychoudhury, Gautam Biswas. Degradations are typically modeled as in- cipient faults, which are slow drifts in system para- meters over of incipient faults under uncertainty using a Dynamic Bayesian Network (DBN) approach. Ini- tially a DBN

  6. A novel approach for fault diagnosis of power transformers based on extracting invariant moments

    Microsoft Academic Search

    M. F. El-Naggar; A. M. Hamdy; S. M. Moussa; E. H. Shehab El-Din

    2008-01-01

    This paper presents a new technique for diagnosis of power transformer faults, which have been previously detected. This technique is based on image analysis for identification of different fault types in power transformers, such as single line to ground, double line to ground, line to line, and symmetrical fault. Also, the technique can clearly identify the index of the faulty

  7. A fault diagnosis mechanism for a proactive maintenance scheme for wireless systems

    Microsoft Academic Search

    Barbara Walsh; Ronan Farrell

    2008-01-01

    This paper presents the fault diagnosis mechanism for a proactive maintenance scheme for wireless systems. Its objective is to reduce the high operational costs encountered in the wireless industry by decreasing maintenance costs and system downtime. The fault diagnosis mechanism is based on the symbol frequency. An analytical method to calculate the symbol frequency is presented. The on-line monitoring system,

  8. Fault Diagnosis of Steam Generator Using Signed Directed Graph and Artificial Neural Networks

    Microsoft Academic Search

    Mohamed N. Aly; Hesham N. Hegazy

    2006-01-01

    Diagnosis is a very complex and important task for finding the root cause of faults in nuclear power plants. The objective of this paper is to investigate the feasibility of using the combination of signed directed graph (SDG) and artificial neural networks for fault diagnosis in nuclear power plants especially in U-Tube steam generator. Signed directed graph has been the

  9. A novel fault diagnosis for vehicles based on time-varied Bayesian network modeling

    Microsoft Academic Search

    Wenqiang Guo; Zoe Zhu; Yongyan Hou

    2011-01-01

    Aiming at one of the key issues in vehicle fault diagnosis underlying time series, modeling the varying diagnosis network structures is investigated in this paper. By incorporating machine learning techniques with the Bayesian network's advantage of handling the inference in large, noisy and uncertain data, an innovative method based on modeling the varied-time Bayesian network?ƒ BN?? for automotive vehicle fault

  10. Transients Analysis of a Nuclear Power Plant Component for Fault Diagnosis

    E-print Network

    Paris-Sud XI, Université de

    Transients Analysis of a Nuclear Power Plant Component for Fault Diagnosis Piero Baraldia.baraldi@polimi.it, We analyze signal data collected during 148 shut-down transients of a nuclear power plant (NPP) turbine for fault diagnosis. The aim is to identify groups of transients with similar characteristics

  11. Colored timed Petri net based statistical process control and fault diagnosis to flexible manufacturing systems

    Microsoft Academic Search

    Chung-Hseng Kuo; Han-Pang Huang

    1997-01-01

    The quality consistence and machine utilization of a flexible manufacturing system (FMS) strongly depend on the statistical process control (SPC) and the correct fault diagnosis of equipment. An FMS can be modelled by the colored timed Petri net (CTPN). However, most CTPN models of FMS lack the activities of SPC and fault diagnosis, and they lead to incomplete FMS CTPN

  12. ADVANCES IN MODEL-BASED FAULT DIAGNOSIS WITH EVOLUTIONARY ALGORITHMS AND NEURAL NETWORKS

    Microsoft Academic Search

    MARCIN WITCZAK

    2006-01-01

    Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding

  13. Fault Diagnosis, Prognosis and Self-Reconfiguration for Nonlinear Dynamic Systems Using Soft Computing Techniques

    Microsoft Academic Search

    P. P. Lin; Xiaolong Li

    2006-01-01

    Diagnosis and prognosis are processes of assessment of a system's health. The former is an assessment about the current health of a system based on observed symptoms, while the latter is an assessment of the future health. System reconfiguration or accommodation is essential once faults are detected and identified. This paper presents model-free fault diagnosis, prognosis and self-reconfiguration using soft

  14. The application of wavelet packet and SVM in rolling bearing fault diagnosis

    Microsoft Academic Search

    Meng Li; Ping Zhao

    2008-01-01

    The method of fault diagnosis of rolling bearings based on wavelet packet transform and support vector machine is presented. The key to fault bearings diagnosis is feature extracting and feature classifying. Wavelet packet transform, as a new technique of signal processing, possesses excellent characteristic of time-frequency localization and is suitable for analyzing the time-varying or transient signals. Support vector machine

  15. A signature-based approach for diagnosis of dynamic faults in SRAMs

    Microsoft Academic Search

    A. Ney; A. Bosio; L. Dilillo; P. Girard; S. Pravossoudovitch; A. Virazel

    2008-01-01

    This paper focuses on diagnosis of dynamic faults in SRAMs. The current techniques for fault diagnosis are mainly based on the signature method. Here, we introduce an extension of the signature scheme by taking in account additional information related to the addressing order during March test execution. A first advantage of the proposed approach is its capability to distinguish between

  16. Synchronous Machine Faults Detection and Diagnosis for Electro-mechanical Actuators

    E-print Network

    Boyer, Edmond

    Detection and Isolation system for permanent magnet synchronous machine (PMSM). Two main faults occurring-circuit, Permanent Magnet Synchronous Machine, Diagnosis. 1. INTRODUCTION Facing the growth of the air transportSynchronous Machine Faults Detection and Diagnosis for Electro-mechanical Actuators in Aeronautics

  17. Detection, diagnosis, and evaluation of faults in vapor compression equipment

    Microsoft Academic Search

    Todd Michael Rossi

    1995-01-01

    This thesis develops techniques for automated detection, diagnostics, and evaluation of faults in vapor compression equipment. Fault evaluation was added to the more common steps of fault detection and diagnostics to consider the special aspects of performance degradation faults over abrupt faults. A model for testing these techniques in a simulation environment was developed. The model is described and experimental

  18. Fault Detection, Diagnosis and Prediction in Electrical Valves Using Self-Organizing Maps

    Microsoft Academic Search

    Luiz Fernando Gonçalves; Jefferson Luiz Bosa; Tiago Roberto Balen; Marcelo Soares Lubaszewski; Eduardo Luis Schneider; Renato Ventura Henriques

    This paper presents a proactive maintenance scheme for fault detection, diagnosis and prediction in electrical valves. The\\u000a proposed scheme is validated with a case study, considering a specific valve used for controlling the oil flow in a distribution\\u000a network. The scheme is based in self-organizing maps, which perform fault detection and diagnosis, and temporal self-organizing\\u000a maps for fault prediction. The

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

    NASA Astrophysics Data System (ADS)

    Xie, Chun-Li; Xia, Hong; Liu, Yong-Kuo

    2005-03-01

    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.

  20. Research and Application of Data Mining in Fault Diagnosis for Big Machines

    Microsoft Academic Search

    Zhigang Chen; Laibin Zhang; Zhaohui Wang; Wei Liang; Qinggang Li

    2007-01-01

    The fault characteristics of big equipments are complex and difficult to distinguish, this paper presents a new method elaborating on selecting more interrelated vibration parameters as original characteristic vectors, and how to mine features from fault database and then analyze running conditions of rotating parts of big machines by applying fuzzy clustering. The theories of establishing models, specific algorithms and

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

    NASA Astrophysics Data System (ADS)

    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.

  2. Fault Diagnosis of Helical Coil Steam Generator Systems of an Integral Pressurized Water Reactor Using Optimal Sensor Selection

    Microsoft Academic Search

    Fan Li; Belle R. Upadhyaya; Sergio R. P. Perillo

    2012-01-01

    Fault diagnosis is an important area in nuclear power industry for effective and continuous operation of power plants. Fault diagnosis approaches depend critically on the sensors that measure important process variables. Allocation of these sensors determines the effectiveness of fault diagnostic methods. However, the emphasis of most approaches is primarily on the procedure to perform fault detection and isolation (FDI)

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

    PubMed

    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. PMID:24981891

  4. A Comparison of Model-based Reasoning and Learning Approaches to Power Transmission Fault Diagnosis

    Microsoft Academic Search

    Ramesh K. Rayudu; Sandhya Samarasinghe; Don Kulasiri

    1995-01-01

    An application of model-based reasoning and model- based learning to an operative diagnostic domain such as electrical power transmission networks is presented. Most of the research in model-based diagnosis is based on maintenance diagnosis. Operative diagnosis, on the other hand, is done while the system is still in operation even after the fault. We plan to develop an efficient algorithm

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

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

  6. Incipient fault diagnosis of chemical processes via artificial neural networks

    Microsoft Academic Search

    Kajiro Watanabe; Ichiro Matsuura; Masahiro Abe; Makoto Kubota; D. M. Himmelblau

    1989-01-01

    Artificial neural networks have capacity to learn and store information about process faults via associative memory, and thus have an associative diagnostic ability with respect to faults that occur in a process. Knowledge of the faults to be learned by the network evolves from sets of data, namely values of steady-state process variables collected under normal operating condition and those

  7. Rotating Machinery Fault Diagnosis Based on Support Vector Machine

    Microsoft Academic Search

    Yajuan Liu; Tao Liu

    2010-01-01

    In order to identify the rotating machinery fault, a method based on support vector machine (SVM) is proposed in this paper. After the feature vectors from the fault signals by means of wavelet packet are extracted and the support vector machine (SVM) classification algorithm to the classification of faults in rolling bearing is applied. By drawing a comparison between the

  8. Wavelet Based Instantaneous Power Analysis for Induction Machine Fault Diagnosis

    Microsoft Academic Search

    Shahin Hedayati Kia; A. Mpanda Mabwe; H. Henao; G.-A. Capolino

    2006-01-01

    The aim of this paper is to present a wavelet based approach to detect broken bar faults in squirrel-cage induction machines. This approach uses instantaneous power as a medium for fault detection. A multi-resolution instantaneous power decomposition based on wavelet transform provides the different frequency bands whose energies are affected directly by the broken bar fault. Actually, the instantaneous power

  9. Subspace-based fault detection algorithms for vibration monitoring

    Microsoft Academic Search

    Michèle Basseville; Maher Abdelghani; Albert Benveniste

    2000-01-01

    We address the problem of detecting faults modeled as changes in the eigenstructureof a linear dynamical system. This problem is of primary interest for structuralvibration monitoring. The purpose of the paper is to describe and analyze newfault detection algorithms, based on recent stochastic subspace-based identificationmethods and the statistical local approach to the design of detection algorithms.

  10. An efficient logic fault diagnosis framework based on effect-cause approach 

    E-print Network

    Wu, Lei

    2009-05-15

    Fault diagnosis plays an important role in improving the circuit design process and the manufacturing yield. With the increasing number of gates in modern circuits, determining the source of failure in a defective circuit is becoming more and more...

  11. On-line implementation of a fault diagnosis system for three-phase induction motors 

    E-print Network

    Alladi, Vijaya Mallikarjun

    2002-01-01

    Condition monitoring of electric machinery has received increased attention due to the advantages it offers in terms of productivity. Automation of the fault detection and diagnosis processes would not only allow for extensive monitoring but also...

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

    SciTech Connect

    Zhao Jinsong [College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029 (China)], E-mail: jinsongzhao@mail.tsinghua.edu.cn; Huang Jianchao [College of Information Science and Technology, Beijing Institute of Technology, Beijing 10086 (China); Sun Wei [College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029 (China)

    2008-11-15

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

  13. A Quantum Neural Networks Data Fusion Algorithm and Its Application for Fault Diagnosis

    Microsoft Academic Search

    Daqi Zhu; Erkui Chen; Yongqing Yang

    2005-01-01

    \\u000a An information fusion algorithm based on the quantum neural networks is presented for fault diagnosis in an integrated circuit.\\u000a By measuring the temperature and voltages of circuit components of mate changing circuit board of photovoltaic radar, the\\u000a fault membership functional assignment of two sensors to circuit components is calculated, and the fusion fault membership\\u000a functional assignment is obtained by using

  14. A general model for the study of fault tolerance and diagnosis.

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.

    1973-01-01

    The concept of a 'system with faults' is introduced as a suggested point of departure for the theoretical study of fault tolerance and diagnosis in systems. The model is defined relative to a general representation scheme for systems and, depending on the choice of representation, can be used to investigate either hardware or software faults that occur during either the design or use of a system.

  15. Handling uncertainty with possibility theory and fuzzy sets in a satellite fault diagnosis application

    Microsoft Academic Search

    Didier CAYRAC; Didier DUBOIS; Henri PRADE

    1996-01-01

    The fault mode effects and criticality analyses (FMECA) describe the impact of identified faults. They form an important category of knowledge gathered during the design phase of a satellite and are used also for diagnosis activities. This paper proposes their extension, allowing a finer representation of the available knowledge, at approximately the same cost, through the introduction of an appropriate

  16. Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine

    E-print Network

    Paris-Sud XI, Université de

    Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine Induction rotor bars and bearing damages. Index Terms--Wind turbines, motor current signature analy- sis, time of maintenance in offshore environment, teledetection of wind turbine faults is becoming a crucial issue

  17. Fault diagnosis of electronic analog circuits using a radial basis function network classifier

    Microsoft Academic Search

    Marcantonio Catelani; Ada Fort

    2000-01-01

    In this paper a fault diagnosis technique, which employs neural networks to analyze signatures of analog circuits, is proposed. Radial basis functions networks (RBFN) are used to process circuit input–output measurements, and to perform soft fault location. Both noise and effect of parameter variations in the tolerance ranges of non-faulty components are taken into account. The network is trained with

  18. On the application of neural networks to fault diagnosis of electronic analog circuits

    Microsoft Academic Search

    M. Catelani; M. Gori

    1996-01-01

    This paper presents a new method for fault diagnosis in linear and non-linear analog circuits that is based on artificial neural networks. A fault dictionary in the frequency domain, previously constructed, represents the set of supervised data for learning. Feedforward networks acting as autoassociators with one hidden layer, trained by backpropagation, are used in order to identify the most likely

  19. Fault diagnosis for a hydraulic drive system using a parameter-estimation method

    Microsoft Academic Search

    D. Yu

    1997-01-01

    Fault diagnosis using a parameter-estimation method is investigated in this paper. A digital state variable filter is employed to obtain derivatives of the variables, and an interpolation technique is used to approximate the values of the variables between samples. The method is applied to a hydraulic test rig based on real data, and the simulated faults — changes in the

  20. A Hierarchical Fault Diagnosis Method Using a Decision Support System Applied to a Chemical Plant

    E-print Network

    Paris-Sud XI, Université de

    A Hierarchical Fault Diagnosis Method Using a Decision Support System Applied to a Chemical Plant D. A hierarchical scheme of fault detection and isolation based on Decision Support System (DSS) is presentedHeuristic Symptoms DECISION Figure 1: Architecture of a Decision Support System This paper is organised as follows

  1. The research on sensor fault diagnosis based on the SVM prediction model

    Microsoft Academic Search

    Yaojun Yu; Shengbo Zhang

    2011-01-01

    A novel method for sensor fault diagnosis based on support vector machine (SVM) prediction model was proposed. This paper put forward the principle of SVM construction process and the system parameters obtained from using dynamic model identification of sensor. The sensor fault was diagnosed on line by prediction model, which avoided that BP algorithm must have mass data and is

  2. Robust fault diagnosis for satellite attitude systems using neural state space models

    Microsoft Academic Search

    Qing Wu; Mehrdad Saif

    2005-01-01

    In this paper, a robust fault detection and diagnosis scheme using neural state space models has been developed for a class of nonlinear systems. The neural state space models are adopted to estimate the modeling uncertainties in the states and outputs of the system. Subsequently, a residual is generated to identify the characteristics of the fault. Moreover, the robustness, sensitivity

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

    Microsoft Academic Search

    Shengfa Yuan; Fulei Chu

    2007-01-01

    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.

  4. A neural-network approach to fault detection and diagnosis in industrial processes

    Microsoft Academic Search

    Yunosuke Maki; Kenneth A. Loparo

    1997-01-01

    Using a multilayered feedforward neural-network approach, the detection and diagnosis of faults in industrial processes that requires observing multiple data simultaneously are studied in this paper. The main feature of our approach is that the detection of the faults occurs during transient periods of operation of the process. A two-stage neural network is proposed as the basic structure of the

  5. On-line diagnosis of incipient faults and cellulose degradation based on artificial intelligence methods

    Microsoft Academic Search

    M. A. Izzularab; G. E. M. Aly; D. A. Mansour

    2004-01-01

    In this paper, a new artificial intelligence technique is proposed to detect incipient faults and cellulose degradation in power transformers using dissolved gas analysis. The proposed technique is based on a combination between neural networks and fuzzy logic theory. Incipient faults diagnosis is based on hydrocarbon gases as an input while cellulose degradation detection is based on carbon monoxide and

  6. Exploratory analysis of massive data for distribution fault diagnosis in smart grids

    Microsoft Academic Search

    Yixin Cai; Mo-Yuen Chow

    2009-01-01

    Fault diagnosis in power distribution systems is critical to expedite the restoration of service and improve the reliability. With power grids becoming smarter, more and more data beyond utility outage database are available for fault cause identification. This paper introduces basic methodologies to integrate and analyze data from different sources. Geographic information system (GIS) provides a framework to integrate these

  7. Wavelet co-efficient of thermal image analysis for machine fault diagnosis

    Microsoft Academic Search

    A. M. Younus; Bo-Suk Yang

    2010-01-01

    The ultimate goal of this study is to introduce a new method of machine fault diagnosis using different machine conditions data such as normal, misalignment, mass-unbalance and bearing-fault from infrared thermography (IRT). Using thermal image, it is easy to obtain information about the machine condition rather than other conventional methods of machine condition diagnostic technique. Thermal image technique can be

  8. Wavelets Neural Network Based on Particle Swarm Optimization Algorithm for Fault Diagnosis

    Microsoft Academic Search

    Changcheng Xiang; Xiyue Huang; Darong Huang; Jia Hu

    2006-01-01

    A systematic method for fault diagnosis of steam-turbine generator sets based on the combination of wavelet neural networks and particle swarm optimization is presented. Using the model of wavelet neural networks, we can not only extract the features of system but also predict the development of the fault. The features are applied to the proposed wavelet neural network and the

  9. SINGULARITY ANALYSIS USING CONTINUOUS WAVELET TRANSFORM FOR BEARING FAULT DIAGNOSIS

    Microsoft Academic Search

    Q. Sun; Y. Tang

    2002-01-01

    In this paper, wavelet transform is applied to detect abrupt changes in the vibration signals obtained from operating bearings being monitored. In particular, singularity analysis across all scales of the continuous wavelet transform is performed to identify the location (in time) of defect-induced bursts in the vibration signals. Through modifying the intensity of the wavelet transform modulus maxima, defect-related vibration

  10. Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN 

    E-print Network

    Zhou, Y.; Zheng, J.; Liu, Z.; Yang, C.; Peng, P.

    2006-01-01

    ICEBO2006, Shenzhen, China Co ntrol Systems for Energy Efficiency and Comfort, Vol. V-5-3 Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN1 Yuli ZHOU Jie ZHENG Zhiju LIU Chaojie YANG Peng PENG... researches on the fault diagnosis of screw chiller. 2 DETECTION MECHANISM The refrigerating cycle of screw chiller is consisted of four thermodynamic cycles which are compression, heat discharging, throttling and heat absorbing. the change of one...

  11. Analog system-level fault diagnosis based on symbolic method in the frequency domain 

    E-print Network

    You, Zhihong

    1993-01-01

    ANALOG SYSTEM ? LEVEL FAULT DIAGNOSIS BASED ON SYMBOLIC METHOD IN THE FREQUENCY DOMAIN A Thesis by ZHIHONG YOU Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree... of MASTER OF SCIENCE August 1993 Major Subject: Electrical Engineering ANALOG SYSTEM ? LEVEL FAULT DIAGNOSIS BASED ON SYMBOLIC METHOD IN THE FREQUENCY DOMAIN A Thesis by ZHIHONG YOU Approved as to style and content by: gar Sanchez ? Sinencio ( Co...

  12. A Qualitive Modeling Approach for Fault Detection and Diagnosis on HVAC Systems 

    E-print Network

    Muller, T.; Rehault, N.; Rist, T.

    2013-01-01

    A QUALITATIVE MODELING APPROACH FOR FAULT DETECTION AND DIAGNOSIS ON HVAC SYSTEMS Thorsten M?ller Nicolas R?hault Fraunhofer Institute for Solar Energy Systems - ISE 79110 Freiburg, Germany Tim Rist ABSTRACT This paper describes... be saved by the practical implementation of automated Fault Detection and Diagnosis (FDD) to support a condition-based maintenance (Katipamula and Brambley 2005). Although big research efforts have been carried out in the last two decades...

  13. Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN

    E-print Network

    Zhou, Y.; Zheng, J.; Liu, Z.; Yang, C.; Peng, P.

    2006-01-01

    ICEBO2006, Shenzhen, China Co ntrol Systems for Energy Efficiency and Comfort, Vol. V-5-3 Research on Fault Detection and Diagnosis of Scrolling Chiller with ANN1 Yuli ZHOU Jie ZHENG Zhiju LIU Chaojie YANG Peng PENG... Co ntrol Systems for Energy Efficiency and Comfort, Vol. V-5-3 [2] Meli Stylianou.Darius Nikanpour. Performance monitoring. Fault detection. and diagnosis of Reciprocating Chillers. ASHARE Transactions.102.1996( ). 615-627 [3] Tuo LIU, Jie...

  14. Fault Diagnosis of Steam Generator Using Signed Directed Graph and Artificial Neural Networks

    SciTech Connect

    Aly, Mohamed N. [Nuclear Eng. Department, Fac. of Eng., Alex. Univ., Alex. (Egypt); Hegazy, Hesham N. [Nuclear Power Plants Authority, Cairo (Egypt)

    2006-07-01

    Diagnosis is a very complex and important task for finding the root cause of faults in nuclear power plants. The objective of this paper is to investigate the feasibility of using the combination of signed directed graph (SDG) and artificial neural networks for fault diagnosis in nuclear power plants especially in U-Tube steam generator. Signed directed graph has been the most widely used form of qualitative based model methods for process fault diagnosis. It is constructed to represent the cause-effect relations among the dynamic process variables. Signed directed graph consists of nodes represent the process variables and branches. The branch represents the qualitative influence of a process variable on the related variable. The main problem in fault diagnosis using the signed directed graph is the unmeasured variables. Therefore, neural networks are used to estimate the values of unmeasured nodes. In this work, different four cases of faults in the steam generator ( SG) have been diagnosed, three of them are single fault and the fourth is multiple fault. The first three faults are by pass valve leakage (Vbp(+)), main feed water valve opening increase (Vfw(+)), main feed water valve opening decrease (Vfw (-)). The fourth fault is a multiple fault where by-pass valve leakage and main feed water valve opening decrease (Vbp(+) and Vfw (-)) in the same time. The used data are collected from a basic principle simulator of pressurized water reactor 925 Mwe. The signed directed graph of the steam generator is constructed to represent the cause-effect relations among SG variables. It consists of 26 nodes represent the SG variables, and 48 branches represent the cause effect relations among this variables. For each fault the values of measured nodes are coming from sensors and the values of unmeasured nodes are coming from the trained neural networks. These values of the nodes are compared by normal values to get the sign of the nodes. The cause-effect graph for each fault is constructed from the steam generator signed directed graph by removing the invalid (normal) nodes and inconsistent branches. Then in the cause-effect graph we search about the node which does not have an input branch. This node is the fault origin node. The result of this work demonstrated that this method can be used in nuclear power plant fault diagnosis. The advantages of this method are, it enables us to diagnose a multi fault, it is not restricted by pre-defined faults, and it is fast method. (authors)

  15. Proceedings of the 2008 International Conference on Electrical Machines Paper ID 1434 DFIG-Based Wind Turbine Fault Diagnosis

    E-print Network

    Boyer, Edmond

    -Based Wind Turbine Fault Diagnosis Using a Specific Discrete Wavelet Transform E. Al-Ahmar1,2 , M for electrical and mechanical fault diagnosis in a DFIG-based wind turbine. The investigated technique unambiguously diagnose faults under transient conditions. Index Terms--Wind turbine, Doubly-Fed Induction

  16. Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon Koutsoukos, and Gautam Biswas

    E-print Network

    Koutsoukos, Xenofon D.

    Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon.j.daigle,xenofon.koutsoukos,gautam.biswas@vanderbilt.edu Abstract-- Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems fault isolation in systems with complex continuous dynamics. This paper presents a novel discrete- event

  17. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network

    Microsoft Academic Search

    Jian-da Wu; Chiu-hong Liu

    2009-01-01

    In the present study, a fault diagnosis system is proposed for internal combustion engines using wavelet packet transform (WPT) and artificial neural network (ANN) techniques. In fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. The signal processing algorithm of the present system is gained from previous work used for speech recognition.

  18. Performance of diagnosis methods for IGBT open circuit faults in three phase voltage source inverters for AC variable speed drives

    Microsoft Academic Search

    K. Rothenhagen; F. W. Fuchs

    2005-01-01

    Variable speed drives have become industrial standard in many applications. Therefore fault diagnosis of voltage source inverters is becoming more and more important. One possible fault within the inverter is an open circuit transistor fault. An overview of the different strategies to detect this fault is given, including the algorithms used to localize the open transistor. Previous work showed significant

  19. Fault Diagnosis for Micro-Gas Turbine Engine Sensors via Wavelet Entropy

    PubMed Central

    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. PMID:22163734

  20. Multiple Models of Physical Systems - Modeling Intermittent Faults, Inaccuracy, and Tests in Diagnosis

    Microsoft Academic Search

    Peter Struss

    1994-01-01

    Diagnosis as a real human activity of problem solving, involves many actions and reasoning steps that seem to conflict with the use of exact, formal, and complete models of physical systems in model-based diagnosis. This paper focuses on demonstrating that some features occurring in practical diagnostic tasks, namely coping withintermittent faults, inaccurate models and observations, and testing, can be incorporated

  1. An investigation of MML methods for fault diagnosis in mobile robots

    Microsoft Academic Search

    Jennifer Carlson; Robin R. Murphy

    2004-01-01

    The purpose of this study is to evaluate the utility of a diagnosis technique, which uses minimum message length (MML) for autonomous mobile robot fault diagnosis. A simulator was developed for a behavior-based robotic system and results were gathered for over 24,000 simulations varying the level of test noise and the components with simulated failures. The results showed that the

  2. Parallel Scan-Like Testing and Fault Diagnosis Techniques for Digital Microfluidic Biochips*

    E-print Network

    Chakrabarty, Krishnendu

    Parallel Scan-Like Testing and Fault Diagnosis Techniques for Digital Microfluidic Biochips* Tao Xu, USA {tx, krish}@ee.duke.edu Abstract Dependability is an important attribute for microfluidic biochips diagnosis method based on test outcomes, for droplet-based microfluidic devices. The proposed method allows

  3. Turbine engine rotor blade fault diagnostics through casing pressure and vibration sensors

    NASA Astrophysics Data System (ADS)

    Cox, J.; Anusonti-Inthra, P.

    2014-11-01

    In this study, an exact solution is provided for a previously indeterminate equation used for rotor blade fault diagnostics. The method estimates rotor blade natural frequency through turbine engine casing pressure and vibration sensors. The equation requires accurate measurements of low-amplitude sideband signals in the frequency domain. With this in mind, statistical evaluation was also completed with the goal of determining the effect of sampling time and frequency on sideband resolution in the frequency domain.

  4. Fault-Diagnosis Using Neural Networks with Ellipsoidal Basis Funcions

    Microsoft Academic Search

    S. Jakubek; T. Strasser

    2002-01-01

    In this paper a fault detection scheme for applications in the automotive industry is presented. The detection scheme has to process up to several hundreds of different measurements at a time and check them for consistency. Our fault detection scheme works in three steps: First, principal component analysis of training data is used to determine nonsparse areas of the measurement

  5. On improving fault diagnosis for synchronous sequential circuits

    Microsoft Academic Search

    Irith Pomeranz; Sudhakar M. Reddy

    1994-01-01

    The multiple observation times approach was proposed as a test generation approach for fault detection, and was shown to allevi- ate deficiencies of conventional test generators. In this work, the multiple observation times approach is applied to fault location. It is shown that the use of multiple observation times has the potential of significantly enhancing the resolution of a given

  6. Soft Fault Diagnosis of Analog Circuit Using Transfer Function Coefficients

    Microsoft Academic Search

    A. Kavithamani; V. Manikandan; N. Devarajan

    2011-01-01

    A method to identify parametric faults occurring in analog circuits is proposed in this paper. This method uses transfer function coefficients to identify faults in analog circuits as these coefficients are sensitive to the parameters of the circuit. Using Monte Carlo simulation each parameter of the circuit is varied within its tolerance limit and the minimum and maximum values of

  7. Diagnosis of mechanical faults by spectral kurtosis energy

    Microsoft Academic Search

    Alberto Bellini; Marco Cocconcelli; Fabio Immovilli; Riccardo Rubini

    2008-01-01

    Generalized roughness is the most common damage occurring to roller bearing. It produces a frequency spreading of the characteristics fault frequencies, thus being difficult to detect with spectral or envelope analysis. A statistical analysis of typical bearing faults is here proposed in order to identify the spreading bandwidth related to a specific conditions, relying on current measurements only. Then a

  8. Development of Root Cause & Consequence Analyzer for Intelligent Fault Diagnosis

    Microsoft Academic Search

    H. A. Gabbar; R. Datu; H. Hayashi; D. Akinlade; A. Suzue; M. Kamel

    2006-01-01

    Fault propagation analysis is an important task that can be used to analyze and predict abnormal situations in chemical and petrochemical plants. It is difficult to identify all possible fault propagation scenarios due to plant complexity, operating condition changes, and computation limitations. In this paper, development of root cause and consequence analysis is proposed. Computational intelligence algorithm is proposed to

  9. Diverse neural net solutions to a fault diagnosis problem \\Lambda

    E-print Network

    Sharkey, Amanda

    detection of faulty combustion in an engine cylinder. Recognition of faulty combustion usually requires to recognise faults in simulated data from a diesel engine; specifically to classify combustion condition of combustion condition in a marine engine is crucial since undetected faults can rapidly become compoun­ ded

  10. Development of model-based fault diagnosis algorithms for MASCOTTE cryogenic test bench

    NASA Astrophysics Data System (ADS)

    Iannetti, A.; Marzat, J.; Piet-Lahanier, H.; Ordonneau, G.; Vingert, L.

    2014-12-01

    This article describes the on-going results of a fault diagnosis benchmark for a cryogenic rocket engine demonstrator. The benchmark consists in the use of classical model- based fault diagnosis methods to monitor the status of the cooling circuit of the MASCOTTE cryogenic bench. The algorithms developed are validated on real data from the last 2014 firing campaign (ATAC campaign). The objective of this demonstration is to find practical diagnosis alternatives to classical redline providing more flexible means of data exploitation in real time and for post processing.

  11. Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals

    Microsoft Academic Search

    Fabio Immovilli; Marco Cocconcelli; Alberto Bellini; Riccardo Rubini

    2009-01-01

    Generalized roughness is the most common damage occurring to rolling bearings. It produces a frequency spreading of the characteristic fault frequencies, thus making it difficult to detect with spectral or envelope analysis. A statistical analysis of typical bearing faults is proposed here in order to identify the spreading bandwidth related to specific conditions, relying on current or vibration measurements only.

  12. Detection of common motor bearing faults using frequency-domain vibration signals and a neural network based approach

    Microsoft Academic Search

    Bo Li; Gregory Goddu; Mo-Yuen Chow

    1998-01-01

    Bearings and their vibration play an important role in the performance of all motor systems. In many cases, the accuracy of the instruments and devices used to monitor and control the motor system is highly dependent on the dynamic performance of the motor bearings. In addition, many problems arising in motor operation are linked to bearing faults. Thus, fault detection

  13. The Application of Lifting Wavelet Transform in the Fault Diagnosis of Reciprocating Air Compressor

    Microsoft Academic Search

    Wang Lijun; Ma Lili; Huang Yongliang

    2010-01-01

    Lifting algorithm gives a simple and effective method for the construction of orthogonal wavelet, which is no longer the Fourier transformation completely, but the obtained wavelet has all the advantages of the first generation. This article makes use of lifting wavelet transformation to de-noise the vibration signals of the air compressor sensor, and to diagnose effectively the fault of reciprocating

  14. ROLLING ELEMENT BEARING FAULT DIAGNOSIS IN ROTATING MACHINES OF OIL EXTRACTION RIGS

    Microsoft Academic Search

    E. Mendel; T. W. Rauber; F. M. Varej; R. J. Batista

    2009-01-01

    This paper presents vibration analysis techniques for fault detection in rotating machines. Rolling element bearing defects inside a motor pump are the subject of study. Signal processing techniques, like frequency filters, Hilbert tra ns- form, and spectral analysis are used to extract features use d later as a base to classify the condition of machines. Also, pattern recognition techniques are

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

    Microsoft Academic Search

    Li Jiang; Tielin Shi; Jianping Xuan

    2012-01-01

    Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem

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

    PubMed Central

    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

  17. Non-cooperative Diagnosis of Submarine Cable Faults

    NASA Astrophysics Data System (ADS)

    Chan, Edmond W. W.; Luo, Xiapu; Fok, Waiting W. T.; Li, Weichao; Chang, Rocky K. C.

    Submarine cable faults are not uncommon events in the Internet today. However, their impacts on end-to-end path quality have received almost no attention. In this paper, we report path-quality measurement results for a recent SEA-ME-WE 4 cable fault in 2010. Our measurement methodology captures the path-quality degradation due to the cable fault, in terms of delay, asymmetric packet losses, and correlation between loss and delay. We further leverage traceroute data to infer the root causes of the performance degradation.

  18. Fault Diagnosis of Timed Systems In this Chapter, we review the main results pertaining to the problem of fault diagnosis

    E-print Network

    Paris-Sud XI, Université de

    of S with the faults, but the faults cannot be observed at runtime (sensors can only observe a subset of the events to Chapter 1 and Chapter 2 in this book. In Section 4.3, we define the notion of diagnoser which

  19. Observer-Based Fault Diagnosis Incorporating Online Control Allocation for Spacecraft Attitude Stabilization under Actuator Failures

    NASA Astrophysics Data System (ADS)

    Hu, Qinglei; Li, Bo; Friswell, Michael I.

    2014-12-01

    This paper proposes a novel observer-based fault diagnosis method, incorporating an online control allocation scheme, for an orbiting spacecraft in the presence of actuator failures/faults, unexpected disturbances and input saturation. The proposed scheme solves a difficult problem in spacecraft fault tolerant control design by compensating for the loss in effectiveness and time-varying faults using redundant actuators, so that the overall system is stable, despite external disturbances and input saturation. This is accomplished by developing an observer-based fault diagnosis mechanism to reconstruct or estimate the actuator faults/failures. An online control allocation scheme is then used to redistribute the control signals to the healthy actuators in the case of faults/failures, without reconfiguring the controller, so that the control signal distribution is based on the reconstructed actuator effectiveness level. Simulation results using a rigid spacecraft model show good performance in the presence of faults, including total actuator failure scenarios, external disturbances and actuator input saturation, which validates the effectiveness and feasibility of the proposed scheme.

  20. An expert system for fault detection and diagnosis 

    E-print Network

    Spasojevic, Predrag

    1992-01-01

    . Characteristic PS Parameter Relations During Faults G. Knowledge Acquisition: Interviews H. Experts' Approach I. Event Analysis J. Case Study K. Protection System Operation Analysis L. Conclusion 7 8 10 13 13 16 16 18 24 27 31 32 34 36 41 45.... 42 13 14 DEDIAS Block Diagram . MATLAB to CLIPS Interface 47 58 15 Decision System 60 16 17 18 Event Analysis Rules . Protection System Operation Analysis Rules . One Line Diagram of the Reduced TVA System 61 62 70 19 AG Fault...

  1. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2012-01-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  2. Fault diagnosis based on signed directed graph and support vector machine

    NASA Astrophysics Data System (ADS)

    Han, Xiaoming; Lv, Qing; Xie, Gang; Zheng, Jianxia

    2011-12-01

    Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.

  3. The reliability of general vague fault-tree analysis on weapon systems fault diagnosis

    Microsoft Academic Search

    J.-R. Chang; K.-H. Chang; S.-H. Liao; C.-H. Cheng

    2006-01-01

    An algorithm of vague fault-tree analysis is proposed in this paper to calculate fault interval of system components from\\u000a integrating expert's knowledge and experience in terms of providing the possibility of failure of bottom events. We also modify\\u000a Tanaka et al's definition and extend the new usage on vague fault-tree analysis in terms of finding most important basic system\\u000a component

  4. Actuator fault diagnosis and fault-tolerant control: Application to the quadruple-tank process

    NASA Astrophysics Data System (ADS)

    Buciakowski, Mariusz; de Rozprza-Faygel, Micha?; Ocha?ek, Joanna; Witczak, Marcin

    2014-12-01

    The paper focuses on an important problem related to the modern control systems, which is the robust fault-tolerant control. In particular, the problem is oriented towards a practical application to quadruple-tank process. The proposed approach starts with a general description of the system and fault-tolerant strategy, which is composed of a suitably integrated fault estimator and robust controller. The subsequent part of the paper is concerned with the design of robust controller as well as the proposed fault-tolerant control scheme. To confirm the effectiveness of the proposed approach, the final part of the paper presents experimental results for considered the quadruple-tank process.

  5. Self-Checking Detection and Diagnosis of Transient, Delay, and Crosstalk Faults Affecting Bus Lines

    Microsoft Academic Search

    Cecilia Metra; Michele Favalli

    2000-01-01

    We present a self-checking detection and diagnosis scheme for transient, delay, and crosstalk faults affecting bus lines of synchronous systems. Faults that are likely to result in the connected logic sampling incorrect bus data are on-line detected. The position of the affected line(s) within the considered bus is identified and properly encoded. The proposed scheme is self-checking with respect to

  6. NonAdaptive Fault Diagnosis for All-Optical Networks via Combinatorial Group Testing on Graphs

    Microsoft Academic Search

    Nicholas J. A. Harvey; Mihai Patrascu; Yonggang Wen; Sergey Yekhanin; Vincent W. S. Chan

    2007-01-01

    We consider the problem of detecting network faults. Our focus is on detection schemes that send probes both proactively and non-adaptively. Such schemes are particularly relevant to all-optical networks, due to these networks' operational characteristics and strict performance requirements. This fault diagnosis problem motivates a new technical framework that we introduce: group testing with graph-based constraints. Using this framework, we

  7. Fuzzy Decision Trees as Intelligent Decision Support Systems for Fault Diagnosis

    Microsoft Academic Search

    Enrico Zio; Piero Baraldi; Irina Popescu

    In the present work, an intelligent decision support system is proposed to assist the operators in fault diagnosis tasks.\\u000a The underlying approach relies on a systematic procedure to manipulate measured data of the monitored variables for constructing\\u000a transparent fuzzy if-then rules associating different patterns of evolution to different faults and anomalies. The resulting\\u000a fuzzy classification model can then be represented

  8. Fault diagnosis method for power transformer based on ant colony SVM classifier

    Microsoft Academic Search

    Niu Wu; Xu Liangfa; Hu Sanguo

    2010-01-01

    Failure of power transformer is very complex, so that it is difficult to use the mathematical model to describe their faults. In this study, an intelligent diagnostic method based on ant colony-support vector machine (AC-SVM) approach is presented for fault diagnosis of power transformer. The AC-SVM selects kernel function parameter and soft margin constant C penalty parameter of support vector

  9. Implementation of a research prototype onboard fault monitoring and diagnosis system

    NASA Technical Reports Server (NTRS)

    Palmer, Michael T.; Abbott, Kathy H.; Schutte, Paul C.; Ricks, Wendell R.

    1987-01-01

    Due to the dynamic and complex nature of in-flight fault monitoring and diagnosis, a research effort was undertaken at NASA Langley Research Center to investigate the application of artificial intelligence techniques for improved situational awareness. Under this research effort, concepts were developed and a software architecture was designed to address the complexities of onboard monitoring and diagnosis. This paper describes the implementation of these concepts in a computer program called FaultFinder. The implementation of the monitoring, diagnosis, and interface functions as separate modules is discussed, as well as the blackboard designed for the communication of these modules. Some related issues concerning the future installation of FaultFinder in an aircraft are also discussed.

  10. Fault diagnosis and accommodation of a three-tank system based on analytical redundancy.

    PubMed

    Theilliol, Didier; Noura, Hassan; Ponsart, Jean-Christophe

    2002-07-01

    This paper investigates the application of a fault diagnosis and accommodation method to a real system composed of three tanks. The performance of a closed-loop system can be altered by the occurrence of faults which can, in some circumstances, cause serious damage on the system. The research goal is to prevent the system deterioration by developing a controller that has some capabilities to compensate for faults, that is, the fault accommodation or fault-tolerant control. In this paper, a two-step scheme composed of a fault detection, isolation and estimation module, and a control compensation module is presented. The main contribution is to develop a unique structured residual generator able to isolate and estimate both sensor and actuator faults. This estimation is of paramount importance to compensate for these faults and to preserve the system performances. The application of this method to the three-tank system gives encouraging results which are presented and commented on in case of various kinds of faults. PMID:12160349

  11. Neural networks and fault probability evaluation for diagnosis issues.

    PubMed

    Kourd, Yahia; Lefebvre, Dimitri; Guersi, Noureddine

    2014-01-01

    This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method. PMID:25132845

  12. Neural Networks and Fault Probability Evaluation for Diagnosis Issues

    PubMed Central

    Lefebvre, Dimitri; Guersi, Noureddine

    2014-01-01

    This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method. PMID:25132845

  13. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Zhao, H. L.; Gu, F.; Ball, A. D.

    2012-05-01

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods.

  14. Reasoning about fault diagnosis for the space station common module thermal control system

    NASA Technical Reports Server (NTRS)

    Vachtsevanos, G.; Hexmoor, H.; Purves, B.

    1988-01-01

    The proposed common module thermal control system for the Space Station is designed to integrate thermal distribution and thermal control functions in order to transport heat and provide environmental temperature control through the common module. When the thermal system is operating in an off-normal state, due to component faults, an intelligent controller is called upon to diagnose the fault type, identify the fault location and determine the appropriate control action required to isolate the faulty component. A methodology is introduced for fault diagnosis based upon a combination of signal redundancy techniques and fuzzy logic. An expert system utilizes parity space representation and analytic redundancy to derive fault symptoms, the aggregate of which is assessed by a multivalued rule based system. A subscale laboratory model of the thermal control system designed is used as the testbed for the study.

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

    NASA Astrophysics Data System (ADS)

    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.

  16. Fault detection and diagnosis using neural network approaches

    NASA Technical Reports Server (NTRS)

    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.

  17. Case study of Chilled Water Loop Low Delta-T Fault Diagnosis 

    E-print Network

    Wang, L.; Meline, K.; Watt, J.

    2014-01-01

    Case study of Chilled Water Loop Low ?T Fault Diagnosis Lei Wang1, Ph.D., P.E. Ken Meline2, P.E. James Watt1, P.E. Bahman Yazdani1 P.E., David E. Claridge1, Ph.D., P.E 1Energy Systems Laboratory, Texas A&M Engineering Experiment Station...Case Study of Chilled Water Loop Low DT Fault Diagnosis Presented by Lei Wang Ph.D. P.E. Energy Systems Laboratory, Texas A&M University System Sep. 15, 2014 Energy Systems Laboratory p. 1 ESL-IC-14-09-12a Proceedings of the 14th International...

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

    NASA Technical Reports Server (NTRS)

    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.

  19. Robust fault diagnosis for a satellite large angle attitude system using an iterative neuron PID (INPID) observer

    Microsoft Academic Search

    Qing Wu; Mehrdad Saif

    2006-01-01

    A fault detection and diagnosis (FDD) scheme using an iterative neuron PID (INPID) observer is explored in this paper. The observer input, which is used to estimate state faults, is computed by utilizing the proportional, integral, and derivative information of the fault estimation error. Two classes of robust adaptive algorithms are adopted to update the parameters of the observer input.

  20. Induction Motor Fault Diagnosis Using a Hilbert-Park Lissajou's Curve Analysis and Neural Network-Based Decision

    E-print Network

    Paris-Sud XI, Université de

    Induction Motor Fault Diagnosis Using a Hilbert-Park Lissajou's Curve Analysis and Neural Network propose an original fault signature based on the Hilbert-Park Lissajou's curve analysis. The performances used. The proposed fault signature does not require a long temporal recording, and their processing

  1. Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions

    Microsoft Academic Search

    Peng Chen; Toshio Toyota; Zhengja He

    2001-01-01

    Dimensional or nondimensional symptom parameters are usually used for condition monitoring of plant machinery. However, it is difficult to extract the most important symptom parameters and the functions of those parameters by which machinery faults can be sensitively detected and the fault types can be precisely distinguished. In order to overcome this difficulty and to ensure highly accurate fault diagnosis,

  2. Planning as Heuristic Search for Incremental Fault Diagnosis and Repair

    Microsoft Academic Search

    Håkan Warnquist; Jonas Kvarnström; Patrick Doherty

    2009-01-01

    In this paper we study the problem of incremental fault diag- nosis and repair of mechatronic systems where the task is to choose actions such that the expected cost of repair is mini- mal. This is done by interleaving acting with the generation of partial conditional plans used to decide the next action. A diagnostic model based on Bayesian Networks

  3. Fault diagnosis of pneumatic systems with artificial neural network algorithms

    Microsoft Academic Search

    M. Demetgul; Ibrahim N. Tansel; S. Taskin

    2009-01-01

    Pneumatic systems repeat the identical programmed sequence during their operation. The data was collected when the pneumatic system worked perfectly and had some faults including empty magazine, zero vacuum, inappropriate material, no pressure, closed manual pressure valve, missing drilling stroke, poorly located material, not vacuuming the material and low air pressure. The signals of eight sensors were collected during the

  4. Predicting Future States With Dimensional Markov Chains for Fault Diagnosis

    Microsoft Academic Search

    Ian Morgan; Honghai Liu

    2009-01-01

    This paper introduces a novel method of predicting future concentrations of elements in lubrication oil, for the aim of identifying possible anomalies in continued operation aboard a large marine vessel. The research carried out is supported by a discussion of previous work in the field of fault detection in tribological mechanisms, although with a focus upon two stroke marine diesel

  5. Neural-network-based motor rolling bearing fault diagnosis

    Microsoft Academic Search

    Bo Li; Mo-Yuen Chow; Yodyium Tipsuwan; James C. Hung

    2000-01-01

    Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the US into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the

  6. A novel fuzzy logic approach to transformer fault diagnosis

    Microsoft Academic Search

    Syed Mofizul Islam; Tony Wu; Gerard Ledwich

    2000-01-01

    Dissolved gas in oil analysis is an well established in-service technique for incipient fault detection in oil-insulated power transformers. A great deal of experience and data in dissolved gas in oil analysis (DGA) is now available within the utilities. Actually, diagnostic interpretations were solely done by human experts using past knowledge and standard techniques such as the ratio method. In

  7. Vibration diagnosis and remediation design for an x-ray optics stitching interferometer system

    NASA Astrophysics Data System (ADS)

    Preissner, Curt A.; Assoufid, Lahsen; Shu, Deming

    2004-10-01

    The Advanced Photon Source (APS) x-ray optics Metrology Laboratory currently operates a small-aperture Wyko laser interferometer in a stitching configuration. While the stitching configuration allows for easier surface characterization of long x-ray substrates and mirrors, the addition of mechanical components for optic element translation can compromise the ultimate measurement performance of the interferometer. A program of experimental vibration measurements, quantifying the laboratory vibration environment and identifying interferometer support-system behavior, has been conducted. Insight gained from the ambient vibration assessment and modal analysis has guided the development of a remediation technique. Discussion of the problem diagnosis and possible solutions are presented in this paper.

  8. A design approach for self-diagnosis of fault-tolerant clock synchronization

    Microsoft Academic Search

    M. Lu; D. Zhang; T. Murata

    1989-01-01

    A general design approach for self-diagnosis of faulty clocking modules in a fault-tolerant clock synchronization (FTCS) system is presented. The approach is based on a statistical testing method. The major advantages are better self-stability control and lower overhead. The design methodology includes a self-diagnosis algorithm to transform a partially self-stabilizing clocking system into a self-stabilizing one. Compound to partially self-stabilizing

  9. The diagnosis of disorders caused by hand-transmitted vibration: Southampton Workshop 2000

    Microsoft Academic Search

    Michael J. Griffin; Massimo Bovenzi

    2002-01-01

    .   \\u000a Objectives: To identify the current state of knowledge, current uncertainties and future needs related to the diagnosis of disorders\\u000a associated with the use of vibratory hand-held tools. Method: An international workshop was convened with invited experts, medical doctors, scientists and engineers familiar with hand-transmitted\\u000a vibration and the diagnosis of vascular, neurological and musculoskeletal disorders. This paper records the general

  10. Robust fault diagnosis in uncertain linear parameter-varying systems

    Microsoft Academic Search

    David Henry; Ali Zolghadri

    2004-01-01

    The purpose of this paper is to develop a new model-based approach for solving FDI (fault detection and isolation) problems in linear parameter-varying plants. The core element of the approach is that the model describing the monitored plant as well as the internal and external perturbations, results in an uncertain linear parameter time-varying model. To solve the problem, a new

  11. A novel approach to fault diagnosis in multicircuit transmission lines using fuzzy ARTmap neural networks.

    PubMed

    Aggarwal, R K; Xuan, Q Y; Johns, A T; Li, F; Bennett, A

    1999-01-01

    The work described in this paper addresses the problems of fault diagnosis in complex multicircuit transmission systems, in particular those arising due to mutual coupling between the two parallel circuits under different fault conditions; the problems are compounded by the fact that this mutual coupling is highly variable in nature. In this respect, artificial intelligence (AI) technique provides the ability to classify the faulted phase/phases by identifying different patterns of the associated voltages and currents. In this paper, a Fuzzy ARTmap (Adaptive Resonance Theory) neural network is employed and is found to be well-suited for solving the complex fault classification problem under various system and fault conditions. Emphasis is placed on introducing the background of AI techniques as applied to the specific problem, followed by a description of the methodology adopted for training the Fuzzy ARTmap neural network, which is proving to be a very useful and powerful tool for power system engineers. Furthermore, this classification technique is compared with a Neural Network (NN) technique based on the error backpropagation (EBP) training algorithm, and it is shown that the former technique is better suited for solving the fault diagnosis problem in complex multicircuit transmission systems. PMID:18252622

  12. Spectrum auto-correlation analysis and its application to fault diagnosis of rolling element bearings

    NASA Astrophysics Data System (ADS)

    Ming, A. B.; Qin, Z. Y.; Zhang, W.; Chu, F. L.

    2013-12-01

    Bearing failure is one of the most common reasons of machine breakdowns and accidents. Therefore, the fault diagnosis of rolling element bearings is of great significance to the safe and efficient operation of machines owing to its fault indication and accident prevention capability in engineering applications. Based on the orthogonal projection theory, a novel method is proposed to extract the fault characteristic frequency for the incipient fault diagnosis of rolling element bearings in this paper. With the capability of exposing the oscillation frequency of the signal energy, the proposed method is a generalized form of the squared envelope analysis and named as spectral auto-correlation analysis (SACA). Meanwhile, the SACA is a simplified form of the cyclostationary analysis as well and can be iteratively carried out in applications. Simulations and experiments are used to evaluate the efficiency of the proposed method. Comparing the results of SACA, the traditional envelope analysis and the squared envelope analysis, it is found that the result of SACA is more legible due to the more prominent harmonic amplitudes of the fault characteristic frequency and that the SACA with the proper iteration will further enhance the fault features.

  13. An integrated approach to performance monitoring and fault diagnosis of nuclear power systems

    NASA Astrophysics Data System (ADS)

    Zhao, Ke

    2005-07-01

    An integrated approach to performance monitoring and fault diagnosis was developed in this dissertation for nuclear power plants using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault diagnosis. In the applications to nuclear power plants, on the one hand, routine operation data may not be able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components may experience degradation. On the other hand, physical models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of physical models in modeling a wide range of anticipated operation conditions and the strength of statistical data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using physical models and model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed method could eliminate false alarms due to model uncertainty and deal with operating condition changes of nuclear power plants. The developed algorithms were demonstrated using the International Reactor Innovative and Secure (IRIS) Helical Coil Steam Generator (HCSG) systems. A thermal hydraulic model was developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operating conditions. This dissertation will bridge the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power plants. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors.

  14. Condition monitoring and fault diagnosis of electrical motors-a review

    Microsoft Academic Search

    Subhasis Nandi; Hamid A. Toliyat; Xiaodong Li

    2005-01-01

    Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as

  15. Twofold Fuzzy Sets in Single and Multiple Fault Diagnosis, Using Information About Normal Values

    Microsoft Academic Search

    Henri Prade; Olivier De Mouzon; Didier Dubois

    2001-01-01

    This paper proposes a general approach to diagnosis based on fuzzy pattern matching, making use of consistency and inclusion-based indices in the setting of possibility theory. The approach was first developed for binary attributes and single faults. It was then generalized to any kind of attributes (including multidimensional ones). The paper presents a refined representation (where a distinction is made

  16. A Tabu-Search Based Neuro-Fuzzy Inference System for Fault Diagnosis

    E-print Network

    Rizvi, Syed Z.

    A Tabu-Search Based Neuro-Fuzzy Inference System for Fault Diagnosis Haris M. Khalid S.Z. Rizvi@kfupm.edu.sa). Abstract: This paper presents a novel hybrid Tabu Search (TS) Subtractive Clustering (SC) based Neuro. Keywords: Tabu Search, Subtractive Clustering, Neuro-Fuzzy, Soft Computing, Artificial Neural Network

  17. Wind Turbines Condition Monitoring and Fault Diagnosis Using Generator Current Amplitude

    E-print Network

    Paris-Sud XI, Université de

    Wind Turbines Condition Monitoring and Fault Diagnosis Using Generator Current Amplitude in the research of renewable energy sources. In order to make wind turbines as competitive as the classical detection in a Doubly-Fed Induction Generator (DFIG) based wind turbine for stationary and nonstationary

  18. INCREASING EFFECTIVENESS OF MODEL-BASED FAULT DIAGNOSIS: A DYNAMIC BAYESIAN NETWORK DESIGN FOR DECISION MAKING

    E-print Network

    Paris-Sud XI, Université de

    FOR DECISION MAKING Philippe WEBER°, Didier THEILLIOL°, Christophe AUBRUN° and Alexandre EVSUKOFF* °Centre de: This papers aims to design a new approach in order to increase the performance of the decision making in model-based fault diagnosis. The decision making, formalised as a bayesian network, is established with a priori

  19. Magnetostatic Field Analysis and Diagnosis of Mixed Eccentricity Fault in Switched Reluctance Motor

    Microsoft Academic Search

    Hossein Torkaman; Ebrahim Afjei

    2011-01-01

    In this article, a novel view of airgap magnetic field analysis of a switched reluctance motor under mixed eccentricity to provide a precise fault diagnosis based on the three-dimensional finite element method is presented. The analytical nature of this method makes it possible to simulate a reliable and precise model by considering the end effects and axial fringing effects. The

  20. Hierarchical Modelling of Automotive Sensor Front-Ends For Structural Diagnosis of Aging Faults

    E-print Network

    Wieringa, Roel

    Hierarchical Modelling of Automotive Sensor Front-Ends For Structural Diagnosis of Aging Faults h.g.kerkhoff@utwente.nl Abstract: The semiconductor industry for automotive applications is growing, dependability, reliability, aging models, hierarchical interfacing, analogue automotive front-ends. I

  1. An Annotated Selective Bibliography on Human Performance in Fault Diagnosis Tasks. Technical Report 435. Final Report.

    ERIC Educational Resources Information Center

    Johnson, William B.; And Others

    This annotated bibliography developed in connection with an ongoing investigation of the use of computer simulations for fault diagnosis training cites 61 published works taken predominantly from the disciplines of engineering, psychology, and education. A review of the existing literature included computer searches of the past ten years of…

  2. Theory of reliable systems. [reliability analysis and on-line fault diagnosis

    NASA Technical Reports Server (NTRS)

    Meyer, J. F.

    1974-01-01

    Research is reported in the program to refine the current notion of system reliability by identifying and investigating attributes of a system which are important to reliability considerations, and to develop techniques which facilitate analysis of system reliability. Reliability analysis, and on-line fault diagnosis are discussed.

  3. A Router for Improved Fault Isolation, Scalability and Diagnosis in CAN

    E-print Network

    1 A Router for Improved Fault Isolation, Scalability and Diagnosis in CAN R. Obermaisser, R. Kammerer Vienna University of Technology, Austria Abstract--Controller Area Network (CAN) provides an inexpensive and robust network technology in many appli- cation domains. However, the use of CAN

  4. An innovations approach to fault diagnosis in linear time-varying descriptor systems*

    E-print Network

    Paris-Sud XI, Université de

    that the considered fault diagnosis problem in linear time varying descriptor systems is equivalent to a classical linear regression problem formulated by appropriately filtering the input-output data. Following. A numerical example is presented to illustrate the proposed method. I. INTRODUCTION Many modern engineering

  5. A distributed approach for fault detection and diagnosis based on Time Petri Nets

    Microsoft Academic Search

    George Jiroveanu; René K. Boel

    2006-01-01

    This paper proposes an algorithm for the model based design of a distributed protocol for fault detection and diagnosis for very large systems. The overall process is modeled as different Time Petri Net (TPN) models (each one modeling a local process) that interact with each other via guarded transitions that becomes enabled only when certain conditions (expressed as predicates over

  6. On the Unknown Input Observer Design : a Decoupling Class Approach with Application to Sensor Fault Diagnosis

    E-print Network

    Paris-Sud XI, Université de

    On the Unknown Input Observer Design : a Decoupling Class Approach with Application to Sensor Fault diagnosis for observer- based residual generators for linear discrete-time systems sub- ject to unknown input. The proposed approach is a new method allowing to characterize a class of unknown inputs from

  7. Synthesis Of Optimal-Cost Dynamic Observers for Fault Diagnosis of Discrete-Event Systems

    E-print Network

    Boyer, Edmond

    Cassez Stavros Tripakis Karine Altisen§ Abstract Fault diagnosis consists in synthesizing a diagnoser. In this paper, we consider dynamic observers, where the observer can switch sensors on or off, thus dynamically). Observing an event usually requires some detection mechanism, i.e. , a sensor of some sort. Which sensors

  8. Fault diagnosis of nonlinear systems using higher order sliding mode technique

    Microsoft Academic Search

    M. Iqbal; A. I. Bhatti; S. Iqbal; Q. Khan; I. H. Kazmi

    2009-01-01

    This paper presents a synthesis of fault diagnosis method for nonlinear systems through the parameter estimation using higher order sliding modes. Initially the uncertain parameters of nonlinear system using robust exact differentiator are estimated. Then residual signal is reconstructed using the estimated parameters, inputs, outputs and their estimated derivatives. The novelty of the method is the determination of unknown, uncertain

  9. Fault diagnosis of inter-turn short-circuit in rotor windings based on artificial intelligence

    Microsoft Academic Search

    Zhao Juan

    2010-01-01

    The inter turn short-circuit in rotor windings take the induced electromotive force, which is detected by detecting coil, as a study object. And a method of fault diagnosis based on Wavelet analysis and neural network is presented. The induced electromotive force is analyzed by wavelet packet, which can decompose and construct the energy eigenvectors. Then set up the neural network

  10. Modeling and Fault Diagnosis of a Polymer Electrolyte Fuel Cell Using Electrical Equivalent Analysis

    Microsoft Academic Search

    Andres Hernandez; Daniel Hissel; Rachid Outbib

    2010-01-01

    Fuel cell systems are complex systems and a high degree of competence is needed in different areas of knowledge such as thermodynamics, fluid dynamics, electrochemistry, and others, for their comprehension. This paper is a contribution to global modeling and fault diagnosis of these systems. More precisely, the goal of this paper is twofold. First, an electrical equivalent model, which could

  11. Use a domain ontology to develop knowledge intensive CBR systems for fault diagnosis

    Microsoft Academic Search

    N. Dendani; Med. Khadir; S. Guessoum; Badji Mokhtar

    2012-01-01

    Our work aims at realizing a system which consists in gathering the knowledge and the know-how in the field of fault diagnosis for steam turbines, by the construction of domain ontology. In order to better exploit the ontology and reason using its classes, sub-classes and instances a Case Based Reasoning (CBR) paradigm is chosen, as it offers an ideal solution

  12. An expert system for fault diagnosis in internal combustion engines using probability neural network

    Microsoft Academic Search

    Jian-da Wu; Peng-hsin Chiang; Yo-wei Chang; Yao-jung Shiao

    2008-01-01

    An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a

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

    Microsoft Academic Search

    Guo-Feng Wang; Tai-Yong Wang

    2005-01-01

    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

  14. New clustering algorithm-based fault diagnosis using compensation distance evaluation technique

    Microsoft Academic Search

    Yaguo Lei; Zhengjia He; Yanyang Zi; Xuefeng Chen

    2008-01-01

    This paper presents a fault diagnosis method of rotating machinery based on a new clustering algorithm using a compensation distance evaluation technique (CDET). A two-stage feature selection and weighting technique is adopted in this algorithm. Feature weights are computed via CDET according to the sensitivity of features and assigned to the corresponding features to indicate their different importance in clustering.

  15. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram

    Microsoft Academic Search

    Yongxiang Zhang; R. B. Randall

    2009-01-01

    The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. However, much practical diagnostic equipment for carrying out the analysis gives little flexibility to change the analysis parameters for

  16. An approach for bearing fault diagnosis based on PCA and multiple classifier fusion

    Microsoft Academic Search

    Min Xia; Fanrang Kong; Fei Hu

    2011-01-01

    The purpose of this paper is to propose a new system, with both high efficiency and accuracy for fault diagnosis of rolling bearing. After pretreatment and choosing sensitive features of different working conditions of bearing from both time and frequency domain, principal component analysis(PCA) is conducted to compress the data dimension and eliminate the correlation among different statistical features. The

  17. Application of Adaptive Estimation Techniques on Battery Fault Diagnosis Amardeep Singh1

    E-print Network

    Zhou, Yaoqi

    Application of Adaptive Estimation Techniques on Battery Fault Diagnosis Amardeep Singh1 , Afshin-ion Batteries are one of the most widely used renewable energy sources today. They find applications in everyday to observe continuously the state of the Li-ion battery and detect Over Charge (OC) and Over Discharge (OD

  18. Testing and Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures

    E-print Network

    Harris, Ian G.

    Testing and Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures Ian G. Harris-mail: harris@ecs.umass.edu, tessier@ecs.umass.edu Abstract As IC densities are increasing, cluster-based FPGA architectures are becoming the architecture of choice for major FPGA manufacturers. A cluster-based architecture

  19. The fault diagnosis of large-scale wind turbine based on expert system

    NASA Astrophysics Data System (ADS)

    Chen, Changzheng; Li, Yun

    2011-10-01

    The wind turbine is the critical equipment for wind power, due to the poor working environment and the long running, the wind turbine components will have a variety of failures. Planned maintenance which has long been used is unable to understand the operational status of equipment comprehensively and timely in a way, especially for large wind machine, the repair work took too long time and cause serious damage. Therefore, fault diagnosis and predictive maintenance becomes more imminent. In this paper, the fault symptoms and corresponding reason of the large-scale wind turbine parts are analyzed and summarized ,such as gear box, generator, yaw system, and so on . And on this basis, the large-scale wind turbine fault diagnosis expert system was constructed by using expert system tool CLIPS and Visual C + +.

  20. Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert-Huang transform

    Microsoft Academic Search

    Seung-Mock Lee; Yeon-Sun Choi

    2008-01-01

    Partial rub and looseness are common faults in rotating machinery because of the clearance between the rotor and the stator.\\u000a These problems cause malfunctions in rotating machinery and create strange vibrations coming from impact and friction. However,\\u000a non-linear and non-stationary signals due to impact and friction are difficult to identify. Therefore, exact time and frequency\\u000a information is needed for identifying

  1. Application of statistics filter method and clustering analysis in fault diagnosis of roller bearings

    Microsoft Academic Search

    L Y Song; H Q Wang; J J Gao; J F Yang; W B Liu; P Chen

    2012-01-01

    Condition diagnosis of roller bearings depends largely on the feature analysis of vibration signals. Spectrum statistics filter (SSF) method could adaptively reduce the noise. This method is based on hypothesis testing in the frequency domain to eliminate the identical component between the reference signal and the primary signal. This paper presents a statistical parameter namely similarity factor to evaluate the

  2. Diagnosis of internal combustion engine through vibration and acoustic pressure non-intrusive measurements

    Microsoft Academic Search

    L. Barelli; G. Bidini; C. Buratti; R. Mariani

    2009-01-01

    The present study proposes a diagnosis methodology for internal combustion engines (I.C.E.) working conditions, by means of non-invasive measurements on the cylinder head, such as acoustic and vibration, related to the internal indicated mean effective pressure. The experimental campaign was carried out on the internal combustion engine of the cogeneration plant at the Faculty of Engineering – University of Perugia

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

    NASA Astrophysics Data System (ADS)

    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.

  4. An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Xue, Xiaoming; Zhou, Jianzhong; Xu, Yanhe; Zhu, Wenlong; Li, Chaoshun

    2015-10-01

    Ensemble empirical mode decomposition (EEMD) represents a significant improvement over the original empirical mode decomposition (EMD) method for eliminating the mode mixing problem. However, the added white noises generate some tough problems including the high computational cost, the determination of the two critical parameters (the amplitude of the added white noise and the number of ensemble trials), and the contamination of the residue noise in the signal reconstruction. To solve these problems, an adaptively fast EEMD (AFEEMD) method combined with complementary EEMD (CEEMD) is proposed in this paper. In the proposed method, the two critical parameters are respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials. Instead, the upper frequency limit of the added white noise is the key parameter which needs to be prescribed beforehand. Unlike the original EEMD method, only two high-frequency white noises are added to the signal to be investigated with anti-phase in AFEEMD. Furthermore, an index termed relative root-mean-square error is employed for the adaptive selection of the proper upper frequency limit of the added white noises. Simulation test and vibration signals based fault diagnosis of rolling element bearing under different fault types are utilized to demonstrate the feasibility and effectiveness of the proposed method. The analysis results indicate that the AFEEMD method represents a sound improvement over the original EEMD method, and has strong practicability.

  5. Enhancing Transition Fault Model for Delay Defect Diagnosis

    Microsoft Academic Search

    Wu-Tung Cheng; Brady Benware; Ruifeng Guo; Kun-Han Tsai; T. Kobayashi; K. Maruo; M. Nakao; Y. Fukui; H. Otake

    2008-01-01

    With nanometer processes, at-speed testing is required to filter out failing chips with delay defects to ensure high product quality. Locating delay defects is important not only for improving yield but also providing important information to enhance at-speed test methods to meet quality goals. In this paper, a method that leverages successful static defect diagnosis method to diagnose delay defects

  6. QUALITATIVE MODELING AND FAULT DIAGNOSIS OF DYNAMIC PROCESSES BY MIDAS†

    Microsoft Academic Search

    F. E. FINCH; M. A. KRAMER

    1990-01-01

    The Model Integrated Diagnostic Analysis System (MIDAS) is a program for diagnosing abnormal transient conditions in chemical, refinery, and utility systems. MIDAS employs causal reasoning using an event model derived from piping and instrumentation diagrams, and from quantitative process models. Root causes typically considered are equipment degradation and failure, incorrect manual actions, and external disturbances. By prompt and accurate diagnosis

  7. Effectiveness of MED for Fault Diagnosis in Roller Bearings

    NASA Astrophysics Data System (ADS)

    Pennacchi, P.; Ricci, Roberto; Chatterton, S.; Borghesani, P.

    Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

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

    PubMed

    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. PMID:21954208

  9. CNC-implemented fault diagnosis and web-based remote services

    Microsoft Academic Search

    Dong-Hoon Kim; Sun-Ho Kim; Kwang-Sik Koh

    2005-01-01

    Recently, the conventional controller of machine-tool has been increasingly replaced by the PC-based open architecture controller,\\u000a which is independent of the CNC vendor and on which it is possible to implement user-defined application programs. This paper\\u000a proposes CNC-implemented fault diagnosis and web-based remote services for machine-tool with open architecture CNC. The faults\\u000a of CNC machine-tool are defined as the operational

  10. Multi-objective Intelligent Optimization Model on Dynamic Error Measurement and Fault Diagnosis for Roll Grinder NC

    Microsoft Academic Search

    Ding Xiaoyan; Liu Lilan; Hua Zhengxiao; Yu Tao

    2009-01-01

    The error measurement and diagnosis process of roll grinder NC has dynamic complexity, non-linearity, and comprehensive characteristics. However, presently roll error measurement examination mostly uses the manual examination or single parameter optimization, and the efficiency of fault diagnosis is also inefficient. In this study, the multi-objective intelligence optimization model (MIOM) is applied to the roller error measurement and diagnosis. The

  11. Robust fault diagnosis of physical systems in operation. Ph.D. Thesis - Rutgers - The State Univ.

    NASA Technical Reports Server (NTRS)

    Abbott, Kathy Hamilton

    1991-01-01

    Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of diagnosis. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based diagnosis systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative diagnosis. The suitability of this constructive approach is shown for diagnosing fault propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.

  12. A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques

    Microsoft Academic Search

    Z. Peng; N. J. Kessissoglou; M. Cox

    2005-01-01

    Vibration and wear debris analyses are the two main conditions monitoring techniques for machinery maintenance and fault diagnosis. These two techniques have their unique advantages and disadvantages associated with the monitoring and fault diagnosis of machinery. When these techniques are conducted independently, only a portion of machine faults are typically diagnosed. However, practical experience has shown that integrating these two

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

    PubMed

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

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

    NASA Technical Reports Server (NTRS)

    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).

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

    NASA Astrophysics Data System (ADS)

    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 and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

  16. A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis

    PubMed Central

    Zhu, Daqi; Bai, Jie; Yang, Simon X.

    2010-01-01

    A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. PMID:22315537

  17. Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

    NASA Technical Reports Server (NTRS)

    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 or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

  18. Electrical Motor Current Signal Analysis using a Dynamic Time Warping Method for Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Alibarbar, A.; Zhou, X.; Gu, F.; Ball, A. D.

    2011-07-01

    This paper presents the analysis of phase current signals to identify and quantify common faults from an electrical motor based on dynamic time warping (DTW) algorithm. In condition monitoring, measurements are often taken when the motor undertakes varying loads and speeds. The signals acquired in these conditions show similar profiles but have phase shifts, which do not line up in the time-axis for adequate comparison to discriminate the small changes in machine health conditions. In this study, DTW algorithms are exploited to align the signals to an ideal current signal constructed based on average operating conditions. In this way, comparisons between the signals can be made directly in the time domain to obtain residual signals. These residual signals are then based on to extract features for detecting and diagnosing the faults of the motor and components operating under different loads and speeds. This study provides a novel approach to the analysis of electrical current signal for diagnosis of motor faults. Experimental data sets of electrical motor current signals have been studied using DTW algorithms. Results show that DTW based residual signals highlights more the modulations due to the compressor process. And hence can obtain better fault detection and diagnosis results.

  19. Classification techniques for fault detection and diagnosis of an air-handling unit

    SciTech Connect

    House, J.M.; Lee, W.Y.; Shin, D.R.

    1999-07-01

    The objective of this study is to demonstrate the application of several classification techniques to the problem of detecting and diagnosing faults in data generated by a variable-air-volume air-handling unit simulation model and to describe the strengths and weaknesses of the techniques considered. Artificial neural network classifiers, nearest neighbor classifiers, nearest prototype classifiers, a rule-based classifier, and a Bayes classifier are considered for both fault detection and diagnostics. Based on the performance of the classification techniques, the Bayes classifier appears to be a good choice for fault detection. It is a straightforward method that requires limited memory and computational effort, and it consistently yielded the lowest percentage of incorrect diagnosis. For fault diagnosis, the rule-based method is favored for classification problems such as the one considered here, where the various classes of faulty operation are well separated and can be distinguished by a single dominant symptom or feature. Results also indicate that the success or failure of classification techniques hinges to a large degree on an ability to separate different classes of operation in some feature (temperature, pressure, etc.) space. Hence, preprocessing of data to extract dominant features is as important as the selection of the classifier.

  20. Immune Systems Inspired Approach to Anomaly Detection, Fault Localization and Diagnosis in Automotive Engines

    Microsoft Academic Search

    Dragan Djurdjanovic; Jianbo Liu; Kenneth A. Marko; Jun Ni

    2010-01-01

    \\u000a As more electronic devices are integrated into automobiles to improve the reliability, drivability and maintainability, automotive\\u000a diagnosis becomes increasingly difficult. Unavoidable design defects, quality variations in the production process as well\\u000a as different usage patterns make it is infeasible to foresee all possible faults that may occur to the vehicle. As a result,\\u000a many systems rely on limited diagnostic coverage

  1. Fault Diagnosis in Discrete Event Systems Modeled by Partially Observed Petri Nets

    Microsoft Academic Search

    Yu Ru; Christoforos N. Hadjicostis

    2009-01-01

    In this paper, we study fault diagnosis in discrete event systems modeled by partially observed Petri nets, i.e., Petri nets\\u000a equipped with sensors that allow observation of the number of tokens in some of the places and\\/or partial observation of the\\u000a firing of some of the transitions. We assume that the Petri net model is accompanied by a (possibly implicit)

  2. Model-Based Fault Diagnosis Using Sliding Mode Observers to Takagi-Sugeno Fuzzy Model

    Microsoft Academic Search

    B. Castillo-Toledo; J. Anzurez-Marin

    2005-01-01

    In this paper, we present some results obtained from the application of a class of sliding mode observers to the model-based fault diagnosis problem in non-linear dynamic systems. A Takagi-Sugeno fuzzy model is used to describe the system and then sliding mode observers are designed to estimate the system state vector, from this the diagnostic signal-residual is generated by the

  3. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    PubMed

    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. PMID:25993810

  4. Hierarchical fault diagnosis and health monitoring in multi-platform space systems

    Microsoft Academic Search

    A. Barua; K. Khorasani

    2009-01-01

    Current spacecraft health monitoring and fault diagnosis practices that involve around-the-clock limit-checking and trend analysis on large amount of telemetry data, do not scale well for future multi-platform space missions due to the presence of larger amount of telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team. The need

  5. Multiple faults diagnosis for sensors in air handling unit using Fisher discriminant analysis

    Microsoft Academic Search

    Zhimin Du; Xinqiao Jin

    2008-01-01

    This paper presents a data-driven method based on principal component analysis and Fisher discriminant analysis to detect and diagnose multiple faults including fixed bias, drifting bias, complete failure of sensors, air damper stuck and water valve stuck occurred in the air handling units. Multi-level strategies are developed to improve the diagnosis efficiency. Firstly, system-level PCA model I based on energy

  6. Developing a Knowledge-Based System Using Rough Set Theory and Genetic Algorithms for Substation Fault Diagnosis

    Microsoft Academic Search

    Ching Lai Hor; Peter Crossley; Simon Watson; Dean Millar

    Supervisory Control and Data Acquisition (SCADA) systems are fundamental tools for quick fault diagnosis and efficient restoration\\u000a of power systems. When multiple faults, or malfunctions of protection devices occur in the system, the SCADA system issues\\u000a many alarm signals rapidly and relays these to the control center. The original cause and location of the fault can be difficult\\u000a to determine

  7. Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution

    Microsoft Academic Search

    Jing Yuan; ZhengJia He; YanYang Zi; Han Liu

    2009-01-01

    Fault diagnosis of rolling mills, especially the main drive gearbox, is of great importance to the high quality products and\\u000a long-term safe operation. However, the useful fault information is usually submerged in heavy background noise under the severe\\u000a condition. Thereby, a novel method based on multiwavelet sliding window neighboring coefficient denoising and optimal blind\\u000a deconvolution is proposed for gearbox fault

  8. Integrated model-based and data-driven fault detection and diagnosis approach for an automotive electric power steering system

    Microsoft Academic Search

    Rajeev Ghimire; Chaitanya Sankavaram; Alireza Ghahari; Krishna Pattipati; Youssef Ghoneim; Mark Howell; Mutasim Salman

    2011-01-01

    Integrity of electric power steering system is vital to vehicle handling and driving performance. Advances in electric power steering (EPS) system have increased complexity in detecting and isolating faults. In this paper, we propose a hybrid model-based and data-driven approach to fault detection and diagnosis (FDD) in an EPS system. We develop a physics- based model of an EPS system,

  9. Board-Level Fault Diagnosis using Bayesian Inference Zhaobo Zhang, Zhanglei Wang, Xinli Gu and Krishnendu Chakrabarty

    E-print Network

    Chakrabarty, Krishnendu

    are controllable; diagnosis is relatively easy compared to that at the board/system level. At the board level, both and Krishnendu Chakrabarty ECE Dept., Duke University, Durham, NC Cisco Systems, Inc., San Jose, CA Abstract fault- insertion test at the module pin level on a fault-free board, and then use this database along

  10. Development of non-energy-saving fault diagnosis software and hardware integration platform of groundwater source heat system

    Microsoft Academic Search

    Zhiwei Wang; Lei Shi; Zhonghe Zhang; Wei Cao; Peng Li

    2010-01-01

    For a groundwater source heat pump (GWHP) system, how to realize system high efficiency operation is very important issue. On basis of development idea of modular and open energy management, a model of non-energy-saving (NES) fault diagnosis has been presented, the model involves input of basic characteristic data and index characteristic data and output of NES fault factors, the relationship

  11. Classifier ensembles to improve the robustness to noise of bearing fault diagnosis

    Microsoft Academic Search

    Beatrice LazzeriniSara; Sara Lioba Volpi

    In this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing\\u000a multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and\\u000a we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 to

  12. Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis

    Microsoft Academic Search

    Ruqiang Yan; Robert X. Gao

    2009-01-01

    This paper presents a new signal processing algorithm, termed multi-scale enveloping spectrogram (MuSEnS), for vibration signal analysis in the condition monitoring and health diagnosis of rolling bearings. Compared to the conventional enveloping spectral analysis technique in which the bandwidth of the signal components of interest needs to be known a priori to obtain consistent results under varying machine operating conditions,

  13. EMD-based fault diagnosis for abnormal clearance between contacting components in a diesel engine

    NASA Astrophysics Data System (ADS)

    Li, Yujun; Tse, Peter W.; Yang, Xin; Yang, Jianguo

    2010-01-01

    The accuracy of fault diagnostic systems for diesel engine-type generators relies on a comparison of the currently extracted sensory features with those captured during normal operation or the so-called "baseline." However, the baseline is not easily obtained without the required expertise. Even worse, in an attempt to save costs, many of the diesel engine generators in manufacturing plants are second hand or have been purchased from unknown suppliers, meaning that the baseline is unknown. In this paper, a novel vibration-based fault diagnostic method is developed to identify the vital components of a diesel engine that have abnormal clearance. The advantage of this method is that it does not require the comparison of current operating parameters to those collected as the baseline. First, the nominal baseline is obtained via theoretical modeling rather than being actually captured from the sensory signals in a healthy condition. The abnormal clearance is then determined by inspecting the timing of impacts created by the components that had abnormal clearance during operation. To detect the timing of these impacts from vibration signals accurately, soft-re-sampling and empirical mode decomposition (EMD) techniques are employed. These techniques have integrated with our proposed ranged angle (RA) analysis to form a new ranged angle-empirical mode decomposition method (RA-EMD). To verify the effectiveness of the RA-EMD in detecting the impacts and their times of occurrence, their induced vibrations are collected from a series of generators under normal and faulty engine conditions. The results show that this method is capable of extracting the impacts induced by vibrations and is able to determine their times of occurrence accurately even when the impacts have been overwhelmed by other unrelated vibration signals. With the help of the RA-EMD, clearance-related faults, such as incorrect open and closed valve events, worn piston rings and liners, etc., become detectable even without the comparison to the baseline. Hence, proper remedies can be applied to defective diesel engines to ensure that valuable fuel is not wasted due to the incorrect timing of combustion as well as unexpected fatal breakdown, which may cause loss of production or even human casualties, can be minimized.

  14. ACTUATOR/SENSORS FAULT DIAGNOSIS FOR AN EXPERIMENTAL HOT ROLLING MILL A CASE STUDY D. THEILLIOL (), M. MAHFOUF(+), D. SAUTER(), J.C. PONSART()

    E-print Network

    Paris-Sud XI, Université de

    ACTUATOR/SENSORS FAULT DIAGNOSIS FOR AN EXPERIMENTAL HOT ROLLING MILL ­ A CASE STUDY D. THEILLIOL of such problems from raw data trends is often difficult, however model-based approach among fault diagnosis methods or Fault Detection and Isolation (FDI) techniques are considered and combined to supervise

  15. Fault Pattern Recognition of Bearing Based on Principal Components Analysis and Support Vector Machine

    Microsoft Academic Search

    Lu Shuang; Yu Fujin

    2009-01-01

    State monitoring and fault diagnosing of rolling bearing by analyzing vibrating signal is one of the major problem which need to be solved in mechanical engineering. In this paper, a new method of fault diagnosis based on principal components analysis and support vector machine is presented on the basis of statistical learning theory and the feature analysis of vibrating signal

  16. Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test

    NASA Astrophysics Data System (ADS)

    Wang, Xiyang; Makis, Viliam

    2009-11-01

    Vibration behavior induced by gear shaft crack is different from that induced by gear tooth crack. Hence, a fault indicator used to detect tooth damage may not be effective for monitoring shaft condition. This paper proposes an autoregressive model-based technique to detect the occurrence and advancement of gear shaft cracks. An autoregressive model is fitted to the time synchronously averaged signal of the gear shaft in its healthy state. The order of the autoregressive model is selected using Akaike information criterion and the coefficient estimates are obtained by solving the Yule-Walker equations with the Levinson-Durbin recursion algorithm. The established autoregressive model is then used as a linear prediction filter to process the future signal. The Kolmogorov-Smirnov test is applied on line for the prediction of error signals. The calculated distance is used as a fault indicator and its capability to diagnose shaft crack effectively is demonstrated using a full lifetime gear shaft vibration data history. The other frequently used statistical measures such as kurtosis and variance are also calculated and the results are compared with the Kolmogorov-Smirnov test.

  17. A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

    SciTech Connect

    Zhang Yumin; Lum, Kai-Yew [Temasek Laboratories, National University of Singapore, Singapore 117508 (Singapore); Wang Qingguo [Depa. Electrical and Computer Engineering, National University of Singapore, Singapore 117576 (Singapore)

    2009-03-05

    In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.

  18. A modular neural network scheme applied to fault diagnosis in electric power systems.

    PubMed

    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. PMID:25610897

  19. Stochastic resonance with Woods-Saxon potential for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Lu, Siliang; He, Qingbo; Kong, Fanrang

    2014-04-01

    This paper proposes a weak signal detection strategy for rolling element bearing fault diagnosis by investigating a new mechanism to realize stochastic resonance (SR) based on the Woods-Saxon (WS) potential. The WS potential has the distinct structure with smooth potential bottom and steep potential wall, which guarantees a stable particle motion within the potential and avoids the unexpected noises for the SR system. In the Woods-Saxon SR (WSSR) model, the output signal-to-noise ratio (SNR) can be optimized just by tuning the WS potential's parameters, which delivers the most significant merit that the limitation of small parameter requirement of the classical bistable SR can be overcome, and thus a wide range of driving frequencies can be detected via the SR model. Furthermore, the proposed WSSR model is also insensitive to the noise, and can detect the weak signals with different noise levels. Additionally, the WS potential can be designed accurately due to its parameter independence, which implies that the proposed method can be matched to different input signals adaptively. With these properties, the proposed weak signal detection strategy is indicated to be beneficial to rolling element bearing fault diagnosis. Both the simulated and the practical bearing fault signals verify the effectiveness and efficiency of the proposed WSSR method in comparison with the traditional bistable SR method.

  20. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

    SciTech Connect

    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.

  1. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    PubMed Central

    Flores, Agustín; Morant, Francisco

    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. PMID:25610897

  2. IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 24, NO. 2, JUNE 2009 431 Fault Detection and Diagnosis in a Set

    E-print Network

    Boyer, Edmond

    W inverter-fed asynchronous motor, in order to detect supply and motor faults. In this application the efficiency of our diagnosis method. Index Terms--Data standardization, diagnosis, induction ma- chine effective when the motor is supplied by the three-phase main network. However, in more and more industrials

  3. Fault Diagnosis in Discrete-Event Systems with Incomplete Models: Learnability and Diagnosability.

    PubMed

    Kwong, Raymond H; Yonge-Mallo, David L

    2015-07-01

    Most model-based approaches to fault diagnosis of discrete-event systems require a complete and accurate model of the system to be diagnosed. However, the discrete-event model may have arisen from abstraction and simplification of a continuous time system, or through model building from input-output data. As such, it may not capture the dynamic behavior of the system completely. In a previous paper, we addressed the problem of diagnosing faults given an incomplete model of the discrete-event system. We presented the learning diagnoser which not only diagnoses faults, but also attempts to learn missing model information through parsimonious hypothesis generation. In this paper, we study the properties of learnability and diagnosability. Learnability deals with the issue of whether the missing model information can be learned, while diagnosability corresponds to the ability to detect and isolate a fault after it has occurred. We provide conditions under which the learning diagnoser can learn missing model information. We define the notions of weak and strong diagnosability and also give conditions under which they hold. PMID:25204002

  4. Wireless power transfer and fault diagnosis of high-voltage power line via robotic bird

    NASA Astrophysics Data System (ADS)

    Liu, Chunhua; Chau, K. T.; Zhang, Zhen; Qiu, Chun; Li, Wenlong; Ching, T. W.

    2015-05-01

    This paper presents a new idea of wireless power transfer (WPT) and fault diagnosis (FD) of high-voltage power line via robotic bird. The key is to present the conceptual robotic bird with WPT coupling coil for detecting and capturing the energy from the high-voltage power line. If the power line works in normal condition, the robotic bird is able to stand on the power line and extract energy from it. If fault occurs on the power line, the corresponding magnetic field distribution will become different from that in the normal situation. By analyzing the magnetic field distribution of the power line, the WPT to the robotic bird and the FD by the robotic bird are performed and verified.

  5. FAULT DIAGNOSIS WITH MULTI-STATE ALARMS IN A NUCLEAR POWER CONTROL SIMULATOR

    SciTech Connect

    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.

  6. Fault Diagnosis with Multi-State Alarms in a Nuclear Power Control Simulation

    SciTech Connect

    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.

  7. Application of data mining and feature extraction on intelligent fault diagnosis by Artificial Neural Network and k-nearest neighbor

    Microsoft Academic Search

    Behrad Bagheri; Hojat Ahmadi; Reza Labbafi

    2010-01-01

    In this paper the frequency domain vibration signals of the gearbox of MF285 tractor is used for fault classification in three class: Healthy gear, Worn tooth face and broken gear. The effect of applying statistical parameters to signals on accuracy is studied. In addition, Influence of feature selection using Improved Distance Evaluation on classification performance and training speed is another

  8. An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line

    Microsoft Academic Search

    A. Ngaopitakkul; S. Bunjongjit

    2012-01-01

    This article proposes an application of the discrete wavelet transform (DWT) and back-propagation neural networks (BPNN) for fault diagnosis on single-circuit transmission line. ATP\\/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is used to decompose the high-frequency component of these signals. In addition, characteristics of the fault current at various fault inception angles, fault locations and

  9. Balancing filters: An approach to improve model-based fault diagnosis based on parity equations

    NASA Astrophysics Data System (ADS)

    Beckerle, Philipp; Schaede, Hendrik; Butzek, Norman; Rinderknecht, Stephan

    2012-05-01

    Model-based fault detection often deals with the problem that fault states cannot be distinguished clearly. One way to improve the results is the use of balancing filters. The purpose of these filters is to balance the magnitude response over its full frequency range, since fault states show deviations from the nominal behavior at different frequencies and therefore at diverse magnitude levels. Their application aims on increasing the magnitude response levels in frequency ranges where it is low and to decrease it where the magnitude is on a higher level. Hence, the influence of the deviations caused by the fault states is weighted equally at all examined frequencies. The compensation of the system's basic characteristics leads to a stronger influence of the fault-caused deviations. Since these are useable features for fault identification, balancing filters lead to a better distinction between the states and faults. To apply such filters on real systems they must be designed and adapted to the particular system. This paper describes the idea of balancing filters for a diagnosis concept based on feature extraction by means of parity equations and shows several methods to design these filters. The first design method is based on placing poles and zeros heuristically to model the global characteristics of the frequency response and inverting this model to get a balancing filter. In contrast to this, the second approach uses measured data by inverting an experimentally identified model of the process. For the third method simple Butterworth filter elements are used to build up an inverted model of the global frequency behavior of the system directly. Since an adaptation of the filters to the investigated system is required experimental results show the improvements induced by these filters. The filters' effects are investigated on a test rig of a centrifugal pump with magnetic bearings. A second system that shows a more complex transfer behavior is used for the evaluation of the repeatability of the resulting improvements. Finally the idea of balancing filters, the presented design methods and the achieved experimental results are discussed in details.

  10. A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

    PubMed Central

    Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

    2014-01-01

    A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

  11. Advanced fault diagnosis for the mass production of small-power electric motors

    NASA Astrophysics Data System (ADS)

    Filbert, Dieter

    1993-09-01

    High quality is a principal goal in the mass production of electric niotors (i.e. d.c. motors for cars and universal motors for house hold appliances).The processing of vibration and acoustical signals are widely used in quality assurance in the mass production but the coupling of the sensors to the motor as well as noise produced in the environment make it still difficult to get reproducible diagnostic results. High quality in production can be achieved by the powerful modern diagnostic methods which became possible because of the progress in microelectronics (microprocessors and signal processors). This progress made mathematical methods and signal processing applicable. Therefore this paper deals with diagnostic methods that use the measured signals of voltage, current and speed only but achieve a good testing. It gives an overview of new methods for the feature extraction and fault detection on small power electric motors.

  12. Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients

    NASA Astrophysics Data System (ADS)

    Baraldi, Piero; Di Maio, Francesco; Rigamonti, Marco; Zio, Enrico; Seraoui, Redouane

    2015-06-01

    Empirical methods for fault diagnosis usually entail a process of supervised training based on a set of examples of signal evolutions "labeled" with the corresponding, known classes of fault. However, in practice, the signals collected during plant operation may be, very often, "unlabeled", i.e., the information on the corresponding type of occurred fault is not available. To cope with this practical situation, in this paper we develop a methodology for the identification of transient signals showing similar characteristics, under the conjecture that operational/faulty transient conditions of the same type lead to similar behavior in the measured signals evolution. The methodology is founded on a feature extraction procedure, which feeds a spectral clustering technique, embedding the unsupervised fuzzy C-means (FCM) algorithm, which evaluates the functional similarity among the different operational/faulty transients. A procedure for validating the plausibility of the obtained clusters is also propounded based on physical considerations. The methodology is applied to a real industrial case, on the basis of 148 shut-down transients of a Nuclear Power Plant (NPP) steam turbine.

  13. Distributed intrusion monitoring system with fiber link backup and on-line fault diagnosis functions

    NASA Astrophysics Data System (ADS)

    Xu, Jiwei; Wu, Huijuan; Xiao, Shunkun

    2014-12-01

    A novel multi-channel distributed optical fiber intrusion monitoring system with smart fiber link backup and on-line fault diagnosis functions was proposed. A 1× N optical switch was intelligently controlled by a peripheral interface controller (PIC) to expand the fiber link from one channel to several ones to lower the cost of the long or ultra-long distance intrusion monitoring system and also to strengthen the intelligent monitoring link backup function. At the same time, a sliding window auto-correlation method was presented to identify and locate the broken or fault point of the cable. The experimental results showed that the proposed multi-channel system performed well especially whenever any a broken cable was detected. It could locate the broken or fault point by itself accurately and switch to its backup sensing link immediately to ensure the security system to operate stably without a minute idling. And it was successfully applied in a field test for security monitoring of the 220-km-length national borderline in China.

  14. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    PubMed

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  15. Research on the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust fault

    Microsoft Academic Search

    Shi-Gui Lv; Li Yang; Qian Yang

    2011-01-01

    This paper mainly introduces the basic principles, the methods and the applications of infrared technique in the diagnosis\\u000a and prediction of diesel engine exhaust faults. The test-bed for monitoring diesel engine exhaust faults by thermal infrared\\u000a imager has been designed. In different running conditions, the exterior surface radiation temperatures of the exhaust pipe\\u000a of the 6135G-1 diesel engine have been

  16. Combined expert system/neural networks method for process fault diagnosis

    DOEpatents

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

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

    NASA Technical Reports Server (NTRS)

    Lee, S. C.

    1989-01-01

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

  18. Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    He, Qingbo

    2013-02-01

    Time-frequency feature is beneficial to representation of non-stationary signals for effective machinery fault diagnosis. The time-frequency distribution (TFD) is a major tool to reveal the synthetic time-frequency pattern. However, the TFD will also face noise corruption and dimensionality reduction issues in engineering applications. This paper proposes a novel nonlinear time-frequency feature based on a time-frequency manifold (TFM) technique. The new TFM feature is generated by mainly addressing manifold learning on the TFDs in a reconstructed phase space. It combines the non-stationary information and the nonlinear information of analyzed signals, and hence exhibits valuable properties. Specifically, the new feature is a quantitative low-dimensional representation, and reveals the intrinsic time-frequency pattern related to machinery health, which can effectively overcome the effects of noise and condition variance issues in sampling signals. The effectiveness and the merits of the proposed TFM feature are confirmed by case study on gear wear diagnosis, bearing defect identification and defect severity evaluation. Results show the value and potential of the new feature in machinery fault pattern representation and classification.

  19. Differential diagnosis of lung carcinoma with three-dimensional quantitative molecular vibrational imaging

    NASA Astrophysics Data System (ADS)

    Gao, Liang; Hammoudi, Ahmad A.; Li, Fuhai; Thrall, Michael J.; Cagle, Philip T.; Chen, Yuanxin; Yang, Jian; Xia, Xiaofeng; Fan, Yubo; Massoud, Yehia; Wang, Zhiyong; Wong, Stephen T. C.

    2012-06-01

    The advent of molecularly targeted therapies requires effective identification of the various cell types of non-small cell lung carcinomas (NSCLC). Currently, cell type diagnosis is performed using small biopsies or cytology specimens that are often insufficient for molecular testing after morphologic analysis. Thus, the ability to rapidly recognize different cancer cell types, with minimal tissue consumption, would accelerate diagnosis and preserve tissue samples for subsequent molecular testing in targeted therapy. We report a label-free molecular vibrational imaging framework enabling three-dimensional (3-D) image acquisition and quantitative analysis of cellular structures for identification of NSCLC cell types. This diagnostic imaging system employs superpixel-based 3-D nuclear segmentation for extracting such disease-related features as nuclear shape, volume, and cell-cell distance. These features are used to characterize cancer cell types using machine learning. Using fresh unstained tissue samples derived from cell lines grown in a mouse model, the platform showed greater than 97% accuracy for diagnosis of NSCLC cell types within a few minutes. As an adjunct to subsequent histology tests, our novel system would allow fast delineation of cancer cell types with minimum tissue consumption, potentially facilitating on-the-spot diagnosis, while preserving specimens for additional tests. Furthermore, 3-D measurements of cellular structure permit evaluation closer to the native state of cells, creating an alternative to traditional 2-D histology specimen evaluation, potentially increasing accuracy in diagnosing cell type of lung carcinomas.

  20. Usefulness of high-resolution thermography in fault diagnosis of fluid power components and systems

    NASA Astrophysics Data System (ADS)

    Pietola, Matti; Varrio, Jukka P.

    1996-03-01

    Infrared thermography has been used routinely in industrial applications for quite a long time. For example, the condition of electric power lines, district heating networks, electric circuits and components, heat exchangers, pipes and its insulations, cooling towers, and various machines and motors is monitored using infrared imaging techniques. Also the usage of this technology in predictive maintenance has proved successful, mainly because of effective computers and tailored softwares available. However, the usage of thermal sensing technique in fluid power systems and components (or other automation systems in fact) is not as common. One apparent reason is that a fluid power circuit is not (and nor is a hydraulic component) an easy object of making thermal image analyses. Especially the high flow speed, fast pressure changes and fast movements make the diagnosis complex and difficult. Also the number of people whose knowledge is good both in thermography and fluid power systems is not significant. In this paper a preliminary study of how thermography could be used in the condition monitoring, fault diagnosis and predictive maintenance of fluid power components and systems is presented. The shortages and limitations of thermal imaging in the condition monitoring of fluid power are also discussed. Among many other cases the following is discussed: (1) pressure valves (leakage, wrong settings), (2) check valves (leakage); (3) cylinders (leakage and other damages); (4) directional valves and valve assemblies; (5) pumps and motors (leakage in piston or control plate, bearings). The biggest advantage of using thermography in the predictive maintenance and fault diagnosis of fluid power components and systems could be achieved in the process industry and perhaps in the commissioning of fluid power systems in the industry. In the industry the predictive maintenance of fluid power with the aid of an infrared camera could be done as part of a condition monitoring of other systems, for instance bearings.

  1. Case Study on Fault Diagnosis of the Actual Operating Transformer by FRA

    NASA Astrophysics Data System (ADS)

    Sano, Takahiro; Ogawa, Yoshiharu; Shimonosono, Takaaki; Wada, Tadayuki

    A high voltage, large capacity power transformer is one of the most important equipment in electric power system. If a failure occurs in such a transformer, stable power supply may become impossible. In addition, efficient power system operation may become difficult because it takes long time to replace the transformer. To prevent failures that may occur, effective external diagnosis must be performed and the defective portions must be correctly identified. However, such anomalous phenomena are complicated in many cases, and their causes cannot be identified in some cases by using conventional techniques. This paper reports a case study on the fault diagnosis of an oil-immersed power transformer that had a tendency to increase in the total combustible gas (TCG) during a regular operation. Specifying the faulty parts became possible by applying various case of Frequency Response Analysis (FRA) diagnosis though it was impossible by the electrical tests, DGA (Dissolved Gas Analysis), and so on. This transformer was disassembled to investigate the condition and was replaced without causing failure.

  2. Research on Multi-agent System Model of Diesel Engine Fault Diagnosis by Case-Based Reasoning

    Microsoft Academic Search

    Chunhua Zhao; Xinze Zhao; Zongqi Tan; Xinping Yan

    2006-01-01

    Oil monitoring technology is a useful method in condition monitoring and fault diagnosis for the machine, especially for low-speed, heavy-load, reciprocated and lubricated diesel engine equipment. But it is difficult to implement intelligent diagnosis because monitored information lacks logical relationship in oil monitoring. To solve this problem, the theory and method of case-based reasoning is adopted for the data processing

  3. Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

    PubMed Central

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

  4. OPAD: An expert system for research reactor operations and fault diagnosis using probabilistic safety assessment tools

    SciTech Connect

    Verma, A.K. [Indian Inst. of Technology, Bombay (India). Dept. of Electrical Engineering; Varde, P.V.; Sankar, S. [Bhabha Atomic Research Centre, Bombay (India). Reactor Operations Division; Prakash, P. [Nuclear Power Corp., Bombay (India). Directorate of Safety

    1996-07-01

    A prototype Knowledge Based (KB) operator Adviser (OPAD) system has been developed for 100 MW(th) Heavy Water moderated, cooled and Natural Uranium fueled research reactor. The development objective of this system is to improve reliability of operator action and hence the reactor safety at the time of crises as well as normal operation. The jobs performed by this system include alarm analysis, transient identification, reactor safety status monitoring, qualitative fault diagnosis and procedure generation in reactor operation. In order to address safety objectives at various stages of the Operator Adviser (OPAD) system development the Knowledge has been structured using PSA tools/information in an shell environment. To demonstrate the feasibility of using a combination of KB approach with PSA for operator adviser system, salient features of some of the important modules (viz. FUELEX, LOOPEX and LOCAEX) have been discussed. It has been found that this system can serve as an efficient operator support system.

  5. A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

    NASA Astrophysics Data System (ADS)

    Propes, Nicholas C.; Vachtsevanos, George

    2003-08-01

    Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

  6. Fault detection and diagnosis for satellite's attitude control system (ACS) using an interactive multiple model (IMM) approach

    Microsoft Academic Search

    N. Tudoroiu; K. Khorasani

    2005-01-01

    The main objective of this paper is development of a fault diagnosis, isolation and detection technique that is constructed based on the interacting multiple model (IMM) algorithm for partial (soft) or total (hard) reaction wheel failures in the spacecraft attitude control system (ACS). Based on different scenarios and assumptions, we develop healthy models of the ACS under various operating conditions

  7. Built-in Self-Test and Fault Diagnosis for Lab-on-Chip Using Digital Microfluidic Logic Gates

    E-print Network

    Chakrabarty, Krishnendu

    Built-in Self-Test and Fault Diagnosis for Lab-on-Chip Using Digital Microfluidic Logic Gates Yang University, Durham, NC 27708, USA Abstract Dependability is an important system attribute for microfluidic are cumbersome and error-prone. We present a built-in self-test (BIST) method for digital microfluidic lab

  8. A robust algebraic approach to fault diagnosis of uncertain linear Abdouramane Moussa Ali, Cedric Join and Frederic Hamelin

    E-print Network

    Paris-Sud XI, Université de

    A robust algebraic approach to fault diagnosis of uncertain linear systems Abdouramane Moussa Ali Ali, C. Join and F. Hamelin are with Research Center for Automatic Control, Nancy-University, CNRS, 54506 Vandoeuvre, France {amoussaa, cjoin, fhamelin}@cran.uhp-nancy.fr A. Moussa Ali joined the project

  9. www.cesos.ntnu.no Bo Zhao Centre for Ships and Ocean Structures Fault Diagnosis based on Particle Filter

    E-print Network

    Nørvåg, Kjetil

    1 www.cesos.ntnu.no Bo Zhao ­ Centre for Ships and Ocean Structures Fault Diagnosis based for Ships and Ocean Structures Danger and harm Danger and harm PollutionPollution Property lossProperty loss.cesos.ntnu.no Bo Zhao ­ Centre for Ships and Ocean Structures Data from: The Software Problem ++, Marine

  10. Incipient interturn fault diagnosis in induction machines using an analytic wavelet-based optimized Bayesian inference.

    PubMed

    Seshadrinath, Jeevanand; Singh, Bhim; Panigrahi, Bijaya Ketan

    2014-05-01

    Interturn fault diagnosis of induction machines has been discussed using various neural network-based techniques. The main challenge in such methods is the computational complexity due to the huge size of the network, and in pruning a large number of parameters. In this paper, a nearly shift insensitive complex wavelet-based probabilistic neural network (PNN) model, which has only a single parameter to be optimized, is proposed for interturn fault detection. The algorithm constitutes two parts and runs in an iterative way. In the first part, the PNN structure determination has been discussed, which finds out the optimum size of the network using an orthogonal least squares regression algorithm, thereby reducing its size. In the second part, a Bayesian classifier fusion has been recommended as an effective solution for deciding the machine condition. The testing accuracy, sensitivity, and specificity values are highest for the product rule-based fusion scheme, which is obtained under load, supply, and frequency variations. The point of overfitting of PNN is determined, which reduces the size, without compromising the performance. Moreover, a comparative evaluation with traditional discrete wavelet transform-based method is demonstrated for performance evaluation and to appreciate the obtained results. PMID:24808044

  11. Roller bearing fault diagnosis based on nonlinear redundant lifting wavelet packet analysis.

    PubMed

    Gao, Lixin; Yang, Zijing; Cai, Ligang; Wang, Huaqing; Chen, Peng

    2011-01-01

    A nonlinear redundant lifting wavelet packet algorithm was put forward in this study. For the node signals to be decomposed in different layers, predicting operators and updating operators with different orders of vanishing moments were chosen to take norm l(p) of the scale coefficient and wavelet coefficient acquired from decomposition, the predicting operator and updating operator corresponding to the minimal norm value were used as the optimal operators to match the information characteristics of a node. With the problems of frequency alias and band interlacing in the analysis of redundant lifting wavelet packet being investigated, an improved algorithm for decomposition and node single-branch reconstruction was put forward. The normalized energy of the bottommost decomposition node coefficient was calculated, and the node signals with the maximal energy were extracted for demodulation. The roller bearing faults were detected successfully with the improved analysis on nonlinear redundant lifting wavelet packet being applied to the fault diagnosis of the roller bearings of the finishing mills in a plant. This application proved the validity and practicality of this method. PMID:22346576

  12. Roller Bearing Fault Diagnosis Based on Nonlinear Redundant Lifting Wavelet Packet Analysis

    PubMed Central

    Gao, Lixin; Yang, Zijing; Cai, Ligang; Wang, Huaqing; Chen, Peng

    2011-01-01

    A nonlinear redundant lifting wavelet packet algorithm was put forward in this study. For the node signals to be decomposed in different layers, predicting operators and updating operators with different orders of vanishing moments were chosen to take norm lp of the scale coefficient and wavelet coefficient acquired from decomposition, the predicting operator and updating operator corresponding to the minimal norm value were used as the optimal operators to match the information characteristics of a node. With the problems of frequency alias and band interlacing in the analysis of redundant lifting wavelet packet being investigated, an improved algorithm for decomposition and node single-branch reconstruction was put forward. The normalized energy of the bottommost decomposition node coefficient was calculated, and the node signals with the maximal energy were extracted for demodulation. The roller bearing faults were detected successfully with the improved analysis on nonlinear redundant lifting wavelet packet being applied to the fault diagnosis of the roller bearings of the finishing mills in a plant. This application proved the validity and practicality of this method. PMID:22346576

  13. Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

    PubMed Central

    Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

    2014-01-01

    Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. PMID:24678281

  14. Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors.

    PubMed

    Camarena-Martinez, David; Valtierra-Rodriguez, Martin; Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

    2014-01-01

    Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. PMID:24678281

  15. Effect of Fault Clearing and Damper Modelling on Excitation and Decay of Vibrations in Generator Shafts Following Severe Disturbances on the System Supply

    Microsoft Academic Search

    T. J. Hammons

    1987-01-01

    The paper examines the effect damper circuit modelling and the current interruption process has on amplitude and decay of torsional vibrations in turbine-generator shafts following severe supply network disturbances. A phase-variable model of a synchronous-generator with up to 2 direct-axis and 3 quadrature-axis dampers where fault current is cleared at fault current zeros is employed to calculate generator airgap torque

  16. A Hybrid Model Based and Statistical Fault Diagnosis System for Industrial Process

    E-print Network

    Lin, Chen-Han

    2014-11-21

    ...................................... 46 Figure 22. The residual signal of leakage fault with five percent noise and disturbance .......... 46 vi Page Figure 23. The SSPRT result of the leakage fault with five percent noise and disturbance ...... 47 Figure 24. The actuator... fault with one percent noise and disturbance ..................................... 48 Figure 25. The residual signal of actuator fault with one percent noise and disturbance .......... 49 Figure 26. The SSPRT result of the actuator fault with one...

  17. Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Ming, A. B.; Zhang, W.; Qin, Z. Y.; Chu, F. L.

    2015-01-01

    Commonly presented as cyclic impulse responses with some degrees of randomness, the vibrations induced by bearing faults are multi-component signals and usually overwhelmed by other deterministic components, which may degrade the efficiency of the traditional envelope analysis used for bearing fault feature extraction. In this paper, the envelope of the multi-component signal, including both discrete frequency components and cyclic impulse responses, is theoretically calculated by the Hilbert transform in both time and frequency domains at first. Then, a novel deterministic component cancellation method is proposed based on the iterative calculation of the signal envelope. Finally, simulations and experiments are used to validate the theoretical calculation and the proposed deterministic component cancellation method. It is indicated that the oscillation part of the envelope is dominated by the cross-terms of the multi-component signal, and that the cross-terms between a discrete frequency component and cyclic impulse responses present as new cyclic impulse responses, which retain the cyclic feature of the original ones. Furthermore, the deterministic component can be canceled by iteratively subtracting the direct current (DC) offset of the envelope. Compared with the cepstrum pre-whiten (CPW) method, used to separate the deterministic (discrete frequency) component from the random component (vibration induced by the bearing fault), the proposed method is more efficient to the shifting of the cyclic impulse responses from the powerful deterministic component with little disruption, and is more suitable for the real time signal processing owing to the high efficient calculation of the Hilbert transform.

  18. Advanced fault diagnosis techniques and their role in preventing cascading blackouts

    E-print Network

    Zhang, Nan

    2007-04-25

    Positioning System of satellites to synchronize data samples from the two ends of the transmission line. The effort has been made to extend the fault location scheme to a complete fault detection, classification and location scheme. Without an extra data...

  19. Condition detection and fault diagnosis system for recoil system based on simulation

    Microsoft Academic Search

    Lijun Cao; Huibin Hu; Junqi Qin

    2009-01-01

    Dynamic analysis is carried out for recoil parts in recoil and counter-recoil process to find out the fault effects of liquid and gas leakage of recoil brake and recuperator. In order to detect and simulate the fault occurrence and development process, virtual prototyping is firstly adopted into fault simulation and detection fields in this paper. The basic steps of virtual

  20. Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

    NASA Technical Reports Server (NTRS)

    Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong

    2011-01-01

    A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

  1. [Application of ICP-AES in automotive hydraulic power steering system fault diagnosis].

    PubMed

    Chen, Li-Dan

    2013-01-01

    The authors studied the innovative applications of the inductively coupled plasma-atomic emission spectrometry in automotive hydraulic power steering system fault diagnosis. After having determined Fe, Cu and Al content in the four groups of Buick Regal 2.4 main metal power-steering fluid whose travel course was respectively 2-9 thousand kilometers, 11-18 thousand kilometers, 22-29 thousandkilometers, and 31-40 thousand kilometers, and the database of primary metal content in the Buick Regal 2.4 different mileage power-steering fluid was established. The research discovered that the main metal content increased with increasing mileage and its normal level is between the two trend lines. Determination of the power-steering fluid main metal content and comparison with its database value can not only judge the wear condition of the automotive hydraulic power steering system and maintain timely to avoid the traffic accident, but also help the automobile detection and maintenance personnel to diagnose failure reasons without disintegration. This reduced vehicle maintenance costs, and improved service quality. PMID:23586258

  2. Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal

    Microsoft Academic Search

    Yonghua Zhu; Xinqiao Jin; Zhimin Du

    This paper presents a new fault diagnosis method for sensors in an air-handling unit based on neural network pre-processed by wavelet and fractal (NNPWF). Three-level wavelet analysis is applied to decompose the measurement data, and then fractal dimensions of each frequency band are extracted and used to depict the failure characteristics of the sensors. With these procedures, a signal is

  3. Tri-tier Immune System in Antivirus and Software Fault Diagnosis of Mobile Immune Robot Based on Normal Model

    Microsoft Academic Search

    Tao Gong; Zixing Cai

    2008-01-01

    In this paper, anti-virus problem and software fault diagnosis of mobile robot, an immune robot, is discussed with proposal\\u000a of a novel tri-tier immune system (TTIS). TTIS is a novel artificial immune system, which is comprised of three computing\\u000a tiers and based on the normal model. The three tiers include inherent immune tier, adaptive immune tier and parallel immune\\u000a tier.

  4. RMINE: A Rough Set Based Data Mining Prototype for the Reasoning of Incomplete Data in Condition-based Fault Diagnosis

    Microsoft Academic Search

    Jing Rong Li; Li Pheng Khoo; Shu Beng Tor

    2006-01-01

    Condition-based fault diagnosis aims at identifying the root cause of a system malfunction from vast amount of condition-based\\u000a monitoring information and knowledge. The needs of extracting knowledge from vast amount of information have spurred the interest\\u000a in data mining, which can be categorized into two stages data preparation and knowledge extraction. It has been established\\u000a that most of the current

  5. Using roving STARs for on-line testing and diagnosis of FPGAs in fault-tolerant applications

    Microsoft Academic Search

    Miron Abramovici; Charles E. Stroud; Carter Hamilton; Sajitha Wijesuriya; Vinay Verma

    1999-01-01

    In this paper we present a novel integrated approach to on-line FPGA testing, diagnosis, and fault-toler- ance, to be used in high-reliability and high-availability hardware. The test process takes place in self-testing areas (STARs) of the FPGA, without disturbing the normal system operation. The entire chip is eventually tested by having STARs gradually rove across the FPGA. Our approach guar-

  6. Fault diagnosis in nonlinear dynamical systems based on left invertibility condition: A real-time application to three-tank system

    Microsoft Academic Search

    Juan L. Mata-Machuca; Rafael Martinez-Guerra; Jose J. Rincon-Pasaye

    2011-01-01

    This work deals with the fault diagnosis prob- lem, some new properties are found using the left invertibility condition through the concept of differential output rank. Two schemes of nonlinear observers are used to estimate the fault signals for comparison purposes, one of these is a reduced order observer and the other is a sliding mode observer. The methodology is

  7. Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine(MSVM)

    Microsoft Academic Search

    O. R. Seryasat; Mahdi Aliyari Shoorehdeli; F. Honarvar; A. Rahmani

    2010-01-01

    Due to the importance of rolling bearings as one of the most populous used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to suppression malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time domain analysis method has good computational efficiency. The point of interest

  8. Application of the Envelope and Wavelet Transform Analyses for the Diagnosis of Incipient Faults in Ball Bearings

    NASA Astrophysics Data System (ADS)

    Rubini, R.; Meneghetti, U.

    2001-03-01

    Fatigue faults on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. In normal operating conditions this kind of damage can be revealed by classical vibration analyses, such as Spectral or Envelope ones. Furthermore, this last technique—by working in time domain—makes it possible to monitor the longitudinal dimension of the defect. In this paper, the limits of the mentioned methodologies are presented by showing their application to bearings affected by different pitting failures on the outer or inner race or a rolling element and subjected to a very low radial load. Results are compared with that obtained by an advanced signal processing method based on the evaluation of the wavelet transform. Effects of fault evolution are investigated.

  9. Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data

    Microsoft Academic Search

    Hasan Ocak; Kenneth A Loparo

    2004-01-01

    This paper presents two separate algorithms for estimating the running speed and the bearing key frequencies of an induction motor using vibration data. Bearing key frequencies are frequencies at which roller elements pass over a defect point. Most frequency domain-based bearing fault detection and diagnosis techniques (e.g. envelope analysis) rely on vibration measurements and the bearing key frequencies. Thus, estimation

  10. USING VIBRATION MONITORING FOR LOCAL FAULT DETECTION ON GEARS OPERATING UNDER FLUCTUATING LOAD CONDITIONS

    Microsoft Academic Search

    C. J. STANDER; P. S. HEYNS; W. SCHOOMBIE

    2002-01-01

    Gearboxes often operate under fluctuating load conditions during service. Conventional techniques for monitoring vibration are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the gearbox. However, this assumption is not valid for fluctuating load conditions. To find a methodology that could deal with such conditions, experiments were conducted on

  11. Identification of significant intrinsic mode functions for the diagnosis of induction motor fault.

    PubMed

    Cho, Sangjin; Shahriar, Md Rifat; Chong, Uipil

    2014-08-01

    For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work. PMID:25096149

  12. Comparison of Fault Detection Algorithms for Real-time Diagnosis in Large-Scale System. Appendix E

    NASA Technical Reports Server (NTRS)

    Kirubarajan, Thiagalingam; Malepati, Venkat; Deb, Somnath; Ying, Jie

    2001-01-01

    In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of I's and O's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the faults. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time fault diagnosis using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time diagnosis tool), we test the performances of 1) TEAMSAT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) HidderMarkov Model based diagnosis.

  13. Intermittent chaos and sliding window symbol sequence statistics-based early fault diagnosis for hydraulic pump on hydraulic tube tester

    NASA Astrophysics Data System (ADS)

    Zhao, Zhen; Jia, Mingxing; Wang, Fuli; Wang, Shu

    2009-07-01

    To ensure the safety, continuity of production, make a reasonable maintenance plan, save the cost of maintenance for hydraulic tube tester, it is needed to quickly identify an assignable cause of a fault. This paper is concerned with early fault diagnosis of hydraulic pump which are the heart of hydraulic tube tester. Considering that the signal of the hydraulic pump early fault is a periodic weak signal, an intermittent chaos, sliding window symbol sequence statistics-based method is proposed to detect the early fault of one single piston loose shoes of hydraulic pump on a hydraulic tube tester. The approach presented is based on the insight that the phase transition of chaos oscillator, for example, the Duffing oscillator, is very sensitive to a periodic weak signal having little angular frequency difference with the referential signal of the oscillator. While observing the intermittent chaos phenomenon through figure is not easy for computer, a sliding window symbol sequence statistics is developed to realize real-time computer observation of this phenomenon. Rather more, this paper takes a trick to decreasing the computational complexity of the sliding window symbol sequence statistics method, also analyzes the influences of different window size, depths of the symbol tree on the information entropy. At last, a control limit is introduced to realize automatic early fault alarm. The resultant approach is experimented with data simulated from an AMESim model of hydraulic tube tester. The results indicate that the proposed approach is capable of detecting the signal of hydraulic pump early fault on hydraulic tube tester.

  14. Model-based fault diagnosis in electric drives using machine learning

    Microsoft Academic Search

    Yi Lu Murphey; M. Abul Masrur; ZhiHang Chen; Baifang Zhang

    2006-01-01

    Electric motor and power electronics-based inverter are the major components in industrial and automotive electric drives. In this paper, we present a model-based fault diagnostics system developed using a machine learning technology for detecting and locating multiple classes of faults in an electric drive. Power electronics inverter can be considered to be the weakest link in such a system from

  15. An Automated Fault Diagnosis System Using Hierarchical Reasoning and Alarm Correlation

    Microsoft Academic Search

    C. S. Chao; D. L. Yang; A. C. Liu

    2001-01-01

    The increasing importance of computer networks in this information age demands a high level of network availability and reliability. As we become more dependent on networks in our so-called cyber-world, network faults and downtime become very costly. Sometimes, a slight fault may cause critical disruptions or remediless damages to the network while the network manager is lost among a large

  16. Real-time fault diagnosis using knowledge-based expert system

    Microsoft Academic Search

    Cen Nan; Faisal Khan; M. Tariq Iqbal

    2008-01-01

    Abnormal operating conditions (faults) cost process industry billons of dollars per year and can be prevented if they are predicted and controlled in advance. Advanced software applications, based on the expert system, has the potential to assist engineers in monitoring, detecting, and diagnosing abnormal conditions and thus providing safe guards against these unexpected process conditions. Abnormal operating conditions (faults) could

  17. Model-based fault Diagnosis for IEEE 802.11 wireless LANs

    Microsoft Academic Search

    Bo Yan; Guanling Chen

    2009-01-01

    The increasingly deployed IEEE 802.11 wireless LANs (WLANs) challenge traditional network management systems because of the shared open medium and the varying channel conditions. There needs to be an automated tool that can help diagnosing both malicious security faults and benign performance faults. It is often difficult, however, to identify the root causes since the manifesting anomalies from network measurements

  18. Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform without Slip Estimation

    Microsoft Academic Search

    Shahin Hedayati Kia; Humbero Henao; S. G.-A. Capolino

    2007-01-01

    The aim of this paper is to present a wavelet-based method for broken bar fault detection in induction machines. The frequency-domain methods which are commonly used need speed information or accurate slip estimation for frequency components localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any induction machine due to numerous previous investigations. The proposed

  19. Optimal design of the absolute positioning sensor for a high-speed maglev train and research on its fault diagnosis.

    PubMed

    Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge

    2012-01-01

    This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project. PMID:23112619

  20. Optimal Design of the Absolute Positioning Sensor for a High-Speed Maglev Train and Research on Its Fault Diagnosis

    PubMed Central

    Zhang, Dapeng; Long, Zhiqiang; Xue, Song; Zhang, Junge

    2012-01-01

    This paper studies an absolute positioning sensor for a high-speed maglev train and its fault diagnosis method. The absolute positioning sensor is an important sensor for the high-speed maglev train to accomplish its synchronous traction. It is used to calibrate the error of the relative positioning sensor which is used to provide the magnetic phase signal. On the basis of the analysis for the principle of the absolute positioning sensor, the paper describes the design of the sending and receiving coils and realizes the hardware and the software for the sensor. In order to enhance the reliability of the sensor, a support vector machine is used to recognize the fault characters, and the signal flow method is used to locate the faulty parts. The diagnosis information not only can be sent to an upper center control computer to evaluate the reliability of the sensors, but also can realize on-line diagnosis for debugging and the quick detection when the maglev train is off-line. The absolute positioning sensor we study has been used in the actual project. PMID:23112619

  1. Robust Condition Monitoring and Fault Diagnosis of Variable Speed Induction Motor Drives 

    E-print Network

    Choi, Seungdeog

    2012-02-14

    has been another concern. In this work, the reliability of an electric motor diagnosis signal processing algorithm itself is studied in detail under harsh industrial conditions. Reliability and robustness of the diagnosis has especially been...

  2. Application of fuzzy neural network to the nuclear power plant in process fault diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Yong-Kuo; Xia, Hong; Xie, Chun-Li

    2005-03-01

    The fuzzy logic and neural networks are combined in this paper, setting up the fuzzy neural network (FNN); meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN. The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system’s validity, the inverted U-tube break accident of steam generator is used as an example and many simulation experiments are performed. The test result shows that the FNN can identify the fault.

  3. Distance Rejection in a Bayesian Network for Fault Diagnosis of Industrial Systems

    E-print Network

    Paris-Sud XI, Université de

    . So, they include a lot of sensors. Consequently, an important amount of data can be obtained from. In section II, we introduce the classical method to diagnose faults with bayesian network classifiers

  4. MonoScope: Automating Network Faults Diagnosis Based on Active Measurements

    E-print Network

    Chang, Rocky Kow-Chuen

    of Computing§ School of Computer Noah's Ark Lab Shenzhen Research Institute National University of Huawei|csxluo|cskpmok|csweicli|csrchang} China edmond.chan@huawei.com @comp.polyu.edu.hk liuyujing@nudt.edu.cn Abstract--Network faults

  5. Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L. (Technical Monitor)

    2003-01-01

    In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.

  6. A review of rolling element bearing vibration detection, diagnosis and prognosis

    NASA Astrophysics Data System (ADS)

    Howard, Ian M.

    1994-10-01

    Rolling element bearings are among the most common components to be found in industrial rotating machinery. They are found in industries from agriculture to aerospace, in equipment as diverse as paper mill rollers to the Space Shuttle Main Engine Turbomachinery. There has been much written on the subject of bearing vibration monitoring over the last twenty five years. This report attempts to summarize the underlying science of rolling element bearings across these diverse applications from the point of view of machine condition monitoring using vibration analysis. The key factors which are addressed in this report include the underlying science of bearing vibration, bearing kinematics and dynamics, bearing life, vibration measurement, signal processing techniques and prognosis of bearing failure.

  7. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

    PubMed Central

    Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

    2015-01-01

    This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in ?? coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method. PMID:25970258

  8. Appropriate IMFs associated with cepstrum and envelope analysis for ball-bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Tsao, Wen-Chang; Pan, Min-Chun

    2014-03-01

    The traditional envelope analysis is an effective method for the fault detection of rolling bearings. However, all the resonant frequency bands must be examined during the bearing-fault detection process. To handle the above deficiency, this paper proposes using the empirical mode decomposition (EMD) to select a proper intrinsic mode function (IMF) for the subsequent detection tools; here both envelope analysis and cepstrum analysis are employed and compared. By virtue of the band-pass filtering nature of EMD, the resonant frequency bands of structure to be measured are captured in the IMFs. As impulses arising from rolling elements striking bearing faults modulate with structure resonance, proper IMFs potentially enable to characterize fault signatures. In the study, faulty ball bearings are used to justify the proposed method, and comparisons with the traditional envelope analysis are made. Post the use of IMFs highlighting faultybearing features, the performance of using envelope analysis and cepstrum analysis to single out bearing faults is objectively compared and addressed; it is noted that generally envelope analysis offers better performance.

  9. Fault dignosis of rolling bearing based on time domain parameters

    Microsoft Academic Search

    Jibin Chang; Taifu Li; Qiang Luo

    2010-01-01

    The rolling bearing is the common component in machinery. Its running state will influence the performance of the whole machine directly. In this paper we put forward a feature extraction method of fault diagnosis of rolling bearing. After the vibration signals of the rolling bearing are analysed and processed, the feature parameters which represent operating state of the rolling bearing

  10. Airdata sensor based position estimation and fault diagnosis in aerial refueling

    NASA Astrophysics Data System (ADS)

    Sevil, Hakki Erhan

    Aerial refueling is the process of transferring fuel from one aircraft (the tanker) to another (the receiver) during flight. In aerial refueling operations, the receiver aircraft is exposed to nonuniform wind field induced by tanker aircraft, and this nonuniform wind field leads to differences in readings of airdata sensors placed at different locations on the receiver aircraft. There are advantages and disadvantages of this phenomenon. As an advantage, it is used as a mechanism to estimate relative position of the receiver aircraft inside the nonuniform wind field behind the tanker. Using the difference in the measurements from multiple identical sensors, a model of the nonuniform wind field that is organized as maps of the airspeed, side slip angle and angle of attack as functions of the relative position is prepared. Then, using the developed algorithms, preformed maps and instant sensor readings, the relative position receiver aircraft is determined. The disadvantage of the phenomenon is that the differences in readings of airdata sensors cause false fault detections in a redundant-sensor-based Fault Detection and Isolation (FDI) system developed based on the assumption of identical sensor readings from three airdata sensors. Such FDI algorithm successfully performs detection and isolation of sensor faults when the receiver aircraft flies solo or outside the wake of the tanker aircraft. However, the FDI algorithm yields false fault detection when the receiver aircraft enters the tanker's wake. This problem can be eliminated by modifying the FDI algorithm. For the robustness, the expected values of the sensor measurements are incorporated in the FDI algorithm, instead of the assumption of identical measurements from the sensors. The expected values, which depend on the position of the receiver relative to the tanker, are obtained from the maps of the nonuniform wind field as functions of the relative position. The new robust FDI detects and isolates sensor faults, as well as it eliminates the false fault detection in the nonuniform wind field induced by the tanker aircraft.

  11. Nonlinear Estimation for Model Based Fault Diagnosis of Nonlinear Chemical Systems 

    E-print Network

    Qu, Chunyan

    2011-02-22

    such as expert systems or fuzzy logic and process history-based methods such as qualitative trend analysis(QTA) or principle component analysis (PCA) also receive a high level of attention. In the area of quantitative model-based methods, first principles model.... Another important work is the use of fuzzy set theory to improve fault resolution in SDG models by [122]. Later Shih and Lee [123] [124] discussed the use of fuzzy logic principles with SDGs for the removal of spurious solutions. Fault trees have also been...

  12. Electrical Motor Current Signal Analysis using a Dynamic Time Warping Method for Fault Diagnosis

    Microsoft Academic Search

    D. Zhen; A. Alibarbar; X. Zhou; F. Gu; A. D. Ball

    2011-01-01

    This paper presents the analysis of phase current signals to identify and quantify common faults from an electrical motor based on dynamic time warping (DTW) algorithm. In condition monitoring, measurements are often taken when the motor undertakes varying loads and speeds. The signals acquired in these conditions show similar profiles but have phase shifts, which do not line up in

  13. Feature Selection for Fault Diagnosis Using Fuzzy-ARTMAP Classification and Conflict Intersection

    Microsoft Academic Search

    Mourad Benkaci; Andrei Doncescu; Bruno Jammes

    2010-01-01

    In automotive industry the safety of cars behavior is monitoring using computers. The information acquired on the bus communication is often redundant and not relevant. Therefore in the case of faults detection and isolation based on machine learning model, we need to reduce the number of variables according with their relevance and allowing taking decision in real time. In this

  14. Induction machine stator fault on-line diagnosis based on LabVIEW environment

    Microsoft Academic Search

    L Collamatit; F. Filippetti; G. Franceschini; S. Pirani; C. Tassoni

    1996-01-01

    The benefits of machine condition monitoring have been widely recognized as superior with respect to other alternative maintenance approaches. Condition monitoring is an operational strategy for machine integrity assessment, fault identification and life extension. The cost-benefit ratio will be reduced in progress owing to the commercial diagnostic environment availability. This paper presents the implementation of a diagnostic procedure to detect

  15. Diagnosis of process valve actuator faults using a multilayer neural network

    Microsoft Academic Search

    M. Karpenko; N. Sepehri; D. Scuse

    2003-01-01

    This paper investigates the ability of a multilayer neural network to diagnose actuator faults in a Fisher-Rosemount 667 process control valve. A software package that comes with the valve is used to obtain experimental figures of merit related to the position response of the valve given a step command. The particular values of the dead time, peak time, percent overshoot,

  16. UML Specification of a Generic Model for Fault Diagnosis of Telecommunication Networks

    Microsoft Academic Search

    Armen Aghasaryan; Claude Jard; Julien Thomas

    2004-01-01

    This document presents a generic model capturing the essential structural and behavioral characteristics of network components in the light of fault management. The generic model is described by means of UML notations, and can be compiled to obtain rules for a Viterbi distributed diagnoser. This paper presents the results of the continued efforts on generic modeling initiated within the Magda

  17. Sensor fault diagnosis based on discrete wavelet transform and BP neural network

    Microsoft Academic Search

    Quan Liu; Xuemei Jiang

    2005-01-01

    Sensor technology is one of three major pillars of the modern information technology. With the extensive application of sensor, the dependability of the sensor is paid more and more attention. The development of sensor faults diagnose technology offers strong guarantee for using the sensor reliably. In this paper, the application of combining the wavelet and BP neural networks to sensors

  18. A LEARNING BASED STOCHASTIC APPROACH FOR FAULT DIAGNOSIS IN A CONTINUOUS STIRRED TANK REACTOR

    Microsoft Academic Search

    Yskandar HAMAM

    2000-01-01

    Many approaches have been developed to detect and diag- nose the different types of faults that may occur in a complex process. Most of these approaches have traditionally been based on linear modeling techniques, which restricts the type of practical situations that can be modeled. Recently, many learning based non linear modeling using neural and other on-line approximation models have

  19. Decoupling features and virtual sensors for diagnosis of faults in vapor compression air conditioners

    Microsoft Academic Search

    Haorong Li; James E. Braun

    2007-01-01

    This paper describes the development and evaluation of features and virtual sensors that form the basis of a methodology for detecting and diagnosing multiple-simultaneous faults in vapor compression air conditioning equipment. The features were developed based upon a physical understanding of the system, cost considerations, and heuristics derived from experimental data and modeling results. Virtual sensors were developed in order

  20. A Hybrid Rule-Based/Case-Based Reasoning Approach for Service Fault Diagnosis

    E-print Network

    affecting the service quality and a reasonable balance between the fault management effort and the costs of network and systems management. Our hybrid architecture consists of a rule-based reasoning module, whose in opposition to network and systems management. While the events that are encountered in network and systems

  1. Data-driven fault diagnosis in a hybrid electric vehicle regenerative braking system

    Microsoft Academic Search

    Chaitanya Sankavaram; Bharath Pattipati; Krishna Pattipati; Yilu Zhang; Mark Howell; Mutasim Salman

    2012-01-01

    Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. In this paper, we discuss a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The process involves data reduction techniques, exemplified by multi-way partial least

  2. COSMAD: a Scilab toolbox for output-only modal analysis and diagnosis of vibrating structures

    Microsoft Academic Search

    Maurice Goursat; Laurent Mevel

    2004-01-01

    Modal analysis of vibrating structures is a usual technique for design and monitoring in many industrial sectors: car manufacturing, aerospace, civil structures. We present COSMAD a software environment for in-operation situation without any measured or controlled input. COSMAD is an identification and detection Scilab toolbox. It covers modal identification with visual inspection of the results via a GUI or fully

  3. Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps

    NASA Astrophysics Data System (ADS)

    Meltzer, G.; Dien, Nguyen Phong

    2004-09-01

    This paper will attempt to analyse the effectiveness of the Continuous Wavelet Transform in vibro-acoustical diagnostics of gearboxes operating under non-stationary rotational speed. For this, a simple PC-software program for signal processing and extraction of diagnostic features was developed and tested. The objective of the program test is fault-detection, localisation, and assessment at helical spur gears. This includes improvements of the visual estimation of the WT-plots, especially by the task-specific balance of time- and frequency-resolution and by the display of the wavelet amplitude versus the rotational angle in polar coordinates. The following examples demonstrate the improvements in the detection of gear faults.

  4. Parameter estimation for uncertain systems based on fault diagnosis using Takagi-Sugeno model.

    PubMed

    Nagy-Kiss, A M; Schutz, G; Ragot, J

    2015-05-01

    The paper addresses a systematic procedure to deal with state and parameter uncertainty estimation for nonlinear time-varying systems. A robust observer with respect to states, inputs and perturbations is designed, using a Takagi-Sugeno (T-S) approach with unknown premise variables. Tools of the linear automatic to the nonlinear systems are applied, using the Linear Matrix Inequalities optimization. The observer estimates the uncertainties, the states and minimizes the effect of external disturbances on the estimation error. The uncertainties are modelled in a polynomial way which allows considering the uncertainty estimation as a fault detection problem. The residual sensitivity to faults while maintaining robustness according to a noise signal is handled by H?/H- approach. The method performance is illustrated using the three-tank system. PMID:25677711

  5. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    Microsoft Academic Search

    D Zhen; H L Zhao; F Gu; A D Ball

    2012-01-01

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it

  6. Application of support vector machines for fault diagnosis in power transmission system

    Microsoft Academic Search

    B. Ravikumar; D. Thukaram; H. P. Khincha

    2008-01-01

    Post-fault studies of recent major power failures around the world reveal that mal- operation and\\/or improper co-ordination of protection system were responsible to some extent. When a major power disturbance occurs, protection and control action are required to stop the power system degradation, restore the system to a normal state and minimise the impact of the dis- turbance. However, this

  7. Detection and Diagnosis of Incipient Faults in Heavy-Duty Diesel Engines

    Microsoft Academic Search

    Ian Morgan; Honghai Liu; Bernardo Tormos; Antonio Sala

    2010-01-01

    This paper proposes a new methodology for detecting and diagnosing faults found in heavy-duty diesel engines based upon spectrometric analysis of lubrication samples and is compared against a conventional method, the redline limits, which is utilized in a number of major laboratories in the U.K. and across Europe. The proposed method applies computational power to a well-known maintenance technique and

  8. Analytic redundancy for on-line fault diagnosis in a nuclear reactor

    Microsoft Academic Search

    Asok Ray; Mukund Desai; John Deyst; Robert Geiger

    1983-01-01

    A computer-aided diagnostic technique has been applied to on-line signal validation in an operating nuclear reactor. To avoid installation of additional redundant sensors for the sole purpose of fault isolation, a real-time model of nuclear instrumentation and the thermal-hydraulic process in the primary coolant loop was developed and experimentally validated. The model provides analytically redundant information sufficient for isolation of

  9. Application of heuristic search and information theory to sequential fault diagnosis

    Microsoft Academic Search

    K. R. Pattipati; M. G. Alexandridis

    1990-01-01

    The problem of constructing optimal and near-optimal test sequences to diagnose permanent faults in electronic and electromechanical systems is considered. The test sequencing problem is formulated as an optimal binary AND\\/OR decision tree construction problem, whose solution is known to be NP-complete. The approach used is based on integrated concepts from information theory and heuristic AND\\/OR graph search methods to

  10. Diagnosis of Broken-Bar Fault in Induction Machines Using Discrete Wavelet Transform Without Slip Estimation

    Microsoft Academic Search

    Shahin Hedayati Kia; Humberto Henao; GÉrard-AndrÉ Capolino

    2009-01-01

    The aim of this paper is to present a wavelet-based method for broken-bar detection in squirrel-cage induction machines. The frequency-domain methods, which are commonly used, need speed information or accurate slip estimation for frequency-component localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any squirrel-cage induction machine due to numerous previous investigations. The proposed approach

  11. A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor

    NASA Astrophysics Data System (ADS)

    Shi, Pu; Chen, Zheng; Vagapov, Yuriy; Zouaoui, Zoubir

    2014-01-01

    This paper proposes a new induction motor broken bar fault extent diagnostic approach under varying load conditions based on wavelet coefficients of stator current in a specific frequency band. In this paper, winding function approach (WFA) is used to develop a mathematical model to provide indication references for parameters under different load levels and different fault cases. It is shown that rise of number of broken bars and load levels increases amplitude of the particular side band components of the stator currents in faulty case. Stator current, rotor speed and torque are used to demonstrate the relationship between these parameters and broken rotor bar severity. An induction motor with 1, 2 and 3 broken bars and the motor with 3 broken bars in experiment at no-load, 50% and 100% load are investigated. A novel criterion is then developed to assess rotor fault severity based on the stator current and the rotor speed. Simulations and experimental results confirm the validity of the proposed approach.

  12. Application of Composite Dictionary Multi-Atom Matching in Gear Fault Diagnosis

    PubMed Central

    Cui, Lingli; Kang, Chenhui; Wang, Huaqing; Chen, Peng

    2011-01-01

    The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results. PMID:22163938

  13. Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

    NASA Astrophysics Data System (ADS)

    Tran, Van Tung; Yang, Bo-Suk; Gu, Fengshou; Ball, Andrew

    2013-07-01

    In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD. At each decomposed level, the IMF is expanded and fused with the residue by gray-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM. The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery fault diagnosis.

  14. IEEE TRANSACTIONS ON COMPUTERAIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, VOL. 21, NO. 11, NOVEMBER 2002 1 Testing and Diagnosis of Interconnect Faults in

    E-print Network

    Tessier, Russell

    , NOVEMBER 2002 1 Testing and Diagnosis of Interconnect Faults in Cluster­Based FPGA Architectures I. G. Harris and R. Tessier Abstract---As IC densities are increasing, cluster­based field pro­ grammable gate arrays (FPGA) architectures are becoming the architecture of choice for major FPGA manufacturers

  15. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

    NASA Astrophysics Data System (ADS)

    Jegadeeshwaran, R.; Sugumaran, V.

    2015-02-01

    Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

  16. Optimization of Fault Detection/Diagnosis Model for Thermal Storage System Using AIC 

    E-print Network

    Pan, S.; Zheng, M.; Nakahara, N.

    2006-01-01

    of the event. In addition, human learning, recognition, and optimal judgment process of any event can be simulated by optimizing the most effective pa-rameters and their numbers for detection and diagnosis by the use of variable selection method. In previous...

  17. Fault diagnosis of roller bearing feature subset select based on greedy algorithm

    Microsoft Academic Search

    Min yong; Guo yi-nan; Yan Jun-rong

    2010-01-01

    Because RST's ability of data reduction, feature subset selection was translated into the process of data reduction. The condition attributes and decidation attributes of the diagnosis system were reducted, and we received the best training swatch which were cleared up the information of redundance and repetition. Greedy algorithm is a method of discretion and a algorithm of attribute reduction. In

  18. An efficient Novelty Detector for online fault diagnosis based on Least Squares Support Vector Machines

    Microsoft Academic Search

    Taimoor S. Khawaja; George Georgoulas; George Vachtsevanos

    2008-01-01

    A paradigm shift in the standard operating procedures (SOP) is underway in the reliability and health management industry. As the community transitions from traditional preventive maintenance procedures to modern predictive or health-based management systems, areas such as efficient online monitoring and diagnosis schemes based on real-time observations have emerged as key research subjects for engineers. Most diagnostic systems require data

  19. Recent developments of induction motor drives fault diagnosis using AI techniques

    Microsoft Academic Search

    Fiorenzo Filippetti; Giovanni Franceschini; Carla Tassoni; Peter Vas

    2000-01-01

    This paper presents a review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). It covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques. The application of genetic algorithms is considered as well.

  20. A fault detection and diagnosis module for oil production plants in offshore platforms

    Microsoft Academic Search

    E. Kaszkurewicz; A. Bhaya; N. F. F. Ebecken

    1997-01-01

    This paper describes an expert system for process supervision and diagnosis of an offshore oil product plant. The expert system contains main modules with detection, diagnostic and advisory functions. The implementation issues of the expert system using the Gensym G2 software environment are discussed. The system was developed on board the Petrobras XXIV platform in the Campos Basin, Rio de