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1

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

E-print Network

A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and Current Signatures the powerful capability of vibration analysis in the bearing point-defect fault diagnosis under stationary operation. The current analysis showed a subtle capability in diagnosis of point- defect faults depending

Yang, Zhenyu

2

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

Microsoft Academic Search

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

Jian-Da Wu; Chao-Qin Chuang

2005-01-01

3

A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals  

Microsoft Academic Search

This paper introduces a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. First features are extracted from amplitude demodulated vibration signals obtained from both normal and faulty bearings. The features are based on the reflection coefficients of the polynomial transfer function of the autoregressive model of the vibration signal. These features are

Hasan OCAK; Kenneth A. LOPARO

2001-01-01

4

Application of Dempster Shafer theory in fault diagnosis of induction motors using vibration and current signals  

NASA Astrophysics Data System (ADS)

This paper presents an approach for the fault diagnosis in induction motors by using Dempster-Shafer theory. Features are extracted from motor stator current and vibration signals and with reducing data transfers. The technique makes it possible for on-line application. Neural network is trained and tested by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electrical and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real-time applications.

Yang, Bo-Suk; Kim, Kwang Jin

2006-02-01

5

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

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

6

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

PubMed Central

Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods. PMID:24379045

He, Qingbo; Wang, Xiangxiang; Zhou, Qiang

2014-01-01

7

Diagnosis of Centrifugal Pump Faults Using Vibration Methods  

Microsoft Academic Search

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

A Albraik; F Althobiani; F Gu; A Ball

2012-01-01

8

Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis  

NASA Astrophysics Data System (ADS)

Rolling element bearing faults are among the main causes of breakdown in rotating machines. In this paper, a rolling bearing fault model is proposed based on the dynamic load analysis of a rotor-bearing system. The rotor impact factor is taken into consideration in the rolling bearing fault signal model. The defect load on the surface of the bearing is divided into two parts, the alternate load and the determinate load. The vibration response of the proposed fault signal model is investigated and the fault signal calculating equation is derived through dynamic and kinematic analysis. Outer race and inner race fault simulations are realized in the paper. The simulation process includes consideration of several parameters, such as the gravity of the rotor-bearing system, the imbalance of the rotor, and the location of the defect on the surface. The simulation results show that different amplitude contributions of the alternate load and determinate load will cause different envelope spectrum expressions. The rotating frequency sidebands will occur in the envelope spectrum in addition to the fault characteristic frequency. This appearance of sidebands will increase the difficulty of fault recognition in intelligent fault diagnosis. The experiments given in the paper have successfully verified the proposed signal model simulation results. The test rig design of the rotor bearing system simulated several operating conditions: (1) rotor bearing only; (2) rotor bearing with loader added; (3) rotor bearing with loader and rotor disk; and (4) bearing fault simulation without rotor influence. The results of the experiments have verified that the proposed rolling bearing signal model is important to the rolling bearing fault diagnosis of rotor-bearing systems.

Cong, Feiyun; Chen, Jin; Dong, Guangming; Pecht, Michael

2013-04-01

9

Fault diagnosis  

NASA Technical Reports Server (NTRS)

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.

Abbott, Kathy

1990-01-01

10

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

PubMed

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

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

2014-01-01

11

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

E-print Network

to develop fault diagnostic classifiers. It is shown that, following training on examples of normal operation normal and four fault conditions were obtained by physically inducing subtle faults in a diesel engineAcoustic Emission, Cylinder Pressure and Vibration: A Multisensor Approach to Robust Fault

Sharkey, Amanda

12

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

Microsoft Academic Search

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

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

2010-01-01

13

Software Fault Diagnosis Peter Zoeteweij  

E-print Network

Software Fault Diagnosis Peter Zoeteweij , Rui Abreu, and Arjan J.C. van Gemund Embedded Software systems. This tutorial paper aims to give an overview of automated diagnosis applied to software faults existing diagnosis / debugging systems that apply SFL, and other approaches to software fault diagnosis. We

Zoeteweij, Peter

14

Bearing fault diagnosis based on rough set  

Microsoft Academic Search

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

Chen Xin; Yuhua Chen; Guofeng Wang; Hu Dong

2010-01-01

15

APPLICATION OF WAVELETS TO GEARBOX VIBRATION SIGNALS FOR FAULT DETECTION  

Microsoft Academic Search

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

W. J. Wang; P. D. McFadden

1996-01-01

16

Hierarchical Approach to Fault Diagnosis Master's Thesis  

E-print Network

Hierarchical Approach to Fault Diagnosis Master's Thesis Alexander Feldman November 14, 2004 for listening to my problems of hierarchical fault diagnosis. Thanks to Leo Breebaart, also from Science, Fault Diagnosis, Hierarchy, Partitioning, Structure, Propo- sitional Model, Backtracking #12

van Gemund, Arjan J.C.

17

Isolability of faults in sensor fault diagnosis  

NASA Astrophysics Data System (ADS)

A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.

Sharifi, Reza; Langari, Reza

2011-10-01

18

Fault diagnosis of analog circuits  

SciTech Connect

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. Theory and algorithms that are associated with these approaches are reviewed and problems of their practical application are identified. Associated with the fault dictionary approach we consider fault dictionary construction techniques, methods of optimum measurement selection, different fault isolation criteria, and efficient fault simulation techniques. Parameter identification techniques that either utilize linear or nonlinear systems of equations to identify all network elements are examined very thoroughly. Under fault verification techniques we discuss node-fault diagnosis, branch-fault diagnosis, subnetwork testability conditions as well as combinatorial techniques, the failure bound technique, and the network decomposition technique. For the approximation approach we consider probabilistic methods and optimization-based methods. The artificial intelligence technique and the different measures of testability are also considered. The main features of the techniques considered are summarized in a comparative table. An extensive, but not exhaustive, bibliography is provided.

Bandler, J.W.; Salama, A.E.

1985-08-01

19

Fault diagnosis method for smart substation  

Microsoft Academic Search

This paper proposed a hierarchical model for smart substation fault diagnosis. This fault diagnosis system gets fault information from SCADA and fault information system. The information from SCADA, including network topology and switch state, is modeled based on IEC61970-CIM. The protection information from the fault information system is modelled based on IEC61850, then encapsulated CIM model. When a fault happens,

Zhanjun Gao; Qing Chen; Zhaofei Li

2011-01-01

20

Fault diagnosis of power systems  

SciTech Connect

Fault diagnosis of power systems plays a crucial role in power system monitoring and control that ensures stable supply of electrical power to consumers. In the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires judgment of complex conditions at various levels. For this reason, research into application of knowledge-based systems go an early start and reports of such systems have appeared in may papers. In this paper, these systems are classified by the method of inference utilized in the knowledge-based systems for fault diagnosis of power systems. The characteristics of each class and corresponding issues as well as the state-of-the-art techniques for improving their performance are presented. Additional topics covered are user interfaces, interfaces with energy management systems (EMS's), and expert system development tools for fault diagnosis. Results and evaluation of actual operation in the field are also discussed. Knowledge-based fault diagnosis of power systems will continue to disseminate.

Sekine, Y. (Dept. of Electrical Engineering, Univ. of Tokyo, Tokyo 133 (JP)); Akimoto, Y. (Tokyo Electric Power Co., Tokyo 104 (JP)); Kunugi, M. (Toshiba Corp., Tokyo 183 (JP))

1992-05-01

21

Bearing fault diagnosis based on wavelet transform and fuzzy inference  

Microsoft Academic Search

This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An

Xinsheng Lou; Kenneth A Loparo

2004-01-01

22

Diagnosis of Cyber Attacks and Faults in Power Networks by Using State Fault Diagnosis Matrix  

E-print Network

Diagnosis of Cyber Attacks and Faults in Power Networks by Using State Fault Diagnosis Matrix Y a diagnosis method for the power grid by using state and output fault diagnosis matrixes which are composed of the proposed method. Key Words: Power Network, Cyber Attack Diagnosis, Fault Diagnosis Matrix, Kalman Filter 1

23

Applications of Fault Detection in Vibrating Structures  

NASA Technical Reports Server (NTRS)

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.

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

2012-01-01

24

Fault diagnosis based on wavelet packet energy and PNN analysis method for rolling bearing  

Microsoft Academic Search

A combined approach based on wavelet packet energy and probabilistic neural network (WPE-PNN) is presented to diagnose faults in the rolling bearing vibration signal research. Firstly wavelet packet is used to decompose rolling bearing vibration signals into three-layer, and extract the energy characteristics. Then PNN is proposed to diagnose faults. Finally, remote fault diagnosis is realized by virtual instrument technology.

Jingyi Zhang; Lan Wang; Meichen Zhu; Yuanyuan Zhu; Qing Yang

2012-01-01

25

Fault Diagnosis of Steam Turbine-Generator Sets Using Evolutionary Based Support Vector Machine  

Microsoft Academic Search

This paper presents particle swarm optimization (PSO)-based support vector machine (SVM) to extract the optimal support vector from database for vibration fault diagnosis of steam turbine-generator sets (STGS). In this paper, the SVM is used to construct the vibration fault diagnosis model and the proposed PSO is then adopted to determine automatically the optimal parameters in the SVM. Test results

Huo-Ching Sun; Yann-Chang Huang

2012-01-01

26

Automated Fault Diagnosis at Philips Medical Systems  

E-print Network

Automated Fault Diagnosis at Philips Medical Systems A Model-Based Approach Master's Thesis W.M. Lindhoud #12;#12;Automated Fault Diagnosis at Philips Medical Systems A Model-Based Approach THESIS-Ray System, © Philips #12;Automated Fault Diagnosis at Philips Medical Systems A Model-Based Approach Author

van Gemund, Arjan J.C.

27

Intelligent Fault Diagnosis in Computer Networks  

E-print Network

Intelligent Fault Diagnosis in Computer Networks Xin Hu Kongens Lyngby 2007 IMM-THESIS-2007-49 #12 As the computer networks become larger and more complicated, fault diagnosis becomes a difficult task for network the root cause is time-consuming and error-prone. Therefore, auto- mated fault diagnosis in computer

28

Hierarchically Structured Inductive Learning for Fault Diagnosis  

E-print Network

Hierarchically Structured Inductive Learning for Fault Diagnosis Michael G. Madden MCS presents a new methodology for fault diagnosis, based on the natural hierarchy of components and sub learning tasks are discussed. In the second section, the author's fault diagnosis system, DE/ IFT

Madden, Michael

29

Online Diagnosis of Hard Faults in Microprocessors  

E-print Network

Online Diagnosis of Hard Faults in Microprocessors FRED A. BOWER Duke University and IBM Systems Paper: Fred A. Bower, Daniel J. Sorin, and Sule Ozev. "A Mechanism for Online Diagnosis of Hard Faults. Online diagnosis of hard faults in microprocessors. Architec. Code Optim. 4, 2, Article 8 (June 2007),

Sorin, Daniel J.

30

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

E-print Network

Fault tree analysis is widely used in industry for fault diagnosis. The diagnosis of incipient in the case of soft faults. This paper presents comprehensive results describing the diagnosis of incipient or `soft' faults is considerably more difficult than that of `hard' faults, which is the case considered

Madden, Michael

31

Fault diagnosis in computing networks  

SciTech Connect

This dissertation is concerned with system-level fault diagnosis, which is the problem of identifying faulty units (components) in computer systems. For the purpose of generality, a computer system, regardless of whether it is a computer network, multiprocessor computer, or distributed system, is considered to be a collection of units. Each unit is tested by other units within the system and the results of the tests are collected in order to identify the faulty units. The units and test assignments are represented by a directed graph, which is called a testing graph. In Chapter 1, motivations for studying the Theory of system-level fault diagnosis is explained. Chapter 2 describes the various models that are used to represent test assignments and the implications of test results, and some of the relevant previous research is discussed. Chapter 3 addresses the problem of determining the diagnosability number (t) of a system's testing graph, which is the maximum number of faulty units that the system can tolerate and remain diagnosable. For previous results, it is assumed that there is a central controller that collects the test results and makes the diagnosis. In Chapters 4 and 5 the author considers distributed diagnosis applicable to systems that do not have a central controller. In distributed diagnosis, each unit in the system arrives at its own diagnosis.

Kreutzer, S.E.

1986-01-01

32

Fault-Trajectory Approach for Fault Diagnosis on Analog Circuits  

E-print Network

This issue discusses the fault-trajectory approach suitability for fault diagnosis on analog networks. Recent works have shown promising results concerning a method based on this concept for ATPG for diagnosing faults on analog networks. Such method relies on evolutionary techniques, where a generic algorithm (GA) is coded to generate a set of optimum frequencies capable to disclose faults.

Savioli, Carlos Eduardo; Calvano, Jose Vicente; Filho, Antonio Carneiro De Mesquita

2011-01-01

33

Fault Diagnosis with Dynamic Observers  

E-print Network

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...

Cassez, Franck

2010-01-01

34

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

Microsoft Academic Search

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

Jian-Da Wu; Jien-Chen Chen

2006-01-01

35

DIPLOMARBEIT Fault Injection for Diagnosis and Maintenance  

E-print Network

DIPLOMARBEIT Fault Injection for Diagnosis and Maintenance in the Time-Triggered Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . #12;#12;Fault Injection for Diagnosis and Maintenance in the Time-Triggered Architecture view over the system, and analysis in order to assess the health state of the system. A fault injection

36

Bayesian based fault diagnosis: application to an electrical motor  

E-print Network

Bayesian based fault diagnosis: application to an electrical motor A. Mechraoui , K. Medjaher , N.fr) Abstract: In the literature, several fault diagnosis methods, qualitative as well as quantitative. Keywords: Diagnosis, Fault isolation, Bayesian networks, Inference, Probabilities 1. INTRODUCTION Fault

Paris-Sud XI, Université de

37

Stator current demodulation for induction machine rotor faults diagnosis  

E-print Network

Stator current demodulation for induction machine rotor faults diagnosis El Houssin El Bouchikhi faults detection and diagnosis. The demodulation techniques can be classified into mono faults detection. Keywords--Induction machine; eccentricity faults; broken ro- tor bars; diagnosis

Boyer, Edmond

38

A new intelligent hierarchical fault diagnosis system  

SciTech Connect

As a part of a substation-level decision support system, a new intelligent Hierarchical Fault Diagnosis System for on-line fault diagnosis is presented in this paper. The proposed diagnosis system divides the fault diagnosis process into two phases. Using time-stamped information of relays and breakers, phase 1 identifies the possible fault sections through the Group Method of Data Handling (GMDH) networks, and phase 2 recognizes the types and detailed situations of the faults identified in phase 1 by using a fast bit-operation logical inference mechanism. The diagnosis system has been practically verified by testing on a typical Taiwan power secondary transmission system. Test results show that rapid and accurate diagnosis can be obtained with flexibility and portability for fault diagnosis purpose of diverse substations.

Huang, Y.C.; Huang, C.L. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Dept. of Electrical Engineering] [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Dept. of Electrical Engineering; Yang, H.T. [Chung Yuan Christian Univ., Chung-Li (Taiwan, Province of China). Dept. of Electrical Engineering] [Chung Yuan Christian Univ., Chung-Li (Taiwan, Province of China). Dept. of Electrical Engineering

1997-02-01

39

Rolling element bearing fault diagnosis based on support vector machine  

Microsoft Academic Search

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

Hong Zheng; Lei Zhou

2012-01-01

40

New Informative Features for Fault Diagnosis of Industrial Systems by  

E-print Network

New Informative Features for Fault Diagnosis of Industrial Systems by Supervised Classification for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classification the misclassification rate. Keywords: Fault Diagnosis, Supervised Classification, Bayesian Networks 1. INTRODUCTION

Paris-Sud XI, Université de

41

A fault diagnosis method for rolling bearing based on empirical mode decomposition and homomorphic filtering demodulation  

Microsoft Academic Search

A new fault diagnosis method based on empirical mode decomposition (EMD) and homomorphic filtering demodulation is proposed for rolling bearing. The vibration signal of fault rolling bearing is decomposed into a series of intrinsic mode functions (IMFs) by EMD, then extract the envelopes from the outstanding IMFs with various fault characteristic information by homomorphic filtering demodulation and Hilbert envelope demodulation,

Junfa Leng; Shuangxi Jing; Wei Hua

2010-01-01

42

Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming  

Microsoft Academic Search

This paper proposes a fault diagnosis method for plant machinery in an unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP). IPS is used to extract feature frequencies of each machine state from measured vibration signals for distinguishing faults by relative crossing information. Excellent symptom parameters for detecting faults are automatically generated by the GP. The excellent

Peng Chen; Masatoshi Taniguchi; Toshio Toyota; Zhengja He

2005-01-01

43

Neural network parameter optimization and fault diagnosis based on orthogonal experiments  

Microsoft Academic Search

The wavelet analysis and neural network for fault diagnosis system in practice has become a hot research topic in the fields of pattern recognition system in recent years. Acoustic emission technology is used for vibrating screen's fault diagnosis in this paper. The energy feature vectors of signals extracted by use of wavelet packet analysis is regarded as neural network input

Li Zigui; Yan Bijuan

2010-01-01

44

Training for Skill in Fault Diagnosis  

ERIC Educational Resources Information Center

The Knitting, Lace and Net Industry Training Board has developed a training innovation called fault diagnosis training. The entire training process concentrates on teaching based on the experiences of troubleshooters or any other employees whose main tasks involve fault diagnosis and rectification. (Author/DS)

Turner, J. D.

1974-01-01

45

Neural networks in process fault diagnosis  

Microsoft Academic Search

Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach. A number of possible neural network architectures for fault diagnosis are studied. The multilayer perceptron network with a hyperbolic tangent as the nonlinear element seems best suited for the task.

Timo Sorsa; Heikki N. Koivo; Hannu Koivisto

1991-01-01

46

Fault Diagnosis and Logic Debugging Using Boolean Satisfiability  

E-print Network

1 Fault Diagnosis and Logic Debugging Using Boolean Satisfiability Alexander Smith, Student Member proposes a novel Boolean satisfiability-based method for multiple fault diagnosis and multiple design error-- VLSI, diagnosis, verification, Boolean satisfia- bility, debugging, faults, design errors I

Viglas, Anastasios

47

Fault diagnosis using substation computer  

SciTech Connect

A number of substation integrated control and protection systems (ICPS) are being developed around the world, where the protective relaying, control, and monitoring functions of a substation are implemented using microprocessors. In this design, conventional relays and control devices are replaced by clusters of microprocessors, interconnected by multiplexed digital communication channels using fibre optic, twisted wire pairs or coaxial cables. The ICPS incorporates enhanced functions of value to the utility and leads to further advancement of the automation of transmission substations. This paper presents an automated method of fault diagnosis which can be incorporated in the station computer of an integrated control and protection system. The effectiveness of this method is demonstrated using a transmission-level substation as an example.

Jeyasurya, B. (Indian Inst. of Tech., Bombay (India)); Venkata, S.S. (Washington Univ., Seattle, WA (USA). Dept. of Electrical Engineering); Vadari, S.V. (ESCA Corp., Bellevue, WA (USA)); Postforoosh, J. (T and D. Protection Group, Puget Sound Power and Light, Bellevue, WA (US))

1990-04-01

48

Diagnosis of Realistic Bridging Faults with Single Stuckat Information  

E-print Network

Diagnosis of Realistic Bridging Faults with Single Stuck­at Information Brian Chess David B. Lavo F that of traditional stuck­at diagnosis. 1 Introduction Accurate fault diagnosis of realistic defects is an in­ tegral faults [16]. However, most fault diagnosis tech­ niques use the single stuck­at fault model to diagnose

Larrabee, Tracy

49

Planetary gearbox fault diagnosis using an adaptive stochastic resonance method  

NASA Astrophysics Data System (ADS)

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

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

2013-07-01

50

Linear circuit fault diagnosis using neuromorphic analyzers  

Microsoft Academic Search

This paper presents a method of analog fault diagnosis using neural networks. The primary focus of the paper is to provide robust diagnosis using a simple mechanism for automatic test pattern generation while reducing test time. A new diagnosis framework consisting of a white noise generator and an artificial neural network for response analysis and classification is proposed. This approach

Robert Spina; Shambhu Upadhyaya

1997-01-01

51

Fault detection and diagnosis capabilities of test sequence selection  

E-print Network

Review Fault detection and diagnosis capabilities of test sequence selection methods based complete fault coverage. These seven methods are formally analysed for their fault diagnosis capabilities of the test sequences they select, and their fault detection and diagnosis capabilities. Keywords: fault

Thulsiraman, Krishnaiyan

52

Optimal residual decoupling for robust fault diagnosis  

Microsoft Academic Search

his paper deals with residual generation for the diagnosis of faults in the presence of disturbances. The emphasis is on modelling errors, represented as multiplicative disturbances, and on parametric faults. These are both characterized as discrepancies in a set of underlying parameters. The residuals are obtained using parity equations. To address the situation when the number of uncertain parameters is

JANOS J. GERTLER; MOID M. KUNWER

1995-01-01

53

Diagnosis of Multiple Faults: A Sensitivity Analysis David Heckerman  

E-print Network

Diagnosis of Multiple Faults: A Sensitivity Analysis David Heckerman Microsoft Research Center., 1986). Several years ago, researchers developed an alternative model of multiple-fault diagnosis INTRODUCTION The development of practical models and inference al- gorithms for diagnosing multiple faults

Heckerman, David

54

Probabilistic Fault Diagnosis in the MAGNETO Autonomic Control Loop  

E-print Network

Probabilistic Fault Diagnosis in the MAGNETO Autonomic Control Loop Pablo Arozarena1 , Raquel focuses on the probabilistic fault diagnosis functionality developed in the MAGNETO project, which enables to problems disrupting the delivery of a given service. 2. Fault Diagnosis in MAGNETO Fault diagnosis

Paris-Sud XI, Université de

55

Cooperative Human-Machine Fault Diagnosis  

NASA Astrophysics Data System (ADS)

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.

Remington, Roger; Palmer, Everett

1987-02-01

56

Cooperative human-machine fault diagnosis  

NASA Technical Reports Server (NTRS)

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.

Remington, Roger; Palmer, Everett

1987-01-01

57

Fault Detection and Automated Fault Diagnosis for Embedded Integrated Electrical Passives  

E-print Network

Fault Detection and Automated Fault Diagnosis for Embedded Integrated Electrical Passives Heebyung and automated fault diagnosis us- ing pole zero analysis of embedded integrated pas- sive. For pole zero-matching algorithm to detect faults and perform automated diagnosis of catastrophic and parametric faults using

Swaminathan, Madhavan

58

Efficient fault diagnosis of helicopter gearboxes  

NASA Technical Reports Server (NTRS)

Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-fault data. To test this diagnostic system, vibration measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox faults so long as they are included in training.

Chin, H.; Danai, K.; Lewicki, D. G.

1993-01-01

59

Vehicle condition monitoring and fault diagnosis  

SciTech Connect

This book contains a compilation of papers on vehicle condition monitoring and fault diagnosis. The complete contents include: Bus operators' needs for the nineties; The use of portable remote data collection devices in vehicle preventive maintenance programs; The diagnosis of cylinder power faults in diesel engines by flywheel speed measurements; Current and future developments in vehicle servicing, condition monitoring and diagnostics; Experience with condition monitoring in other industries; Contamination and viscosity monitoring of automobile and motor cycle oils using a portable contamination meter; Knock detection alternatives for production vehicles; Oil monitoring - under what conditions can it improve engine life, yet be financed by condition-based oil changes: The use of speed sensing for monitoring the condition of military vehicle engines; The development of vehicle condition monitoring and fault diagnosis equipment for commercial vehicle fleets; The development of automotive diagnostic systems for armoured fighting vehicles in the British Army; Oil analysis techniques used in the development of automotive diesel engines and their condition monitoring in service; Recent developments in the nonintrusive diagnosis of engine faults; Operating experience with a vehicle fault diagnosis system; The case for on-board diagnostics; An on-board monitoring system with its essential sensors and evaluating characteristics; Computerized diagnostics for diesel engines; Laser tools for diesel engine diagnosis.

Not Available

1985-01-01

60

Computationally Efficient Tiered Inference for Multiple Fault Diagnosis  

E-print Network

Computationally Efficient Tiered Inference for Multiple Fault Diagnosis Juan Liu, Lukas Kuhn an efficient computational framework for statistical diagnosis featuring two main ideas: (1) structuring fault) discriminates fault assumptions based on their com- plexity. Diagnosis starts with simple fault assumptions (e

de Kleer, Johan

61

Compact Dictionaries for Fault Diagnosis in Scan-BIST  

E-print Network

Compact Dictionaries for Fault Diagnosis in Scan-BIST Chunsheng Liu, Member, IEEE, and Krishnendu for cause-effect fault diagnosis in scan-BIST. This approach relies on the use of three compact dictionaries Terms--ATPG, BIST, cause-effect fault diagnosis, diagnostic resolution, fault dictionary. æ 1

Chakrabarty, Krishnendu

62

Automatic Diagnosis of Software Functional Faults by Means of  

E-print Network

Automatic Diagnosis of Software Functional Faults by Means of Inferred Behavioral Models Ph girlfriend 3 #12;#12;Contents Introduction 19 1 Functional Faults Diagnosis 21 1.1 Impact of functional faults . . . . . . . . . . . . . . . . . . . . . 21 1.2 Automated fault diagnosis techniques

Milano-Bicocca, Università

63

Neural-network-based robust fault diagnosis in robotic systems  

Microsoft Academic Search

Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault

Arun T. Vemuri; Marios M. Polycarpou

1997-01-01

64

System-level fault diagnosis and reconfiguration  

SciTech Connect

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.

Gupta, R.

1987-01-01

65

Completing fault models for abductive diagnosis  

SciTech Connect

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.

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

1992-11-05

66

Fault diagnosis in sparse multiprocessor systems  

NASA Technical Reports Server (NTRS)

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.

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

1988-01-01

67

Fault Diagnosis and Logic Debugging Using Boolean Satisfiability Andreas Veneris  

E-print Network

Fault Diagnosis and Logic Debugging Using Boolean Satisfiability Andreas Veneris University during design error diag- nosis (logic debugging) and during fault diagnosis. De- sign error diagnosis testing. Given a faulty chip and a netlist, fault diagnosis iden- tifies locations in the correct netlist

Veneris, Andreas

68

Fault-tolerance multiprocessor interconnection networks and their fault diagnosis  

SciTech Connect

A new scheme to provide multistage interconnection networks with fault tolerance is introduced. Multiple paths between any input/output pair are created by connecting switching elements in the same stage together. Because the maximum number of possible alternative paths inherent in a network is exploited, the proposed fault-tolerant network possesses long mean lifetime and demonstrates high bandwidth. This scheme can be applied to notably enhance reliability and performance of any known multistage interconnection networks. To diagnose a fault in a redundant-path interconnection network is far more involved than a regular one. Based on a novel fault model, a diagnostic procedure is developed to effectively detect and locate any single fault existing in the multiple-path network. The fault model is practical and has potential usefulness as a tool for modeling faulty states of larger switching elements (e.g., n x n switching elements with n > 2). To facilitate this procedure, faults are classified into two groups in each of which the necessary test vectors are provided for correctly setting switching elements in the network under diagnosis when the procedure is conducted.

Tzeng, N.F.

1986-01-01

69

A representation scheme for fault diagnosis  

SciTech Connect

Subsequent to developing the conceptual description of a intelligent software system a representation scheme is selected. The knowledge representation scheme constrains the way in which knowledge is represented and executed in a software system. The representation scheme consists of methods for writing down information, organizing information, controlling software execution, and performing inference. This paper discusses a knowledge representation scheme that we are developing for use in building intelligent systems that assist in physical system fault diagnosis. This scheme consists of function and object hierarchies, generalized inference methodologies, and a control scheme that allows for concurrent reasoning during fault diagnosis.

Stratton, R.C.

1990-07-01

70

Fault Diagnosis in HVAC Chillers  

NASA Technical Reports Server (NTRS)

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

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

2005-01-01

71

Monitoring and Diagnosis of Multiple Incipient Faults Using Fault Tree Induction  

E-print Network

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 for monitoring and fault diagnosis which has a fast reaction time and is capable of dealing with complicated

Madden, Michael

72

Finding Isolated Cliques by Queries --An Approach to Fault Diagnosis with Many Faults  

E-print Network

Finding Isolated Cliques by Queries -- An Approach to Fault Diagnosis with Many Faults William, Republic of Singapore fstephan@comp.nus.edu.sg Abstract. A well­studied problem in fault diagnosis # C and j #= i that (i, j) # E i# j # C. In the present work, the classical setting of fault diagnosis

Stephan, Frank

73

Automatic Software Fault Diagnosis by Exploiting Application Signatures  

E-print Network

Automatic Software Fault Diagnosis by Exploiting Application Signatures Xiaoning Ding ­ The Ohio ­ The Ohio State University ABSTRACT Application problem diagnosis in complex enterprise environments of application faults using applications' runtime behaviors. These behaviors along with various system states

74

On the Intelligent Fault Diagnosis Method for Marine Diesel Engine  

Microsoft Academic Search

The marine diesel engine is a complex system. Its mapping process of fault diagnosis has multi-fault attributes, which means input and output of fault pattern attribute are the multi-mapping relations. An approach of intelligent fault diagnosis using fuzzy neural networks and genetic algorithms to optimize and train is studied in this paper for this system. The structure and the model

Peng Li; Baoku Su

2008-01-01

75

On Models for Diagnosable Systems and Probabilistic Fault Diagnosis  

Microsoft Academic Search

This paper is concerned with automatic fault diagnosis for digital systems with multiple faults. Three problems are treated: 1) Probabilistic fault diagnosis is presented using the graph-theoretic model of Preparata et al. The necessary and sufficient conditions to correctly diagnose any fault set whose probability of occurrence is greater than t have been developed. Some simple sufficient conditions are also

Shachindra N. Maheshwari; S. Louis Hakimi

1976-01-01

76

Automated fault location and diagnosis on electric power distribution feeders  

Microsoft Academic Search

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

Jun Zhu; D. L. Lubkeman; A. A. Girgis

1997-01-01

77

Fault Testing and Diagnosis in Combinational Digital Circuits  

Microsoft Academic Search

Abstract—he problem of designing test schedules for the testing or diagnosis of a small number of nontransient faults in combinational digital circuits (switching networks) is considered in detail. By testing and diagnosis we mean the following: 1) detection of a fault, 2) location of a fault, and 3) location of a fault within the confines of a prescribed package or

WILLIAM H. KAUTZ

1968-01-01

78

Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings  

NASA Astrophysics Data System (ADS)

Rolling element bearings are frequently used in rotary machinery, but they are also fragile mechanical parts. Hence, exact condition monitoring and fault diagnosis for them plays an important role in ensuring machinery's reliable running. Timely diagnosis of early bearing faults is desirable, but the early fault signatures are easily submerged in noise. In this paper, Wigner-Ville spectrum based on cyclic spectral density (CSWVS for a brief notation) is studied, which is able to represent the cyclostationary signals while reducing the masking effect of additive stationary noise. Both simulations and experiments show that CSWVS is a noise resistant time frequency analysis technique for extracting bearing fault patterns, when bearing signals are under influences of random noise and gear vibrations. The 3-D feature of the CSWVS is proved useful in extracting bearing fault pattern from gearbox vibration signals, where bearing signals are affected by gear meshing vibration and noise. Besides, CSWVS utilizes the second order cyclostationary property of the vibration signals produced by bearing distributed fault, and clearly extracts its fault features, which cannot be extracted by envelope analysis. To quantitatively describe the extent of bearing fault, Renyi information encoded in the time frequency diagram of CSWVS is studied. It is shown to be a more sensitive index to reflect bearing performance degradation, compared with the spectral entropy (SE), squared envelope spectrum entropy (SESE) and Renyi informations for WVD, PWVD, especially when SNR is low.

Dong, Guangming; Chen, Jin

2012-11-01

79

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

PubMed Central

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

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

2014-01-01

80

Fault detection and diagnosis of photovoltaic systems  

NASA Astrophysics Data System (ADS)

The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on fault diagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in fault diagnosis.

Wu, Xing

81

A dynamic integrated fault diagnosis method for power transformers.  

PubMed

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

Gao, Wensheng; Bai, Cuifen; Liu, Tong

2015-01-01

82

A Dynamic Integrated Fault Diagnosis Method for Power Transformers  

PubMed Central

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.

Gao, Wensheng; Liu, Tong

2015-01-01

83

Simulator for a fault-diagnosis system  

SciTech Connect

The main task of a real-time failure diagnosis system is to monitor the process measurements constantly and to detect and locate disturbances and failures. In this paper the use of a fault diagnosis system is studied from the process operator's point of view. A fuel feeding system of a coal-fueled power plant is presented as an example process. The user interface was built using a commercial process monitoring and control system (PMC1000 by HP). The diagnosis methods used are model-based, i.e. they utilize a mathematical model describing the physical relations of the process.

Lautala, P.; Vaelisuo, M.

1986-10-01

84

Fault Diagnosis Using Consensus of Markov Chains Dejan P. Jovanovi  

E-print Network

Fault Diagnosis Using Consensus of Markov Chains Dejan P. Jovanovi Department of Mathematics--A fault diagnosis procedure is proposed based on consensus in a group of local agents/experts. Local to accommodate Hidden Markov models (HMMs). Index Terms--Fault diagnosis, consensus algorithm, mixtures of Markov

Pollett, Phil

85

Distributed Fault Diagnosis Using Consensus of Unobservable Markov Chains  

E-print Network

Distributed Fault Diagnosis Using Consensus of Unobservable Markov Chains Dejan P. Jovanovi@maths.uq.edu.au Abstract A fault diagnosis procedure is proposed based on consensus in a group of local agents is extended to accommodate Hidden Markov models (HMMs). Index Terms Fault diagnosis, consensus algorithm

Pollett, Phil

86

Improved Bayesian network in steam turbine fault diagnosis  

Microsoft Academic Search

The fault diagnosis model of steam turbine based on Bayesian network is direct impacts on the complexity of the diagnostic process, so the construction of Bayesian network model is the primary problem. According actual fault diagnosis system of steam turbine containing redundancy and uncertain information, proposed attribute reduction method to fault feature, obtained the minimal diagnosis rules. Based on two-node

Zhao Qi; Liu Yi

2010-01-01

87

A novel fault diagnosis model research for electronic circuit  

Microsoft Academic Search

Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. A new optimization method for the fault diagnosis model is proposed. To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, an integrated fault diagnosis model based on support vector regression and principal

JiCheng Liu; WenJie Tian

2010-01-01

88

Optimal Adaptive Fault Diagnosis for Simple Multiprocessor Systems  

E-print Network

Optimal Adaptive Fault Diagnosis for Simple Multiprocessor Systems Evangelos Kranakis yz Andrzej Pelc \\Lambdaz Anthony Spatharis y Abstract We study adaptive system­level fault diagnosis unreliable. The aim of diagnosis is to determine correctly the fault status of all processors. We present

Kranakis, Evangelos

89

POIROT: a logic fault diagnosis tool and its applications  

Microsoft Academic Search

Logic fault diagnosis or fault isolation is the process of analyzing the failing logic portions of an integrated circuit to isolate the cause of failure. Fault diagnosis plays an important role in multiple applications at different stages of design and manufacturing. A logic diagnosis tool with applicability to a spectrum of logic DFT, ATPG and test strategies including full\\/almost fullscan

Srikanth Venkataraman; Scott Brady Drummonds

2000-01-01

90

Theories and Proofs in Fault Diagnosis Ilyas Cicekli  

E-print Network

Theories and Proofs in Fault Diagnosis Ilyas Cicekli Dept. of Comp. Eng. and Info. Sc., BilkentProlog play an important role in the expression of the fault diagnosis problem. These facilities of MetaProlog make it easier to represent digital circuits and the fault diagnosis algorithm on them. Meta

Cicekli, Ilyas

91

AUTONOMOUS FAULT DIAGNOSIS: STATE OF THE ART AND AERONAUTICAL BENCHMARK  

E-print Network

AUTONOMOUS FAULT DIAGNOSIS: STATE OF THE ART AND AERONAUTICAL BENCHMARK Julien Marzat1,2 , Hélène-case for in-flight fault diagnosis. The main concepts are recalled, the links between the approaches fault diagnosis strategies in an aeronautical context. Index Terms aircraft, analytical redundancy

Paris-Sud XI, Université de

92

On Scan Chain Diagnosis for Intermittent Faults Dan Adolfsson  

E-print Network

On Scan Chain Diagnosis for Intermittent Faults Dan Adolfsson NXP Semiconductors Corp. Innovation. In this paper, we address scan chain diagnosis under the assumption of a single, but possibly intermittent fault, and for p = 1, the scan chain fault is permanent. Note that p is unknown at the start of the diagnosis

Larsson, Erik

93

Beyond the Byzantine Generals: Unexpected Behavior and Bridging Fault Diagnosis  

E-print Network

Beyond the Byzantine Generals: Unexpected Behavior and Bridging Fault Diagnosis David B. Lavo Tracy matching and ranking algorithm. The diagnosis procedure is used to perform high­ quality bridging fault the simulations of faulty circuits. 1 Introduction Accurate fault diagnosis is an integral part of failure

Larrabee, Tracy

94

A Cognitive Fault Diagnosis System for Distributed Sensor Networks  

E-print Network

1 A Cognitive Fault Diagnosis System for Distributed Sensor Networks Cesare Alippi, IEEE Fellow positives induced by the model bias of the HMMs. Index Terms--Fault diagnosis; distributed sensor network and inappropriate control actions. A Fault Diagnosis System plays the important role of su- pervising the process

Alippi, Cesare

95

Fault diagnosis for Takagi-Sugeno nonlinear Dalil Ichalal  

E-print Network

Fault diagnosis for Takagi-Sugeno nonlinear systems Dalil Ichalal Benoit Marx Jos´e Ragot Didier.maquin}@ensem.inpl-nancy.fr Abstract: This paper addresses a new scheme for fault diagnosis in nonlinear systems described by Takagi. The convergence conditions are given in LMI formulation. Keywords: Fault diagnosis, Nonlinear systems, Takagi

Paris-Sud XI, Université de

96

A Note on Fault Diagnosis Algorithms Franck Cassez, Member, IEEE  

E-print Network

A Note on Fault Diagnosis Algorithms Franck Cassez, Member, IEEE Abstract-- In this paper we review of the complexity results for the different fault diagnosis problems. Note: This paper is an extended version the unobservable actions. The Fault diagnosis problem is a typical example of a problem under partial observation

Paris-Sud XI, Université de

97

Sensor fault diagnosis for a class of LPV descriptor systems  

E-print Network

Sensor fault diagnosis for a class of LPV descriptor systems Carlos-M. Astorga-Zaragoza Didier-based fault diagnosis method for a particular class of linear parameter variant (LPV) systems is developed fault diagnosis, linear time variant system, generalized observer scheme, descriptor system 1

Paris-Sud XI, Université de

98

BAYESIAN NETWORKS AND MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF  

E-print Network

BAYESIAN NETWORKS AND MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF INDUSTRIAL SYSTEMS Sylvain Verron-of-control status); fault diagnosis (find the root cause of the disturbance); process recovery (return the process al., 1997). Finally, for the fault diagnosis techniques we can cite the book of Chiang, Russell

Paris-Sud XI, Université de

99

Fault diagnosis for linear time-varying descriptor systems  

E-print Network

Fault diagnosis for linear time-varying descriptor systems Abdouramane Moussa Ali Qinghua Zhang, qinghua.zhang@}@inria.fr) Abstract In this paper fault diagnosis is studied for linear time varying on this result, fault diagnosis is performed by estimating the parameters characterizing actuator and sensor

Paris-Sud XI, Université de

100

Robust nonlinear fault diagnosis in input-output systems  

Microsoft Academic Search

The design and analysis of fault diagnosis architectures using the model-based analyticalredundancy approach has received considerable attention during the last two decades. One ofthe key issues in the design of such fault diagnosis schemes is the effect of modeling uncertaintieson their performance. This paper describes a fault diagnosis algorithm for a class of nonlineardynamic systems with modeling uncertainties when not

Arun T. Vemuri; Marios M. Polycarpou

1997-01-01

101

Bibliography on Induction Motors Faults Detection and Diagnosis  

E-print Network

Bibliography on Induction Motors Faults Detection and Diagnosis M.E.H. Benbouzid, Member, IEEE and diagnosis techniques. However, performing reliable and accurate motor faults detection and diagnosis a comprehensive list of books, workshops, conferences, and journal papers related to induction motors faults

Brest, Université de

102

Studies on system-level fault diagnosis and related topics  

SciTech Connect

This dissertation deals mainly with the diagnosis aspects of fault-tolerant computing. A number of system models are studied, and their diagnosability conditions established. Fault-diagnosis algorithms for some models are proposed and complexity of diagnosis problem analyzed for some other models. Finally, some fault-tolerant computer networks are studied, and efficient routing algorithms are proposed for these networks.

Sen, A.

1987-01-01

103

Modeling induction machine winding faults for diagnosis In Electrical Machines Diagnosis  

E-print Network

Chapter 2 Modeling induction machine winding faults for diagnosis In Electrical Machines Diagnosis the fault appears, its diagnosis (detection, location, characterization of fault severity) and the decision of a winding fault situation, then the time available to the experimenter may vary from a few minutes to a few

Paris-Sud XI, Université de

104

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

E-print Network

Chapter 4 Fault Diagnosis of Timed Systems In this Chapter, we review the main results pertaining to the problem of fault diagnosis of timed automata. Timed automata are introduced in Chapter 1 and Chapter 2 from a particular state, ...). The notion of fault diagnosis for discrete event systems (DES

Paris-Sud XI, Université de

105

Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method  

NASA Astrophysics Data System (ADS)

Fault diagnosis of rotating machinery is receiving more and more attentions. Vibration signals of rotating machinery are commonly analyzed to extract features of faults, and the features are identified with classifiers, e.g. artificial neural networks (ANNs) and support vector machines (SVMs). Due to nonlinear behaviors and unknown noises in machinery, the extracted features are varying from sample to sample, which may result in false classifications. It is also difficult to analytically ensure the accuracy of fault diagnosis. In this paper, a feature extraction and evaluation method is proposed for fault diagnosis of rotating machinery. Based on the central limit theory, an extraction procedure is given to obtain the statistical features with the help of existing signal processing tools. The obtained statistical features approximately obey normal distributions. They can significantly improve the performance of fault classification, and it is verified by taking ANN and SVM classifiers as examples. Then the statistical features are evaluated with a decoupling technique and compared with thresholds to make the decision on fault classification. The proposed evaluation method only requires simple algebraic computation, and the accuracy of fault classification can be analytically guaranteed in terms of the so-called false classification rate (FCR). An experiment is carried out to verify the effectiveness of the proposed method, where the unbalanced fault of rotor, inner race fault, outer race fault and ball fault of bearings are considered.

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

2015-01-01

106

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

E-print Network

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

Paris-Sud XI, Université de

107

Advanced Fault Diagnosis Methods in Molecular Networks  

PubMed Central

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

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

2014-01-01

108

A PC based fault diagnosis expert system  

NASA Technical Reports Server (NTRS)

The Integrated Status Assessment (ISA) prototype expert system performs system level fault diagnosis using rules and models created by the user. The ISA evolved from concepts to a stand-alone demonstration prototype using OPS5 on a LISP Machine. The LISP based prototype was rewritten in C and the C Language Integrated Production System (CLIPS) to run on a Personal Computer (PC) and a graphics workstation. The ISA prototype has been used to demonstrate fault diagnosis functions of Space Station Freedom's Operation Management System (OMS). This paper describes the development of the ISA prototype from early concepts to the current PC/workstation version used today and describes future areas of development for the prototype.

Marsh, Christopher A.

1990-01-01

109

Planetary Gearbox Fault Detection Using Vibration Separation Techniques  

NASA Technical Reports Server (NTRS)

Studies were performed to demonstrate the capability to detect planetary gear and bearing faults in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling fault detection.

Lewicki, David G.; LaBerge, Kelsen E.; Ehinger, Ryan T.; Fetty, Jason

2011-01-01

110

Fault detection and diagnosis in multiprocessor systems  

SciTech Connect

A hierarchical approach to multiprocessor system fault tolerance is presented there. This scheme consists of employing concurrent error detection at the processor level while utilizing multiprocessor system testing and diagnosis at the system level. In this manner, errors that are not caused by serious fault conditions are detected and recovered at the processor level, while the more serious faults are detected and diagnosed at the system level. In the concurrent-error-detection area, a technique known as the data-block capture and analysis monitoring process is presented. This process consists of first capturing a sequence of signals forming a block of data from a system and then analyzing the data block for the presence of fault symptoms. At the system level, the problems of detection and diagnosis of faulty processors are considered under a new uniformly probabilistic model. 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.

Blough, D.M.

1988-01-01

111

The comparison approach to multiprocessor fault diagnosis  

SciTech Connect

A system-level, comparison-based strategy for identifying faulty processors in a multiprocessor system is described. Unlike other strategies which have been proposed in the literature, the comparison approach is more efficient and relies on more realistic assumptions about the system under consideration. The new strategy is shown to correctly identify the set of faulty processors with a remarkably high probability, making it an attractive and viable addition or alternative to present fault diagnosis techniques.

Dahbura, A.T.; Sabnani, K.K.; King, L.L.

1987-03-01

112

A Note on Fault Diagnosis Algorithms  

E-print Network

In this paper we review algorithms for checking diagnosability of discrete-event systems and timed automata. We point out that the diagnosability problems in both cases reduce to the emptiness problem for (timed) B\\"uchi automata. Moreover, it is known that, checking whether a discrete-event system is diagnosable, can also be reduced to checking bounded diagnosability. We establish a similar result for timed automata. We also provide a synthesis of the complexity results for the different fault diagnosis problems.

Cassez, Franck

2010-01-01

113

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

NASA Astrophysics Data System (ADS)

Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing vibration data and proposes a novel method for fault diagnosis of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing fault data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing fault data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing fault data with different fault diameters is significantly or slightly different depending on different types of bearing faults. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing faults. Subsequently, five characteristic parameters sensitive to changes of bearing fault conditions were extracted from the multifractal spectrum and utilized to construct fault features of bearing fault data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing fault data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked and used as the bearing fault features. Next, MDC was used to classify the bearing fault features extracted by EMD, WT and MF-DFA in the time domain and assess the abilities of the three methods to extract fault features from bearing fault data. The results show that MF-DFA seems to outperform each of envelope analysis, statistical parameters, EMD and WT in feature extraction of bearing fault data and then the proposed method in this paper delivers satisfactory performances in distinguishing different types and severity of bearing faults. Furthermore, to further ascertain the nature causing the multifractality of bearing vibration data, the generalized Hurst exponents of the original bearing vibration data were compared with those of the shuffled and the surrogated data. Consequently, the long-range correlations for small and large fluctuations of data seem to be chiefly responsible for the multifractality of bearing vibration data.

Lin, Jinshan; Chen, Qian

2013-07-01

114

Robust Fault Diagnosis for Systems with Electronic Induced Delays  

E-print Network

Robust Fault Diagnosis for Systems with Electronic Induced Delays R. Fonod D. Henry E, France catherine.charbonnel@thalesaleniaspace.com Abstract: A problem of robust fault diagnosis in this paper. Two residual-based fault detection and isolation (FDI) schemes are proposed that are robust

Paris-Sud XI, Université de

115

A technique for fault diagnosis of defects in scan chains  

Microsoft Academic Search

In this paper, we present a scan chain fault diagnosis procedure. The diagnosis for a single scan chain fault is performed in three steps. The first step uses special chain test patterns to determine both the faulty chain and the fault type in the faulty chain. The second step uses a novel procedure to generate special test patterns to identify

Ruifeng Guo; Srikanth Venkataraman

2001-01-01

116

MULTIPLE BANDPASS AUTOREGRESSIVE DEMODULATION FOR ROLLING-ELEMENT BEARING FAULT DIAGNOSIS  

Microsoft Academic Search

This paper presents a novel method to enhance the detection and diagnosis of low-speed rolling-element bearing faults based on discrete wavelet packet analysis (DWPA). The method involves the automatic extraction of wavelet packets containing bearing fault-related features from the discrete wavelet packet analysis representation of machine vibrations. Automated selection of the wavelet packets of interest is achieved via an adaptive

J. Altmann; J. Mathew

2001-01-01

117

Application of Hilbert-Huang transformation to fault diagnosis of rotary machinery  

NASA Astrophysics Data System (ADS)

The vibration signal of a rotor bearing system is usually nonlinear and non-stationary. Fourier transform is hard to analyze these signals. A new method based upon empirical mode decomposition (EMD) and Hilbert spectrum is proposed for fault diagnosis of roller bearings. We get vibration signals from 6205-type ball bearings with inner-race faults and with outer-race faults, then analyzing its local Hilbert spectrum and local Hilbert marginal spectrum. Comparing the results with theory value, we can diagnose the fault of rotary machinery fault. In this study, we find that local Hilbert spectrum and local Hilbert marginal spectrum are very useful. Hilbert Transformation is introduced to confirm the HHT method is fit to process nonlinear and non-stationary signals.

Chen, Feng; Zhou, Xiang; Wu, Qinghua; He, Tao; He, Haixia

2008-10-01

118

PROBABILISTIC EVENT-DRIVEN FAULT DIAGNOSIS THROUGH INCREMENTAL HYPOTHESIS UPDATING  

E-print Network

PROBABILISTIC EVENT-DRIVEN FAULT DIAGNOSIS THROUGH INCREMENTAL HYPOTHESIS UPDATING M. Steinder {steinder,sethi}@cis.udel.edu Abstract: A probabilistic event-driven fault localization technique is presented, which uses a symp- tom-fault map as a fault propagation model. The technique isolates the most

Sethi, Adarshpal

119

Incipient fault diagnosis of dynamical systems using online approximators  

Microsoft Academic Search

Detection of incipient (slowly developing) faults is crucial in automated maintenance problems where early detection of worn equipment is required. In this paper, a general framework for model-based fault detection and diagnosis of a class of incipient faults is developed. The changes in the system dynamics due to the fault are modeled as nonlinear functions of the state and input

Michael A. Demetriou; Marios M. Polycarpou

1998-01-01

120

Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis  

NASA Astrophysics Data System (ADS)

Fault diagnosis of rotating machinery is very important and critical to avoid serious accidents. However, the complex and non-stationary vibration signals with a large amount of noise make the fault detection to be challenging, especially at the early stage. Based on the inner product principle, fault detection using wavelet transforms is to match fault features most correlative to basis functions, and its effectiveness is determined by the construction and choice of wavelet basis function. In this paper, a new method based on adaptive multiwavelets via two-scale similarity transforms (TSTs) is proposed. Multiwavelets can offer multiple wavelet basis functions and so have the possibility of matching various fault features preferably. TSTs are simple and straightforward methods to design a series of new biorthogonal multiwavelets with some desirable properties. Using TSTs, a changeable and adaptive multiwavelet library is established so as to provide various ascendant multiple basis functions for inner product operation. By the rule of kurtosis maximization principle, optimal multiwavelets most similar to the fault features of a given signal are searched for. The applications to a rolling bearing of outer-race fault and a flue gas turbine unit of rub-impact fault show that the proposed method is an effective approach to detecting the impulse feature components hidden in vibration signals and performs well for rotating machinery fault diagnosis.

Yuan, Jing; He, Zhengjia; Zi, Yanyang; Lei, Yaguo; Li, Zhen

2009-07-01

121

Rolling Bearing Fault Diagnosis Based on Wavelet Packet and RBF Neural Network  

Microsoft Academic Search

Based upon wavelet packet analysis and radial basis function (RBF) neural network, a method for the fault diagnosis of roller bearings is proposed in this paper. Firstly, wavelet package was used to decompose vibration time signals of bearing to extract the characteristic values-wavelet packet energy, and features were input into the RBF NN. After training, the RBF NN could be

Sun Fang; Wei Zijie

2007-01-01

122

FEATURE EXTRACTION BASED ON MORLET WAVELET AND ITS APPLICATION FOR MECHANICAL FAULT DIAGNOSIS  

Microsoft Academic Search

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

Jing Lin; Liangsheng Qu

2000-01-01

123

A Fault Diagnosis System for Turbo-Generator Set by Data Mining  

Microsoft Academic Search

Aiming at difficulties of vibration fault diagnosis for turbo-generator sets, an intelligent data-mining system based on acquired data in SCADA systems is structured. The hard core of the system is a focusing quantization algorithm and a reduction algorithm. The focusing quantization algorithm put focus on the transition point from normal to abnormal state of variables, the resolution near the focus

Yang Ping; Ren Wei

2006-01-01

124

Incipient fault diagnosis of chemical processes via artificial neural networks  

SciTech Connect

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 collected under faulty conditions, together with information about the degree of the faults and their causes. The authors describe how to apply artificial neural networks to fault diagnosis. A suitable two-stage multilayer neural network is proposed as the network to be used for diagnosis. The first stage of the network discriminates between the causes of faults when fed the noisy process measurements. Once the fault is identified, the second stage of the network estimates the degree of the fault. Thus, the diagnosis of incipient faults becomes possible.

Watanabe, K.; Matsuura, I.; Abe, M. Kubota, M. (Dept. of Instrument and Control Engineering, Hosei Univ. Tokyo 184 (JP))

1989-11-01

125

Fault detection and diagnosis of HVAC systems  

SciTech Connect

This paper presents a model-based fault detection and diagnosis (FDD) system for building heating, ventilating, and air conditioning (HVAC). Model-based fault detection is based on the strategy of determining the difference or the residuals between the normal and the existing patterns. Their approach was to attack the problem on many levels of abstraction: from the signal level, controller programming level, and system component, all the way up to the information and knowledge processing level. The various issues of real implementation of the system and the processing of real-time on-line data in actual systems of campus buildings using the proven technology and off-the-shelf commercial tools are discussed. The research was based on input and output points and software control programs found in typical direct digital control systems used for variable-air-volume air handlers and VAV cooling and hot water reheat terminal units.

Han, C.Y.; Xiao, Y.; Ruther, C.J.

1999-07-01

126

Similarity Matching Techniques for Fault Diagnosis in Automotive Infotainment Electronics  

E-print Network

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...

Kabir, Mashud

2009-01-01

127

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

Microsoft Academic Search

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

R. Isermann

1997-01-01

128

Automated fault location and diagnosis on electric power distribution feeders  

SciTech Connect

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.

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

129

Design and Implementation of a Missile Fault Diagnosis System Based on Fault-Tree Analysis  

Microsoft Academic Search

The paper briefly introduces the basic theory of fault tree analysis and rule-based expert system, and combines the fault tree analysis with rule-based expert system. Connecting fault tree with diagnosis expert system knowledge by cut set, we can express expert knowledge totally, systematically, and logically by building fault tree. It realizes the knowledge automatic acquisition and insure the consistency and

Fan Wang; Duo-Sheng Wu

2007-01-01

130

A Class of Nonlinear Unknown Input Observer for Fault Diagnosis: Application to Fault Tolerant  

E-print Network

A Class of Nonlinear Unknown Input Observer for Fault Diagnosis: Application to Fault Tolerant Unknown Input Observer (NUIO) based Fault Detection and Isolation (FDI) scheme design for a class and accommodate thruster faults of an autonomous spacecraft involved in the rendezvous phase of the Mars Sample

Boyer, Edmond

131

Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding  

NASA Astrophysics Data System (ADS)

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.

Feng, Zhipeng; Liang, Ming

2014-09-01

132

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

NASA Astrophysics Data System (ADS)

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.

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

2013-07-01

133

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

NASA Astrophysics Data System (ADS)

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.

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

2014-06-01

134

Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMD-fractal dimension  

NASA Astrophysics Data System (ADS)

In this paper a novel method for de-noising nonstationary vibration signal and diagnosing diesel engine faults is presented. The method is based on the adaptive wavelet threshold (AWT) de-noising, ensemble empirical mode decomposition (EEMD) and correlation dimension (CD). A new adaptive wavelet packet (WP) thresholding function for vibration signal de-noising is used in this paper. To alleviate the mode mixing problem occurring in EMD, ensemble empirical mode decomposition (EEMD) is presented. With EEMD, the components with truly physical meaning can be extracted from the signal. Utilizing the advantage of EEMD, this paper proposes a new AWT-EEMD-based method for fault diagnosis of diesel engine. A study of correlation dimension in engine condition monitoring is reported also. Some important influencing factors relating directly to the computational precision of correlation dimension are discussed. Industrial engine normal and fault vibration signals measured from different operating conditions are analyzed using the above method.

Wang, Xia; Liu, Changwen; Bi, Fengrong; Bi, Xiaoyang; Shao, Kang

2013-12-01

135

Research on Internet-Based Open Remote Fault Diagnosis Expert System  

Microsoft Academic Search

Based on Internet and using artificial intelligence techniques to guild remote fault diagnosis expert system can provide expert-level fault diagnosis service for remote devices. Based on the introduction of general structure and related key techniques of fault diagnosis system, the expert system was introduced into fault diagnosis system in this paper. The architecture of an open fault diagnosis expert system

Zhi-qin Ding

2009-01-01

136

Research on Internet-based open remote fault diagnosis expert system  

Microsoft Academic Search

Based on Internet and using artificial intelligence techniques to guild remote fault diagnosis expert system can provide expert-level fault diagnosis service for remote devices. Based on the introduction of general structure and related key techniques of fault diagnosis system, the expert system was introduced into fault diagnosis system in this paper. The architecture of an open fault diagnosis expert system

Huang Yi

2009-01-01

137

Beyond the byzantine generals: unexpected behavior and bridging fault diagnosis  

Microsoft Academic Search

Physical defects cause behaviors unmodeled by even the best fault simulators, which complicates predictive diagnosis. This paper reports on a diagnosis procedure that uses modified composite signatures constructed from single stuck-at information combined with a lexicographic matching and ranking algorithm. The diagnosis procedure is used to perform high-quality bridging fault diagnosis for more than 400,000 diagnostic experiments involving dropping or

David B. Lavo; Tracy Larrabee; Brian Chess

1996-01-01

138

Experimental Evaluation of a Structure-Based Connectionist Network for Fault Diagnosis of Helicopter Gearboxes  

NASA Technical Reports Server (NTRS)

This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

Jammu, V. B.; Danai, K.; Lewicki, D. G.

1998-01-01

139

Composite Bending Box section Modal Vibration fault Detection  

Microsoft Academic Search

Abstract: One of the primary concerns with Composite construction in critical structures such as wings and stabilizers is that hidden faults and cracks can develop operationally. In the real world, catastrophic sudden failure can result from these undetected,faults in composite structures. Vibration data incorporating a broad frequency modal approach, could detect significant changes,prior to failure. The purpose,of this report is

Rudy Werlink

140

Fault diagnosis for nonlinear systems represented by heterogeneous multiple models  

E-print Network

Fault diagnosis for nonlinear systems represented by heterogeneous multiple models Rodolfo Orjuela the simultaneous state and unknown input (e.g. a fault) estimation of the system under investigation to accomplish the detection, the localisation and eventually the estimation of sensor faults acting

Paris-Sud XI, Université de

141

Modeling Fault Propagation in Telecommunications Networks for Diagnosis Purposes  

E-print Network

Modeling Fault Propagation in Telecommunications Networks for Diagnosis Purposes A. Aghasaryan , C- works when a fault occurs and how the effects are propagated across equipment. The objective of a model corresponding to the supervised network; this model can be used to simulate fault propagation

Pencolé, Yannick

142

Planning as Heuristic Search for Incremental Fault Diagnosis and Repair  

E-print Network

Planning as Heuristic Search for Incremental Fault Diagnosis and Repair HÃ¥kan Warnquist and Jonas of incremental fault diag- nosis and repair of mechatronic systems where the task is to choose actions state, but given the in- formation available a probability distribution over possible faults can

Doherty, Patrick

143

Non-cooperative Diagnosis of Submarine Cable Faults  

E-print Network

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- surement results for a recent SEA-ME-WE 4 cable fault in 2010. Our measurement methodology captures

Chang, Rocky Kow-Chuen

144

Fault diagnosis of electronic systems using intelligent techniques: a review  

Microsoft Academic Search

In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. The purpose of fault diagnosis is the isolation of faults on defective systems, a task requiring a high skill set. This has driven the need for automated diagnostic tools. Over the last two decades, automated diagnosis has been an active research area, but the

William G. Fenton; T. Martin Mcginnity; Liam P. Maguire

2001-01-01

145

Fault diagnosis of ball bearings using continuous wavelet transform  

Microsoft Academic Search

Bearing failure is one of the foremost causes of breakdown in rotating machines, resulting in costly systems downtime. This paper presents a methodology for rolling element bearings fault diagnosis using continuous wavelet transform (CWT). The fault diagnosis method consists of three steps, firstly the six different base wavelets are considered in which three are from real valued and other three

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

2011-01-01

146

Fault diagnosis based on support vector machine ensemble  

Microsoft Academic Search

Accurate fault diagnosis for large machines is very important for its economic meaning. In essence, fault diagnosis is a pattern classification and recognition problem of judging whether the operation status of the system is normal or not. Support vector machines (SVMs), well motivated theoretically, have been introduced as effective methods for solving classification problems. However, the generalization performance of SVMs

Ye Li; Yun-Ze Cal; Ru-Po Yin; Xiao-Ming Xu

2005-01-01

147

Development of smart sensors system for machine fault diagnosis  

Microsoft Academic Search

Machine fault diagnosis is a traditional maintenance problem. In the past, the maintenance using tradition sensors is money-cost, which limits wide application in industry. To develop a cost-effective maintenance technique, this paper presents a novel research using smart sensor systems for machine fault diagnosis. In this paper, a smart sensors system is developed which acquires three types of signals involving

Jong-duk Son; Gang Niu; Bo-suk Yang; Don-ha Hwang; Dong-sik Kang

2009-01-01

148

Equipment fault diagnosis algorithm of SVM based on GA  

Microsoft Academic Search

To solve the problem of equipment fault diagnosis, the paper proposes a fault diagnosis model based on Support Vector Machines (SVM) and studies the parameters that influence model accuracy. On the basis of analyzing model parameters influence, A new kind of evaluation function about algorithm accuracy and the Genetic algorithm of the global optimization parameters selection are presented. According to

Xiaoli Cao; Chao-yuan Jiang; Siyuan Gan

2010-01-01

149

A Nand Model ror Fault Diagnosis in Combinational Logic Networks  

Microsoft Academic Search

A network model colled the normal NAND model is introduced for the study of fault diagnosis in combinational logic circuits. It is shown that every network can be transformed into an equivalent normal NAND network from which all the information pertaining to the diagnosis of the original network con be obtained. The use of this model greatly simplifies fault analysis

JOHN P. HAYES

1971-01-01

150

A Probabilistic Approach to Fault Diagnosis in Linear Lightwave Networks  

Microsoft Academic Search

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

Robert Huijie Deng; Aurel A. Lazar; Weiguo Wang

1993-01-01

151

Fault Diagnosis of Rolling Bearing Based on Wavelet Packet Transformation  

Microsoft Academic Search

\\u000a The faults of rolling bearings frequently occur in rotary machinery, therefore the rolling bearings fault diagnosis is a very\\u000a important research project. In this paper, a method of pattern recognition for fault diagnosis of rolling bearing is proposed,\\u000a which is based on wavelet packet transformation combined with Statistics. Firstly, the wavelet packet analysis is utilized\\u000a to divide the dynamic signal

Zhitong Jiang; Chengfei Zhu; Guanqing Chang; Hongxing Chang

152

Aiding the operator during novel fault diagnosis  

SciTech Connect

The design and evaluation are presented for knowledge-based aiding for a human operator who must diagnose a novel fault in a dynamic, physical system. Since the operator must employ deep reasoning about system behavior to diagnose such a fault, the performance may be restricted by cognitive limitations and biases. A computer aid based on a qualitative model of the system was built to help the operator overcome some of his/her cognitive limitations. This aid differs from most expert systems in that it operates at several levels of interaction believed to be more suitable for deep reasoning. Four aiding approaches, each of which provided unique information to the operator, were evaluated. The aiding features were designed to help the human's causal reasoning about the system in predicting normal system behavior (N aiding), integrating observations into actual system behavior (O aiding), finding discrepancies between the two (O-N aiding), or finding discrepancies between observed behavior and hypothetical behavior (O-H aiding). Three experiments were conducted to evaluate the aiding approaches and to investigate the nature of deep-reasoning diagnosis.

Yoon, W.C.

1987-01-01

153

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

NASA Astrophysics Data System (ADS)

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.

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

2011-07-01

154

Multisensor fusion for induction motor aging analysis and fault diagnosis  

NASA Astrophysics Data System (ADS)

Induction motors are the most commonly used electrical drives, ranging in power from fractional horsepower to several thousand horsepowers. Several studies have been conducted to identify the cause of failure of induction motors in industrial applications. Recent activities indicate a focus towards building intelligence into the motors, so that a continuous on-line fault diagnosis and prognosis may be performed. The purpose of this research and development was to perform aging studies of three-phase, squirrel-cage induction motors; establish a database of mechanical, electrical and thermal measurements from load testing of the motors; develop a sensor-fusion method for on-line motor diagnosis; and use the accelerated aging models to extrapolate to the normal aging regimes. A new laboratory was established at The University of Tennessee to meet the goals of the project. The accelerated aging and motor performance tests constitute a unique database, containing information about the trend characteristics of measured signatures as a function of motor faults. The various measurements facilitate enhanced fault diagnosis of motors and may be effectively utilized to increase the reliability of decision making and for the development of life prediction techniques. One of these signatures is the use of Multi-Resolution Analysis (MRA) using wavelets. Using MRA in trending different frequency bands has revealed that higher frequencies show a characteristic increase when the condition of a bearing is in question. This study effectively showed that the use of MRA in vibration signatures can identify a thermal degradation or degradation via electrical charge of the bearing, whereas other failure mechanisms, such as winding insulation failure, do not exhibit such characteristics. A motor diagnostic system, called the Intelligent Motor Monitoring System (IMMS) was developed in this research. The IMMS integrated the various mechanical, electrical and thermal signatures, and artificial neural networks and fuzzy logic algorithms. The IMMS was then used for motor fault detection and isolation and for estimating its remaining operable lifetime. The performance of the IMMS was evaluated using the motor aging data, and showed that several motor degradation modes could be effectively diagnosed and the prognosis of motor operation could be established.

Erbay, Ali Seyfettin

155

An approach to performance assessment and fault diagnosis for rotating machinery equipment  

NASA Astrophysics Data System (ADS)

Predict and prevent maintenance is routinely carried out. However, how to address the problem of performance assessment maximizing the use of available monitoring data, and how to build a framework that integrates performance assessment, fault detection, and diagnosis are still a significant challenge. For this purpose, this article introduces an approach to performance assessment and fault diagnosis for rotating machinery, including wavelet packet decomposition for extracting energy feature samples from vibration signals acquired during normal and faulty conditions; clustering analysis for demonstrating the separability of the samples; and Fisher discriminant analysis for providing an optimal lower-dimensional representation, in terms of maximizing the separability among different populations, by projecting the samples into a new space. In the new low-dimensional space, the Mahalanobis distance (MD) between the new measurement data and normal population can be calculated for performance assessment. Moreover, this model for performance assessment only requires data to be available in normal conditions and any one of all possible fault conditions, without the necessity for the full life cycle of condition monitoring data. In addition, if monitoring data under different fault conditions are available, the fault mode can be identified accurately by comparing the MDs between the new measurement data and each fault population. Finally, the proposed method was verified to be successful on performance assessment and fault diagnosis via a hydraulic pump test and a ball bearing test.

Tao, Xiaochuang; Lu, Chen; Lu, Chuan; Wang, Zili

2013-12-01

156

Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine  

Microsoft Academic Search

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine.

A. Duyar; T.-H. Guo; W. Merrill; J. Musgrave

1992-01-01

157

Detection of signal transients based on wavelet and statistics for machine fault diagnosis  

NASA Astrophysics Data System (ADS)

This paper presents a transient detection method that combines continuous wavelet transform (CWT) and Kolmogorov-Smirnov (K-S) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed "step-by-step detection" based on K-S test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the K-S test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.

Zhu, Z. K.; Yan, Ruqiang; Luo, Liheng; Feng, Z. H.; Kong, F. R.

2009-05-01

158

Fault-diagnosis of some multistage networks  

SciTech Connect

It was shown previously that four tests are required in order to detect single faults and to locate single link stuck faults for a class of multistage interconnection networks. In this paper the authors show that only three tests are actually necessary and sufficient both to detect single faults and to locate single link stuck faults. The test schemes described achieve the least number of tests required for detecting and locating such faults. 2 references.

Tse-yun Feng; I-pieng Kao

1982-01-01

159

Wind Energy Conversion Systems Fault Diagnosis Using Wavelet Analysis  

E-print Network

Wind Energy Conversion Systems Fault Diagnosis Using Wavelet Analysis Elie Al-Ahmar1,2 , Mohamed El transient technique suitable for electrical and mechanical failure diagnosis in an induction generator based, induction generator, Discrete Wavelet Transform (DWT), failure diagnosis. I. Introduction Wind energy

Paris-Sud XI, Université de

160

Adaptive PCA based fault diagnosis scheme in imperial smelting process.  

PubMed

In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. PMID:24439836

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

2014-09-01

161

Expert systems for real-time monitoring and fault diagnosis  

NASA Technical Reports Server (NTRS)

Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and fault diagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft.

Edwards, S. J.; Caglayan, A. K.

1989-01-01

162

Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis  

NASA Astrophysics Data System (ADS)

The sparse decomposition based on matching pursuit is an adaptive sparse expression of the signals. An adaptive matching pursuit algorithm that uses an impulse dictionary is introduced in this article for rolling bearing vibration signal processing and fault diagnosis. First, a new dictionary model is established according to the characteristics and mechanism of rolling bearing faults. The new model incorporates the rotational speed of the bearing, the dimensions of the bearing and the bearing fault status, among other parameters. The model can simulate the impulse experienced by the bearing at different bearing fault levels. A simulation experiment suggests that a new impulse dictionary used in a matching pursuit algorithm combined with a genetic algorithm has a more accurate effect on bearing fault diagnosis than using a traditional impulse dictionary. However, those two methods have some weak points, namely, poor stability, rapidity and controllability. Each key parameter in the dictionary model and its influence on the analysis results are systematically studied, and the impulse location is determined as the primary model parameter. The adaptive impulse dictionary is established by changing characteristic parameters progressively. The dictionary built by this method has a lower redundancy and a higher relevance between each dictionary atom and the analyzed vibration signal. The matching pursuit algorithm of an adaptive impulse dictionary is adopted to analyze the simulated signals. The results indicate that the characteristic fault components could be accurately extracted from the noisy simulation fault signals by this algorithm, and the result exhibited a higher efficiency in addition to an improved stability, rapidity and controllability when compared with a matching pursuit approach that was based on a genetic algorithm. We experimentally analyze the early-stage fault signals and composite fault signals of the bearing. The results further demonstrate the effectiveness and superiority of the matching pursuit algorithm that uses the adaptive impulse dictionary. Finally, this algorithm is applied to the analysis of engineering data, and good results are achieved.

Cui, Lingli; Wang, Jing; Lee, Seungchul

2014-05-01

163

Solar Dynamic Power System Fault Diagnosis  

NASA Technical Reports Server (NTRS)

The objective of this research is to conduct various fault simulation studies for diagnosing the type and location of faults in the power distribution system. Different types of faults are simulated at different locations within the distribution system and the faulted waveforms are monitored at measurable nodes such as at the output of the DDCU's. These fault signatures are processed using feature extractors such as FFT and wavelet transforms. The extracted features are fed to a clustering based neural network for training and subsequent testing using previously unseen data. Different load models consisting of constant impedance and constant power are used for the loads. Open circuit faults and short circuit faults are studied. It is concluded from present studies that using features extracted from wavelet transforms give better success rates during ANN testing. The trained ANN's are capable of diagnosing fault types and approximate locations in the solar dynamic power distribution system.

Momoh, James A.; Dias, Lakshman G.

1996-01-01

164

Application of Image Recognition Technology based on Fractal Dimension for Diesel Engine Fault Diagnosis  

Microsoft Academic Search

A new method of diesel engine fault diagnosis that uses image recognition technology based on fractal dimension is proposed. The Wigner-Ville distributions of six kinds of vibration acceleration signals which are acquired from diesel engine cylinder head are calculated by time-frequency analysis, and a series of time-frequency gray images can be obtained from above distributions by image processing. According to

Yanping Cai; Shu Cheng; Yanping He; Ping Xu

2008-01-01

165

Fault diagnosis in non-rectangular interconnection networks  

SciTech Connect

A method for fault diagnosis in a class of multistage interconnection networks which can be used in a real-time multicomputer system is presented. The method allows single and multiple fault diagnosis of regular sw-Banyan networks with arbitrary spread s, arbitrary fanout f, and arbitrary number of levels l, and covers stuck-at-0, stuck-at-1 and bridge type faults in the data and the control parts of the network. The method is based on a graph-theoretic approach and can be applied serially for simple fault detection or in parallel for fault detection and location. Analytical bounds on the number of tests which are needed for the network diagnosis are given and some implementation issues on the Texas reconfigurable array computer are presented. 19 references.

Opper, E.; Lipovski, G.J.

1983-01-01

166

Diagnosis of Realistic Defects Based on the X-Fault Model Ilia Polian  

E-print Network

Diagnosis of Realistic Defects Based on the X-Fault Model Ilia Polian Yusuke Nakamura Piet Engelke by conventional fault models are a challenge for state-of-the-art fault diagnosis techniques. The X-fault model the performance of the X-fault diagnosis for a number of defect classes leading to highly complex circuit behavior

Polian, Ilia

167

Fault Diagnosis of Industrial Systems by Conditional Gaussian Network including a Distance Rejection  

E-print Network

Fault Diagnosis of Industrial Systems by Conditional Gaussian Network including a Distance faults and to obtain sufficient results in rejection of new types of fault. Key words: Fault Diagnosis, for the fault diagnosis techniques we can cite the book of Chiang et al. (2001) which presents a lot of them

Paris-Sud XI, Université de

168

Probabilistic fault diagnosis in communication systems through incremental hypothesis updating q  

E-print Network

Probabilistic fault diagnosis in communication systems through incremental hypothesis updating q M reasoning; Event correlation 1. Introduction Fault diagnosis is a central aspect of network fault management. The core of fault diagnosis is fault localization [1­3]­­a process of analyzing external symptoms

Sethi, Adarshpal

169

Fault diagnosis system for rotary machines based on fuzzy neural networks  

Microsoft Academic Search

This paper is concerned with the application of fuzzy neural networks to a fault diagnosis system of a rotary machine. The fault diagnosis system is based on a series of standard fault pattern pairs between fault symptoms and fault. Fuzzy neural networks are trained to memorize these standard pattern pairs. When an unknown sample is input into the trained fault

Sheng Zhang; T. Asakura; Xiaoli Xu; Baojie Xu

2003-01-01

170

Fault Diagnosis for AUVs using Support Vector Machines  

Microsoft Academic Search

In this paper an observer-based fault diagnosis (FD) approach for autonomous underwater vehicles (AUVs), subject to actuator faults (i.e., faults affecting the propulsion system and\\/or the control surfaces), is proposed. A diagnostic observer is developed based on the available dynamic model of the AUV. Compensation of unknown dynamics, uncertainties and disturbances is achieved through the adoption of a class of

Gianluca Antonelli; Fabrizio Caccavale; Carlo Sansone; Luigi Villani

2004-01-01

171

Robust model-based fault diagnosis for chemical process systems  

E-print Network

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...

Rajaraman, Srinivasan

2006-08-16

172

CAPRI : a common architecture for distributed probabilistic Internet fault diagnosis  

E-print Network

This thesis presents a new approach to root cause localization and fault diagnosis in the Internet based on a Common Architecture for Probabilistic Reasoning in the Internet (CAPRI) in which distributed, heterogeneous ...

Lee, George J. (George Janbing), 1979-

2007-01-01

173

CAPRI: A Common Architecture for Distributed Probabilistic Internet Fault Diagnosis  

E-print Network

This thesis presents a new approach to root cause localization and fault diagnosis in the Internet based on a Common Architecture for Probabilistic Reasoning in the Internet (CAPRI) in which distributed, heterogeneous ...

Lee, George J.

2007-06-04

174

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

E-print Network

system solution to a problem of fault diagnosis in a four­stroke marine diesel engine; that of early the intervention of a skilled marine engineer, to undertake the time­ consuming and fallible process of comparing of combustion condition in a marine engine is crucial since undetected faults can rapidly become compoun­ ded

Sharkey, Amanda

175

Real-time fault diagnosis for propulsion systems  

NASA Technical Reports Server (NTRS)

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.

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

1991-01-01

176

Fault Detection and Diagnosis Method for VAV Terminal Units  

E-print Network

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...

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

2004-01-01

177

Catastrophic fault diagnosis in dynamic systems using bond graph methods  

SciTech Connect

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.

Yarom, Tamar.

1990-01-01

178

An artificial neural network approach to transformer fault diagnosis  

SciTech Connect

This paper presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach.

Zhang, Y.; Ding, X.; Liu, Y. [Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States). Bradley Dept. of Electrical Engineering] [Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States). Bradley Dept. of Electrical Engineering; Griffin, P.J. [Doble Engineering Co., Watertown, MA (United States)] [Doble Engineering Co., Watertown, MA (United States)

1996-10-01

179

An Efficient Fault Diagnosis Algorithm for Symmetric Multiple Processor Architectures  

Microsoft Academic Search

A new diagnosis algorithm for determining the existing fault situation in a symmetric multiple processor architecture is given. The algorithm assumes that there are n processors, each of which is tested by at least t other processors, and at most t of which are faulty. The existing fault situation is always diagnosed if n ? 2t + 1 and, in

Gerard G. L. Meyer; Gerald M. Masson

1978-01-01

180

An artificial neural network approach to transformer fault diagnosis  

Microsoft Academic Search

This paper presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. A two-step ANN method is used to detect faults with or without cellulose involved. Good diagnosis accuracy is obtained with the proposed approach.

Y. Zhang; X. Ding; Y. Liu; P. J. Griffin

1996-01-01

181

Fault diagnosis at substations based on sequential event recorders  

SciTech Connect

Expert systems have been developed to help dispatchers understand the immediate situation after a fault occurrence on Extra High Voltage or High Voltage power systems. This paper presents work specifically related to fault diagnosis at the substation level using recorded sequential signals from various protective and control devices. When necessary, comparisons will be made with relevant diagnostic systems at the centralized, supervisor level.

Hertz, A.; Fauquembergue, P. (Research and Development Div., Electricite de France, Clamart (FR))

1992-05-01

182

A NEW PROCEDURE BASED ON MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF  

E-print Network

A NEW PROCEDURE BASED ON MUTUAL INFORMATION FOR FAULT DIAGNOSIS OF INDUSTRIAL SYSTEMS Sylvain and compared on the same data with those of other published methods. Keywords: Fault diagnosis, bayesian relevant variables for the diagnosis of the fault (disturbance). The third step is the fault diagnosis

Paris-Sud XI, Université de

183

Procedure based on mutual information and bayesian networks for the fault diagnosis of industrial systems  

E-print Network

Procedure based on mutual information and bayesian networks for the fault diagnosis of industrial variables for the diagnosis of the fault. The third step is the fault diagnosis (which is the purpose-of- control status. The fault diagnosis procedure can be seen as a classi- fication task. Indeed, as we said

Paris-Sud XI, Université de

184

Sensor Minimization Problems with Static or Dynamic Observers for Fault Diagnosis  

E-print Network

Sensor Minimization Problems with Static or Dynamic Observers for Fault Diagnosis (Extended in the context of fault diagnosis. Fault diagnosis consists of synthesizing a diagnoser that observes a given it wishes to observe. 1 Introduction Monitoring, Testing, Fault Diagnosis and Control. Ma- ny problems

Paris-Sud XI, Université de

185

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

PubMed

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

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

2013-03-01

186

Composite Bending Box Section Modal Vibration Fault Detection  

NASA Technical Reports Server (NTRS)

One of the primary concerns with Composite construction in critical structures such as wings and stabilizers is that hidden faults and cracks can develop operationally. In the real world, catastrophic sudden failure can result from these undetected faults in composite structures. Vibration data incorporating a broad frequency modal approach, could detect significant changes prior to failure. The purpose of this report is to investigate the usefulness of frequency mode testing before and after bending and torsion loading on a composite bending Box Test section. This test article is representative of construction techniques being developed for the recent NASA Blended Wing Body Low Speed Vehicle Project. The Box section represents the construction technique on the proposed blended wing aircraft. Modal testing using an impact hammer provides an frequency fingerprint before and after bending and torsional loading. If a significant structural discontinuity develops, the vibration response is expected to change. The limitations of the data will be evaluated for future use as a non-destructive in-situ method of assessing hidden damage in similarly constructed composite wing assemblies. Modal vibration fault detection sensitivity to band-width, location and axis will be investigated. Do the sensor accelerometers need to be near the fault and or in the same axis? The response data used in this report was recorded at 17 locations using tri-axial accelerometers. The modal tests were conducted following 5 independent loading conditions before load to failure and 2 following load to failure over a period of 6 weeks. Redundant data was used to minimize effects from uncontrolled variables which could lead to incorrect interpretations. It will be shown that vibrational modes detected failure at many locations when skin de-bonding failures occurred near the center section. Important considerations are the axis selected and frequency range.

Werlink, Rudy

2002-01-01

187

Support Vector Machine for Mechanical Faults Diagnosis  

Microsoft Academic Search

Aiming at the difficulty that Support Vector Machine (SVM) model selection of classification algorithm affect classification accuracy, it research relevant factors that influence the precision of fault classifiers based on the typical fault data samples obtained by experimental setup of rotor-bearing systems. The results show that different SVM classifiers, in which different kernel functions and different kernel functions parameters are

Changlin Wang; Yimei Song

2010-01-01

188

Gray-box approach to fault diagnosis of dynamical systems  

Microsoft Academic Search

Model-based fault diagnosis has become an important approach for diagnosis of dynamical systems. By comparing the observed sensor values with those of the predicted values by the model, e.g., the residual, the health of the system can be assessed. However, because of modeling errors, sensor noise, disturbances, etc., direct comparison between observed and predicted values can be difficult. In an

Michail Zak; Han Park

2001-01-01

189

A Technique for Logic Fault Diagnosis of Interconnect Open Defects  

Microsoft Academic Search

A technique to perform logic diagnosis of defects that cause interconnects in a digital logic circuit to become open or highly resistive is presented. The novel features of this work include a diagnostic fault model to capture potential faulty behaviors in the presence of an open defect and diagnosis algorithms that leverage the diagnostic model while circumventing the need for

Srikanth Venkataraman; Scott Brady Drummonds

2000-01-01

190

Rao-Blackwellised Particle Filtering for Fault Diagnosis  

Microsoft Academic Search

We tackle the fault diagnosis problem using conditionally Gaussian state space models and an efficient Monte Carlo method known as Rao-Blackwellised parti- cle filtering. In this setting, there is one different linear- Gaussian state space model for each possible discrete state of operation. The task of diagnosis is to identify the dis- crete state of operation using the continuous measurements

Nando de Freitas

2001-01-01

191

Evaluation of thermography image data for machine fault diagnosis  

Microsoft Academic Search

A novel approach for fault diagnosis of rotating machine based on thermal image investigation using image histogram features is proposed in this paper. Herein, the machine learning and statistical approach are adopted along with thermal image signal to machine condition diagnosis. Using thermal images, the information of machine condition can be investigated more simply than other conventional methods of machine

Achmad Widodo; Bo-Suk Yang

2010-01-01

192

Wind Turbines Condition Monitoring and Fault Diagnosis Using Generator Current Amplitude  

E-print Network

Wind Turbines Condition Monitoring and Fault Diagnosis Using Generator Current Amplitude cases. Index Terms--Wind turbine, DFIG, fault detection, diagnosis, amplitude modulation, Hilbert constitutes an essential background for the development of any failure diagnosis system. Regarding a failure

Paris-Sud XI, Université de

193

A method of fault analysis for test generation and fault diagnosis  

Microsoft Academic Search

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

Henry Cox; Janusz Rajski

1988-01-01

194

Detection of Induction Motor Faults: A Comparison of Stator Current, Vibration and Acoustic Methods  

Microsoft Academic Search

In this paper we present the comparison results of induction motor fault detection using stator current, vibration, and acoustic methods. A broken rotor bar fault and a combination of bearing faults (inner race, outer race, and rolling element faults) were induced into variable speed three-phase induction motors. Both healthy and faulty signatures were acquired under different speed and load conditions.

WEIDONG LI; CHRIS K. MECHEFSKE

2006-01-01

195

Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks  

NASA Astrophysics Data System (ADS)

A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

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

2011-03-01

196

Fault diagnosis of marine main engine based on BP neural network  

Microsoft Academic Search

The fault diagnosis of a marine's main engine is a significant but complicated problem, and artificial intelligence has been considered in this field for decades. This paper describes a fault diagnosis method for main engine based on BP neural network with the system fault classified into hierarchies according to fault tree analysis. Sample data of supercharger's fault is collected and

Huiqing Jiang; Suling Jia; Guanjun Lai

2009-01-01

197

Model-based reasoning for fault diagnosis  

SciTech Connect

Recent developments in Artificial Intelligence (AI) have resulted in newer approaches using knowledge-based expert systems to problems in the design of automated process fault diagnostic systems. Despite the advantages offered by these first-generation systems over conventional methods such as fault tree analysis and signed digraphs there are some serious drawbacks. Owing to their complete reliance on heuristic or experiential knowledge, the first-generation systems are not flexible to accommodate even small changes in process configuration and are incapable of diagnosing unanticipated fault combinations. In this paper, the authors discuss a methodology that aids the development of expert systems which are process-independent, transparent in their reasoning, and capable of diagnosing a wide diversity of faults. A prototype expert system, called MODEX, has been implemented incorporating these ideas. The domain knowledge of the system is based on qualitative reasoning principles and captures physical interconnections between equipment units as well as casual relationships among process state variables. The inference strategy uses model-based reasoning for analyzing the plant behavior. Using a variant of the technique adopted from fault tree synthesis, an initially observed abnormal symptom is considered to be a top level event and a tree structure is constructed as the system searches for a basic event to which the fault can be traced. The diagnostic reasoning process is driven by a problem reduction strategy. The knowledge base is process-independent, thereby enhancing the generality of the expert system. Reasoning from first-principles with the aid of causal and fault models facilitates the diagnoses of novel or unanticipated faults.

Venkatasubramanian, V.; Rich, S.H.

1987-01-01

198

An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems  

E-print Network

An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems Matthew Daigle matthew.j.daigle,xenofon.koutsoukos,gautam.biswas@vanderbilt.edu 1 Fault Diagnosis of Electrical Power Systems Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Faults

Daigle, Matthew

199

Control Engineering Practice 12 (2004) 11511165 Fault diagnosis for a turbine engine$  

E-print Network

Control Engineering Practice 12 (2004) 1151­1165 Fault diagnosis for a turbine engine$ Yixin Diaoa 43210, USA Received 16 February 2000; accepted 28 November 2003 Abstract Fault detection and diagnosis and deterioration differences, in addition to a variety of faults. To develop a fault diagnosis system we begin

200

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

E-print Network

­ 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 faults; however, we do not enumerate all the possible multiple faults. The diagnosis is performed in two

Aboulhamid, El Mostapha

201

An Event-based Approach to Integrated Parametric and Discrete Fault Diagnosis in Hybrid Systems  

E-print Network

An Event-based Approach to Integrated Parametric and Discrete Fault Diagnosis in Hybrid Systems.j.daigle@nasa.gov, {xenofon.koutsoukos,gautam.biswas}@vanderbilt.edu Abstract Fault diagnosis is crucial for ensuring the safe for hybrid diagnosis of parametric and discrete faults by incorporating the effects of both types of faults

Daigle, Matthew

202

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

E-print Network

INCREASING EFFECTIVENESS OF MODEL-BASED FAULT DIAGNOSIS: A DYNAMIC BAYESIAN NETWORK DESIGN-based fault diagnosis when signature vectors of various faults are identical or closed. The proposed approach-based fault diagnosis. The decision making, formalised as a bayesian network, is established with a priori

Paris-Sud XI, Université de

203

Fault diagnosis of industrial systems with bayesian networks and mutual information  

E-print Network

Fault diagnosis of industrial systems with bayesian networks and mutual information Sylvain VERRON-of-control status); fault diagnosis (find the root cause of the fault); process recovery (return the process is the MYT decomposition of the T2 statistic [10], [11]. Finally, for the fault diagnosis techniques we can

Paris-Sud XI, Université de

204

An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems  

E-print Network

An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems Matthew Daigle {matthew.j.daigle,xenofon.koutsoukos,gautam.biswas}@vanderbilt.edu 1 Fault Diagnosis of Electrical Power Systems Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Faults

Koutsoukos, Xenofon D.

205

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

NASA Astrophysics Data System (ADS)

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.

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

2015-02-01

206

Auxiliary signal design in fault detection and diagnosis  

NASA Astrophysics Data System (ADS)

Fault-detection and diagnosis schemes for systems represented by linear MIMO stochastic models are developed analytically, with a focus on on the design and application of auxiliary signals. The basic principles of optimal-input design are reviewed, and consideration is given to the sequential probability ratio test (SPRT), auxiliary signals for improving SPRT fault detection, and the extension of the SPRT to multiple-hypothesis testing. Two chapters are devoted to the application of the SPRT to a model chemical plant (producing anhydrous caustic soda), including model derivation, model identification, detection of type I and type II faults, and the fault-diagnosis decision-making mechanism. Numerical results are presented in graphs and briefly characterized.

Zhang, Xue Jun

207

Sensor fault diagnosis with a probabilistic decision process  

NASA Astrophysics Data System (ADS)

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

Sharifi, Reza; Langari, Reza

2013-01-01

208

The application of energy operator demodulation approach based on EMD in machinery fault diagnosis  

NASA Astrophysics Data System (ADS)

An energy operator demodulation approach based on EMD (Empirical Mode Decomposition) is proposed to extract the instantaneous frequencies and amplitudes of the multi-component amplitude-modulated and frequency-modulated (AM-FM) signals. Furthermore the proposed approach is applied to machinery fault diagnosis. Firstly, EMD method is used to decompose a multi-component AM-FM signal into a number of intrinsic mode functions (IFMs). Secondly, the energy operator demodulation method is applied to each IMF and the instantaneous amplitudes and frequencies of a multi-component AM-FM signal are extracted. Finally, the spectrum analysis is applied to each instantaneous amplitude in order to obtain envelope spectra from which the mechanical fault can be diagnosed. The analysis results show that the energy operator demodulation approach based on EMD can extract the characteristic of machinery fault vibration signals efficiently.

Junsheng, Cheng; Dejie, Yu; Yu, Yang

2007-02-01

209

Research on iterated Hilbert transform and its application in mechanical fault diagnosis  

NASA Astrophysics Data System (ADS)

Multicomponent AM-FM demodulation is an available method for machinery fault vibration signal analysis, so a new method for mechanical fault diagnosis based on iterated Hilbert transform (IHT) is proposed. The principle of computing the asymptotically exact multicomponent sinusoidal model for an arbitrary signal by iterating Hilbert transform is introduced, and some properties of IHT are analyzed. Theoretical analysis for the generic two-component signal shows that there are limitations in the direct estimation of instantaneous frequencies via the phase signals of the previously obtained model. Therefore, a smoothed instantaneous frequency estimation (SIFE) method based on difference operator and zero-phase digital low-pass filtering is proposed, and then the accuracy and validity of this method have been proved by the simulation results. The analysis results of the mechanical fault signals show that the weak features of these signals can be efficiently extracted with the proposed approach.

Qin, Yi; Qin, Shuren; Mao, Yongfang

2008-11-01

210

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

NASA Astrophysics Data System (ADS)

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.

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

2015-02-01

211

Research into a distributed fault diagnosis system and its application  

NASA Astrophysics Data System (ADS)

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

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

2005-12-01

212

A data structure and algorithm for fault diagnosis  

NASA Technical Reports Server (NTRS)

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

Bosworth, Edward L., Jr.

1987-01-01

213

Application of Random Forest Algorithm in Machine Fault Diagnosis  

Microsoft Academic Search

The purpose of this paper is to present a methodology by which rotating machinery faults can be diagnosed. The proposed method\\u000a is based on random forests algorithm (RF), a novel assemble classifier which builds a large amount of decision trees to improve\\u000a on the single tree classifier. Although there are several existed techniques for faults diagnosis, such as artificial neural

Xiao Di Tian Han; Bo-Suk Yang; Soo-Jong Lee

214

Simulation-based testability analysis and fault diagnosis  

Microsoft Academic Search

In the past, system-level testability analysis and fault diagnosis have been largely based an either structure or manual cause-effect analysis (e.g. qualitative models, dependency models, multi-signal models, signed directed graphs). This process is time-consuming. This paper proposes a general methodology for automatically extracting multi-signal diagnostic inference models of systems via fault-simulation of design descriptions. Specifically it addresses the problem of

Sujoy Sen; Sulakshana S. Nath; Venkata N. Malepati; Krishna R. Pattipati

1996-01-01

215

Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis  

NASA Astrophysics Data System (ADS)

Based on the traditional theory of singular value decomposition (SVD), singular values (SVs) and ratios of neighboring singular values (NSVRs) are introduced to the feature extraction of vibration signals. The proposed feature extraction method is called SV-NSVR. Combined with selected SV-NSVR features, continuous hidden Markov model (CHMM) is used to realize the automatic classification. Then the SV-NSVR and CHMM based method is applied in fault diagnosis and performance assessment of rolling element bearings. The simulation and experimental results show that this method has a higher accuracy for the bearing fault diagnosis compared with those using other SVD features, and it is effective for the performance assessment of rolling element bearings.

Jiang, Huiming; Chen, Jin; Dong, Guangming; Liu, Tao; Chen, Gang

2015-02-01

216

New Informative Features for Fault Diagnosis of Industrial Systems by Supervised Classification  

E-print Network

New Informative Features for Fault Diagnosis of Industrial Systems by Supervised Classification a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised and Diagnosis (FDD) ([1]). The goal of a FDD scheme is to detect, the earliest possible, when a fault occurs

Paris-Sud XI, Université de

217

Max-Product Algorithms for the Generalized Multiple Fault Diagnosis Problem  

E-print Network

Max-Product Algorithms for the Generalized Multiple Fault Diagnosis Problem Tung Le to the generalized multiple fault diagnosis (GMFD) problem. The GMFD is described by a set of com- ponents (or diagnosis with respect to the MAP solution. Index Terms-- Multiple fault diagnosis, belief propagation, max

Hadjicostis, Christoforos

218

Fault prediction and diagnosis in large analog circuit networks  

SciTech Connect

Electronic circuits and systems have become so versatile and useful that their testing becomes increasingly. In this dissertation, the fault prediction problem is initiated and a fault prediction algorithm is presented. For the case that the parameters of potential faulty components are assumed to change gradually during each maintenance period, by continuously monitoring the responses of the network, the proposed algorithm can precisely predict whether any of the network components are about to fail. In order to apply the proposed algorithms to large circuit networks with reasonably high speed, a decomposition approach fault prediction algorithm is proposed. The approach can be used hierarchically to decompose a network into any desired level to predict and diagnose faulty subnetworks. Due to technical limitation, it is difficult to provide proportionately more accessible terminals for testing purpose in large circuit networks. To deal with this problem, an analog build-in self-test (ABIST) structure is proposed which can provide more test points while still keeping low pin overhead and acquire test data at various test points simultaneously. It is the first analog BIST structure ever proposed for analog fault diagnosis. In order to properly design diagnosable networks, an efficient algorithm is developed to select an appropriate minimum set of test points. In summary, this dissertation focuses on fault prediction and diagnosis. The proposed decomposition approach, ABIST structure and diagnosability design provide a useful means for fault prediction and diagnosis in large analog circuit networks.

Jiang, B.

1989-01-01

219

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

Microsoft Academic Search

\\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

Guifeng Jia; Shengfa Yuan; Chengwen Tang

2010-01-01

220

Fault diagnosis for a class of rearrangeable networks  

SciTech Connect

Two methods for fault diagnosis on a class of rearrangeable networks using the same fault model for blocking multistage networks are developed. The first method uses minimal two-bit test vectors and uniform switch settings in two phases to detect and locate a fault. In most cases, the number of tests needed is independent of the network size. The second method uses (n + 1)-bit test vectors and nonuniform switch settings in two test phases. Speedup of the process is obtained in many cases. Again, the number of tests needed is independent of the network size in most cases. A combination of these two methods is proposed to achieve greater efficiency.

Feng, T.Y.; Young, W.

1986-03-01

221

Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis  

NASA Astrophysics Data System (ADS)

Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.

Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.

2009-04-01

222

Comparative investigation of diagnosis media for induction machine mechanical unbalance fault.  

PubMed

For an induction machine, we suggest a theoretical development of the mechanical unbalance effect on the analytical expressions of radial vibration and stator current. Related spectra are described and characteristic defect frequencies are determined. Moreover, the stray flux expressions are developed for both axial and radial sensor coil positions and a substitute diagnosis technique is proposed. In addition, the load torque effect on the detection efficiency of these diagnosis media is discussed and a comparative investigation is performed. The decisive factor of comparison is the fault sensitivity. Experimental results show that spectral analysis of the axial stray flux can be an alternative solution to cover effectiveness limitation of the traditional stator current technique and to substitute the classical vibration practice. PMID:23938005

Salah, Mohamed; Bacha, Khmais; Chaari, Abdelkader

2013-11-01

223

Application of classification functions to chiller fault detection and diagnosis  

SciTech Connect

This paper describes the application of a statistical pattern recognition algorithm (SPRA) to fault detection and diagnosis of commercial reciprocating chillers. The developed fault detection and diagnosis module has been trained to recognize five distinct conditions, namely, normal operation, refrigerant leak, restriction in the liquid refrigerant line, and restrictions in the water circuits of the evaporator and condenser. The algorithm used in the development is described, and the results of its application to an experimental test bench are discussed. Experimental results show that the SPRA provides an effective way of classifying patterns in multivariable, multiclass problems without having to explicitly use a rule-based system.

Stylianou, M. [EDRL-CANMET, Varennes, Quebec (Canada)

1997-12-31

224

A Probabilistic Model Approach for Fault Diagnosis  

Microsoft Academic Search

This paper introduces a novel approach for fault diag- nosis based on probabilistic models. This approach is suitable for applications where reliable measurements are unlikely to occur or where a deterministic analyt- ical model is difficult to obtain. In particular, a com- bination of two Bayesian networks is used to detect and isolate faulty components. One Bayesian network, representing a

Pablo H. Ibarguengoytia; L. Enrique Sucar; Eduardo Morales

2000-01-01

225

Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine  

NASA Technical Reports Server (NTRS)

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

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

1992-01-01

226

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

NASA Astrophysics Data System (ADS)

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.

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

2011-07-01

227

GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER  

Microsoft Academic Search

Vibration signals from a gearbox are usually noisy. As a result, it is difficult to find early symptoms of a potential failure in a gearbox. Wavelet transform is a powerful tool to disclose transient information in signals. An adaptive wavelet filter based on Morlet wavelet is introduced in this paper. The parameters in the Morlet wavelet function are optimised based

J. Lin; M. J. ZUO

2003-01-01

228

Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.  

PubMed

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

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

2014-02-01

229

Display interface concepts for automated fault diagnosis  

NASA Technical Reports Server (NTRS)

An effort which investigated concepts for displaying dynamic system status and fault history (propagation) information to the flight crew is described. This investigation was performed by developing several candidate display formats and then conducting comprehension tests to determine those characteristics that made one format preferable to another for presenting this type of information. Twelve subjects participated. Flash tests, or limited time exposure tests, were used to determine the subjects' comprehension of the information presented in the display formats. It was concluded from the results of the comprehension tests that pictographs were more comprehensible than both block diagrams and text for presenting dynamic system status and fault history information, and that pictographs were preferred over both block diagrams and text. It was also concluded that the addition of this type of information in the cockpit would help the crew remain aware of the status of their aircraft.

Palmer, Michael T.

1989-01-01

230

Representation of device knowledge for versatile fault diagnosis  

SciTech Connect

Diagnosis in the circuit domain is the task of localizing a fault to a specific component or connection. Based on the requirements of expressibility, buildability, computer-usability, and expandability, a device in the circuit domain is modeled as a hierarchically arranged set of subparts from both logical and physical perspectives. A new mechanism for connecting components is developed to preserve the special features of wires and points of contact so as to make the common connection problems diagnosable. The idea of an expandable component library is introduced. The work leads to a device-representation formalism, which not only provides the system with necessary knowledge for diagnosing a wide range of faults,but also makes the system adaptable to new devices, e.g., different circuits in the electronic domain. The representation scheme was used to represents a six-channel pulse-code modulation board for telecommunications and several adder/multiplier boards in a fault-diagnosis system, which successfully locates the faults on these devices. The research results in a better understanding of knowledge-representation issues in versatile fault diagnosis, and provides a prototype of developing device representation schemes for such systems in both circuit and non-circuit domains.

Taie, M.R.

1987-01-01

231

Multiscale envelope manifold for enhanced fault diagnosis of rotating machines  

NASA Astrophysics Data System (ADS)

The wavelet transform has been widely used in the field of machinery fault diagnosis for its good property of band-pass filtering. However, the filtered signal still faces the contamination of in-band noise. This paper focuses on wavelet enveloping, and proposes a new method, called multiscale envelope manifold (MEM), to extract the envelope information of fault impacts with in-band noise suppression. The MEM addresses manifold learning on the wavelet envelopes at multiple scales. Specifically, the proposed method is conducted by three following steps. First, the continuous wavelet transform (CWT) with complex Morlet wavelet base is introduced to obtain the wavelet envelopes at all scales. Second, the wavelet envelopes are restricted in one or more narrow scale bands to simply include the envelope information of fault impacts. The scale band is determined through a smoothness index-based (SI-based) selection method by considering the impulsiveness inside the power spectrum. Third, the manifold learning algorithm is conducted on the wavelet envelopes at selected scales to extract the intrinsic envelope manifold of fault-related impulses. The MEM combines the envelope information at multiple scales in a nonlinear approach, and may thus preserve the factual envelope structure of machinery fault. Simulation studies and experimental verifications confirm that the new method is effective for enhanced fault diagnosis of rotating machines.

Wang, Jun; He, Qingbo; Kong, Fanrang

2015-02-01

232

Fault Detection of Reciprocating Compressors using a Model from Principles Component Analysis of Vibrations  

NASA Astrophysics Data System (ADS)

Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals originating from the many different impact sources and wide ranges of practical operating conditions. In this paper Principal Component Analysis (PCA) is used for selecting vibration feature and detecting different faults in a reciprocating compressor. Vibration datasets were collected from the compressor under baseline condition and five common faults: valve leakage, inter-cooler leakage, suction valve leakage, loose drive belt combined with intercooler leakage and belt loose drive belt combined with suction valve leakage. A model using five PCs has been developed using the baseline data sets and the presence of faults can be detected by comparing the T2 and Q values from the features of fault vibration signals with corresponding thresholds developed from baseline data. However, the Q -statistic procedure produces a better detection as it can separate the five faults completely.

Ahmed, M.; Gu, F.; Ball, A. D.

2012-05-01

233

Kernel orthogonal local fisher discrimination for rotor fault diagnosis  

NASA Astrophysics Data System (ADS)

In order to better identify the fault of rotor system, one new method based on kernel orthogonal local fisher discriminant (KOLFD) is proposed.Considering kernel mapping and iteration-orthgonal idea,training data with supervision information was mapped to kernel space, computed local with-class scatter and between-class scatter, constructed kernel fisher discriminant function. To ensure the minimum reconstruction error during deimensionality reduction, algorithm joined the orthonormal constraints condition,found optimal basic projection vector by iterative orthogonal approach.Then testing data was mapped by this vector and got new data's class information by neighbor classifier,and eventually realize fault diagnosis.The experiment of rotor fault diagnosis shows, KOLFD algorithm has better effect to other manifold learning algorithm.

Wang, Guangbin; Huang, Liangpei

2010-08-01

234

Optimal sensor placement for fixture fault diagnosis using Bayesian network  

Microsoft Academic Search

Purpose – Fixture failures are the main cause of the dimensional variation in the assembly process. The purpose of this paper is to focus on the optimal sensor placement of compliant sheet metal parts for the fixture fault diagnosis. Design\\/methodology\\/approach – Based on the initial sensor locations and measurement data in launch time of the assembly process, the Bayesian network

Yinhua Liu; Sun Jin; Zhongqin Lin; Cheng Zheng; Kuigang Yu

2011-01-01

235

Rolling element bearing fault diagnosis using wavelet transform  

Microsoft Academic Search

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the

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

2011-01-01

236

Intelligent alarm processing and fault diagnosis in digital substations  

Microsoft Academic Search

For the present situation of the traditional substation has many drawbacks, a system of intelligent alarm and fault diagnosis for transmission and transformation equipments in the digital substation is proposed in this work based on the multi-agents structure. According to the architecture of the digital substation, the characteristic of information flow and data flow, the accident handling process, the layered

Jianbo Xin; Zhiwei Liao; Fushuan Wen

2010-01-01

237

An integrated process for system maintenance, fault diagnosis and support  

Microsoft Academic Search

This paper presents an overview of an integrated process for system maintenance, fault diagnosis and support. The solution is based on Qualtech Systems, Inc.'s (QSI's) TEAMS toolset for integrated diagnostics and involves several key innovations. As a showcase of the integrated solution, QSI, along with Antech Systems and Carnegie Mellon University (CMU), have recently completed a research project for the

S. Ghoshal; R. Shrestha; A. Ghoshal; V. Malepati; S. Deb; K. Patripati; D. Kleinman

1999-01-01

238

Fault diagnosis model based on Gaussian support vector classifier machine  

Microsoft Academic Search

In view of the bad diagnosing capability of standard support vector classifier machine (SVC) for fault diagnosis pattern series with Gaussian noises, Gaussian function is used as loss function of SVC and a new SVC based on Gaussian loss function technique, by name g-SVC, is proposed. To seek the optimal parameter combination of g-SVC, particle swarm optimization (PSO) is proposed.

Qi Wu

2010-01-01

239

Dynamic Observers for Fault Diagnosis of Timed Systems  

E-print Network

In this paper we extend the work on \\emph{dynamic ob\\-servers} for fault diagnosis to timed automata. We study sensor minimization problems with static observers and then address the problem of computing the most permissive dynamic observer for a system given by a timed automaton.

Cassez, Franck

2010-01-01

240

Actuator fault diagnosis: an adaptive observer-based technique  

Microsoft Academic Search

This paper presents a novel approach for the fault diagnosis of actuators in known deterministic dynamic systems by using an adaptive observer technique. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. Under the assumption that the system state observer can be

H. Wang; S. Daley

1996-01-01

241

Fault diagnosis and prediction on uninterruptible power systems  

Microsoft Academic Search

The authors deal with a fault diagnosis technique, particularly suitable for power electrical devices, based on the integration of simulation and identification methods. The device under analysis was simulated in faultless and faulty conditions and the experimental validation was carried out. Both the system parametric \\

A. Bernieri; G. Betta; C. De Capua; A. Pietrosanto

1993-01-01

242

The Fault Diagnosis of Aircraft Generator using Fuzzy Petri Nets  

Microsoft Academic Search

Aircraft generator is the power conversion equipment transforming the engine energy to electric energy for all electro-equipment in airplane. Its right working state is a key factor to ensure airplane normal and secure. So fault diagnosis is the main study field of aircraft electric system. In the paper, the fuzzy Petri nets reasoning production rules is investigated and developed to

Ping Xu; Qishuang Ma

2010-01-01

243

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

E-print Network

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

Koutsoukos, Xenofon D.

244

A study on SVM with feature selection for fault diagnosis of power systems  

Microsoft Academic Search

When faults occur in power systems, it is hard to manually deal with the fault data reported by the system of supervisory control and data acquisition (SCADA) because of the huge amount of alarm information. In this paper, we study the problem of power system fault diagnosis by using support vector machine (SVM), and enhance the ability of fault diagnosis

Yufei Wang; Chunguo Wu; Liming Wan; Yanchun Liang

2010-01-01

245

FAULT DIAGNOSIS IN NONLINEAR SYSTEMS THROUGH AN ADAPTIVE FILTER UNDER A CONVEX SET  

E-print Network

FAULT DIAGNOSIS IN NONLINEAR SYSTEMS THROUGH AN ADAPTIVE FILTER UNDER A CONVEX SET REPRESENTATION M that performs fault detection, isolation and estimation for a large class of nonlinear systems. Fault diagnosis: medina@cran.uhp-nancy.fr Keywords: Nonlinear system, multiple linear models, fault detection

Paris-Sud XI, Université de

246

What Stator Current Processing Based Technique to Use for Induction Motor Rotor Faults Diagnosis?  

E-print Network

What Stator Current Processing Based Technique to Use for Induction Motor Rotor Faults Diagnosis fault detection. Index Terms--Induction motor, rotor fault diagnosis, stator current. I. INTRODUCTION in induction motors. Features of these techniques which are relevant to fault detection are presented

Paris-Sud XI, Université de

247

Experimental investigation for fault diagnosis based on a hybrid approach using wavelet packet and support vector classification.  

PubMed

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples. PMID:24688361

Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

2014-01-01

248

Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification  

PubMed Central

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples. PMID:24688361

Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

2014-01-01

249

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

NASA Astrophysics Data System (ADS)

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.

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

2015-01-01

250

Fault-diagnosis in a multiple-path interconnection network  

SciTech Connect

While a multiple-path interconnection network is capable of tolerating any single fault, the knowledge of the fault's location is required before it can adapt itself. An approach of on-line single-fault detection is given. Based on a new fault-model, a system-wide diagnostic procedure is developed to effectively detect and locate a single fault throughout a fault-tolerant network as that proposed by Tzeng, Yew, and Zhu. The model is realistic and has potential usefulness as a tool for modeling faulty states of larger switching elements (e.g., n x n switching elements, n > 2). Networks under diagnosis behave in a distributed control manner, i.e., a tag needed for establishing a path is conveyed by the same resources (switching elements and links) as those for transmitting data. Test vectors for appropriately setting switching elements when the procedure is conducted are presented. Faults are classified into two different groups each of which is dealt with separately to ease our diagnostic procedure. 20 refs., 8 figs.

Tzeng, N.F.; Yew, P.C.; Zhu, C.Q.

1985-11-24

251

Multiple faults diagnosis using causal graph  

E-print Network

This work proposes to put up a tool for diagnosing multi faults based on model using techniques of detection and localization inspired from the community of artificial intelligence and that of automatic. The diagnostic procedure to be integrated into the supervisory system must therefore be provided with explanatory features. Techniques based on causal reasoning are a pertinent approach for this purpose. Bond graph modeling is used to describe the cause effect relationship between process variables. Experimental results are presented and discussed in order to compare performance of causal graph technique and classic methods inspired from artificial intelligence (DX) and control theory (FDI).

Fliss, Imtiez

2012-01-01

252

Sensor fault diagnosis using Bayesian belief networks  

SciTech Connect

This paper describes a method based on Bayesian belief networks (BBNs) sensor fault detection, isolation, classification, and accommodation (SFDIA). For this purpose, a BBN uses three basic types of nodes to represent the information associated with each sensor: (1) sensor-reading nodes that represent the mechanisms by which the information is communicated to the BBN, (2) sensor-status nodes that convey the status of the corresponding sensors at any given time, and (3) process-variable nodes that are a conceptual representation of the actual values of the process variables, which are unknown.

Aradhye, H.B.; Heger, A.S. [Univ. of New Mexico, Albuquerque, NM (United States)

1997-12-01

253

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

NASA Astrophysics Data System (ADS)

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.

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

2011-10-01

254

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

PubMed Central

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

Wang, Huaqing; Chen, Peng

2009-01-01

255

Typical fault diagnosis method for high-speed turbopump of Liquid Rocket Engine  

Microsoft Academic Search

Turbopump is a high-fault-rate component in Liquid Rocket Engine (LRE). In this paper, the reasons of the typical fault of turbopump rotor blade abruption and abscission are analyzed. And, by the method of dynamic analysis, the vibration features of the fault are studied to select the frequency features diagnosing effectively the fault. Then, the extraction method of the features is

Lurui Xia; Niaoqing Hu; Guojun Qin; Ming Gao

2008-01-01

256

A distributed expert system for fault diagnosis  

Microsoft Academic Search

The authors describe 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 on a network

E. Cardozo; S. N. Talukdar

1988-01-01

257

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

E-print Network

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

Tolbert, Leon M.

258

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

E-print Network

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

Tolbert, Leon M.

259

Performance Analysis of Sum-Product Algorithms for Multiple Fault Diagnosis Applications  

E-print Network

Performance Analysis of Sum-Product Algorithms for Multiple Fault Diagnosis Applications Tung Le of sum- product algorithms (SPAs) to multiple fault diagnosis (MFD) problems in order to diagnose are very tight and significantly improve existing results. We also show that fault diagnosis based on SPA

Tatikonda, Sekhar

260

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

E-print Network

Diagnosis Souad Bezzaoucha, Benoît Marx, Didier Maquin, José Ragot Abstract-- This paper addresses fault performances, safety and reliability, fault diagnosis for uncertain systems with Unknown Input (UI) has) is identified. Different schemes of bank observers can be used for fault diagnosis (Dedicated Observer Scheme

Paris-Sud XI, Université de

261

Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems: A Review  

E-print Network

Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems: A Review Y. Amirat, M of condition monitoring and fault diagnosis in WECS (blades, drive trains, and generators); and keeping in mind. Index Terms--Wind turbine, induction generator, drive train, condition monitoring, fault diagnosis. I

Paris-Sud XI, Université de

262

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

E-print Network

An innovations approach to fault diagnosis in linear time-varying descriptor systems* Abdouramane Moussa Ali1, 2 and Qinghua Zhang3 Abstract-- In this paper fault diagnosis is studied for linear time its properties that are essential for the purpose of fault diagnosis. Based on the analysis

Paris-Sud XI, Université de

263

A QUALITATIVE EVENT-BASED APPROACH TO FAULT DIAGNOSIS OF HYBRID SYSTEMS  

E-print Network

A QUALITATIVE EVENT-BASED APPROACH TO FAULT DIAGNOSIS OF HYBRID SYSTEMS By Matthew J. Daigle and multiple faults, (iii) developing an integrated framework for diagnosis of parametric, sensor, and discrete of the approach is demonstrated on two practical systems. First, the single fault diagnosis method for continuous

Daigle, Matthew

264

Dynamic structure identification of Bayesian network model for fault diagnosis of FMS  

E-print Network

Dynamic structure identification of Bayesian network model for fault diagnosis of FMS Dang Trinh of the equipment in production flow or product itself. Index terms--Fault diagnosis, Flexible Manufacturing Systems to control, di- agnose and recover the abnormal events in timely manner. The fault detection and diagnosis

265

STOCHASTIC OBSERVABILITY AND FAULT DIAGNOSIS OF ADDITIVE CHANGES IN STATE SPACE MODELS  

E-print Network

STOCHASTIC OBSERVABILITY AND FAULT DIAGNOSIS OF ADDITIVE CHANGES IN STATE SPACE MODELS Fredrik for a new approach to fault detection and diagnosis, where the state estimate from past data is com- pared in a sim- ple way. For fault estimation in the diagnosis, the general concept of stochastic observability

Gustafsson, Fredrik

266

A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems  

E-print Network

A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems to the importance of condition monitoring and fault diagnosis in WECS (blades, drive trains, and generators--Wind turbine, induction generator, drive train, condition monitoring, fault diagnosis. Y. Amirat1 , M

Paris-Sud XI, Université de

267

Compact Dictionaries for Fault Diagnosis in BIST Chunsheng Liu and Krishnendu Chakrabarty  

E-print Network

Compact Dictionaries for Fault Diagnosis in BIST Chunsheng Liu and Krishnendu Chakrabarty no loss in diagnostic resolution. 1 Introduction Fault diagnosis is necessary for the identification of manufacturing defects and for yield learning. One approach to diagnosis is based on the use of fault

Chakrabarty, Krishnendu

268

MULTIPLE FAULT DIAGNOSIS OF ANALOG CIRCUITS BY LOCATING AMBIGUITY GROUPS OF TEST EQUATION  

E-print Network

MULTIPLE FAULT DIAGNOSIS OF ANALOG CIRCUITS BY LOCATING AMBIGUITY GROUPS OF TEST EQUATION J. A circuit. 1. INTRODUCTION Fault diagnosis is an important problem of analog circuit testing. Due circuits such as component tolerance and nonlinearity, the automation level of analog fault diagnosis has

Starzyk, Janusz A.

269

Fault Diagnosis of Civil Engineering Structures using the Bond Graph Abbas Moustafa  

E-print Network

Fault Diagnosis of Civil Engineering Structures using the Bond Graph Approach Abbas Moustafa.mahadevan, xeno- fon.koutsoukos}@vanderbilt.edu Abstract This paper develops a fault diagnosis methodology illustrations of fault diagnosis of a frame struc- ture driven by time-varying loads are provided. Introduction

Roychoudhury, Indranil

270

Fault Detection and Diagnosis in TurbineEngines using Fuzzy Logic  

E-print Network

Fault Detection and Diagnosis in TurbineEngines using Fuzzy Logic Dennice Gayme Sunil Menon Charles.Mukavetz @honevwell.com Abstract In thispaper, wepresent ajiazy Iogic basedmethod of fault detection and diagnosis fault detection and diagnosis (FDD) is vitally important to reducing airline operating costs

Gayme, Dennice

271

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

E-print Network

Testing and Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures Ian G. Harris and diagnosis. This technique enables the detection of bridging faults involving intra-cluster interconnect for rapid, partial device reconfiguration [4] [3]. Our fault test and diagnosis approach is driven by recent

Harris, Ian G.

272

Lossy Electric Transmission Line Soft Fault Diagnosis: an Inverse Scattering Approach  

E-print Network

1 Lossy Electric Transmission Line Soft Fault Diagnosis: an Inverse Scattering Approach Huaibin Tang and Qinghua Zhang Abstract--In this paper, the diagnosis of soft faults in lossy electric lines to confirm the feasibility of the studied approach to soft fault diagnosis. Index Terms

Paris-Sud XI, Université de

273

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

E-print Network

A Hierarchical Fault Diagnosis Method Using a Decision Support System Applied to a Chemical Plant D. In order to improve the automatic process control, it is important to develop fault diagnosis strategy. For fault diagnosis, a knowledge based procedure is required. In addition to analytic symptoms, heuristic

Paris-Sud XI, Université de

274

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 is constrained by limitations with respect to fault isolation, bandwidth, wire length, namespaces and diagnosis factor for ef- fective diagnosis. Without fault isolation, the tracing of experienced errors back

275

Robust H Fault Diagnosis for Multi-Model Descriptor Systems: A Multi-Objective Approach  

E-print Network

Robust H Fault Diagnosis for Multi-Model Descriptor Systems: A Multi-Objective Approach Hamdi Habib Observer. The stability and robustness properties of the fault diagnosis scheme are investigated in term of LMIs. A simulation example illustrating the ability of the proposed fault diagnosis architecture

Paris-Sud XI, Université de

276

A new model-free method performing closed-loop fault diagnosis  

E-print Network

A new model-free method performing closed-loop fault diagnosis for an aeronautical system Julien), CNRS-SUPELEC-Univ Paris-Sud, France, eric.walter@lss.supelec.fr Abstract: Fault diagnosis for a closed detection and isolation, fault diagnosis, guidance and control. 1. INTRODUCTION In-flight securement

Paris-Sud XI, Université de

277

1 Copyright 2007 by ASME A ROLE OF UNSUPERVISED CLUSTERING FOR INTELLIGENT FAULT DIAGNOSIS  

E-print Network

1 Copyright © 2007 by ASME A ROLE OF UNSUPERVISED CLUSTERING FOR INTELLIGENT FAULT DIAGNOSIS Datta, we have taken the example of an intelligent decision making process in the field of fault diagnosis attempt to use these models in the field of health monitoring as tool for fault diagnosis and state

Mavroidis, Constantinos

278

Fault Modeling for Monitoring and Diagnosis of Sensor-Rich Hybrid Systems  

E-print Network

Fault Modeling for Monitoring and Diagnosis of Sensor-Rich Hybrid Systems Xenofon Koutsoukos Feng- toring and diagnosis of real-time embedded systems. We describe a fault parameterization based on hybrid and fault diagnosis in a document processing factory (or print shop) consisting of multiple printing

Koutsoukos, Xenofon D.

279

Multiple Fault Diagnosis of Analog Circuits Based on Large Change Sensitivity Analysis  

E-print Network

Multiple Fault Diagnosis of Analog Circuits Based on Large Change Sensitivity Analysis Janusz A. Starzyk* and Dong Liu* Abstract - A new method is proposed in this paper for multiple fault diagnosis to illustrate the proposed method. 1 Introduction Fault diagnosis of analog circuits usually consists of three

Starzyk, Janusz A.

280

Distance Rejection in a Bayesian Network for Fault Diagnosis of Industrial Systems  

E-print Network

Distance Rejection in a Bayesian Network for Fault Diagnosis of Industrial Systems Sylvain VERRON in an observed out-of-control status); fault diagnosis (find the root cause of the disturbance); process recovery of this article is to present a new method for the diagnosis of faults in large industrial systems. This method

Paris-Sud XI, Université de

281

Transients Analysis of a Nuclear Power Plant Component for Fault Diagnosis  

E-print Network

Transients Analysis of a Nuclear Power Plant Component for Fault Diagnosis Piero Baraldia) turbine for fault diagnosis. The aim is to identify groups of transients with similar characteristics of the measured signal transients. The fault diagnosis method is based on the combined use of an original fuzzy

Paris-Sud XI, Université de

282

Improved Methods for Fault Diagnosis in Scan-Based BIST Ismet Bayraktaroglu  

E-print Network

Improved Methods for Fault Diagnosis in Scan-Based BIST £ Ismet Bayraktaroglu Computer Science for fault diagnosis in Scan-Based BIST is proposed. The incorporation of the superposition principle benefits, a limitation in its further adop- tion as the main test methodology is inherent fault diagnosis

Bayraktaroglu, Ismet

283

A review of process fault detection and diagnosis: Part I: Quantitative model-based methods  

Microsoft Academic Search

Fault detection and diagnosis is an important problem in process engineering. It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently. AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process. Early detection and diagnosis of process faults while the plant is still operating in

Venkat Venkatasubramanian; Raghunathan Rengaswamy; Kewen Yin; Surya N. Kavuri

2003-01-01

284

Adaptive observer based fault diagnosis applied to differential-algebraic systems  

E-print Network

Adaptive observer based fault diagnosis applied to differential-algebraic systems Abdouramane equations (ODE). This paper proposes an approach to fault diagnosis for systems described by DAEs. Through parameters. As an illustrative example, the diagnosis of faults in a heat exchanger modeled by nonlinear DAEs

Paris-Sud XI, Université de

285

A knowledge base system for rotary equipment fault detection and diagnosis  

Microsoft Academic Search

This paper studies the fault detection and diagnosis for the most common faults in the rotary equipment. Large amount of experiments are carried out on the machinery fault simulator for simulating different types of rotary machine faults. The study covers from different type of data acquisition sensors, different signal processing and feature extraction techniques. A hierarchical rule-based fault detection system

Junhong Zhou; Louis Wee; Zhao-Wei Zhong

2010-01-01

286

APPLICATION OF WAVELET PACKET ANALYSIS FOR FAULT DETECTION IN ELECTROMECHANICAL SYSTEMS BASED ON TORSIONAL VIBRATION MEASUREMENT  

Microsoft Academic Search

This paper primarily focuses on detecting electrical faults in turbine generator sets by monitoring torsional vibrations with the help of the non-contact measurement technique and analysing the data acquired from torsional vibration meter. Torsional vibrations in shaft trains can be excited by periodic excitation due to a variety of electromagnetic disturbances or unsteady flow in large steam turbine generator sets

X. Li; L. Qu; G. Wen; C. Li

2003-01-01

287

Digraph and fault-tree technique for online process diagnosis  

SciTech Connect

Available diagnostic systems and existing diagnostic techniques illustrate the need for a comprehensive, model-based theory for on-line process diagnosis. This thesis classifies the diagnostic task into two functions, problem recognition and fault detection. The thesis hypothesizes the need for goal-directed, causal models to perform these functions in chemical process systems. A diagnostic theory is developed comprising goal-directed, causal models and a method to use these models to perform the diagnostic functions. Digraph and fault trees form the causal model base. The fault tree's primal events are verified with available on-line indications using probabilistic theory and the digraph's causal structure. Problem recognition is disclosed through monitoring the on-line unreliability of the process goal. Fault detection is accomplished through ranking the failure rates of potential process failures using on-line indications and design data. The theory provides a basis for performing comprehensive, robust system diagnosis. Examples illustrate the technique's ability to diagnose single and multiple system faults including equipment failures, disturbances, and indicator failures in linear and control loop systems. Limitations to application of the theory are discussed.

Ulerich, N.H.

1989-01-01

288

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

NASA Astrophysics Data System (ADS)

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.

Wang, Jun; He, Qingbo

2014-05-01

289

A distributed expert system for fault diagnosis  

SciTech Connect

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.

Cardozo, E.; Talukdar, S.N.

1988-05-01

290

Vibrations of balanced fault-free ball bearings  

NASA Astrophysics Data System (ADS)

This paper investigates the vibrations of balanced fault-free ball bearings. A lumped mass-damper-spring model is adopted including the use of the Hertzian contact theory to represent the stiffness of the bearing rolling elements. We found that the equilibrium point of the bearing undergoes a supercritical pitchfork bifurcation as the bearing internal clearance increases. We developed closed-form expressions for the frequency-response functions of the horizontal and vertical motions of bearings with small internal clearance (below the bifurcation point). We also developed a chaos map to describe the locations and intensity of chaos in the internal clearance-shaft speed parameter space for bearings with larger internal clearance (beyond the bifurcation point).

Ghafari, S. H.; Abdel-Rahman, E. M.; Golnaraghi, F.; Ismail, F.

2010-04-01

291

Fault Diagnosis of Power Systems Using Intelligent Systems  

NASA Technical Reports Server (NTRS)

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.

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

1996-01-01

292

Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors  

NASA Astrophysics Data System (ADS)

Gearbox fault diagnosis is very important for preventing catastrophic accidents. Vibration signals of gearboxes measured by sensors are useful and dependable as they carry key information related to the mechanical faults in gearboxes. Effective signal processing techniques are in necessary demands to extract the fault features contained in the collected gearbox vibration signals. Overcomplete rational dilation discrete wavelet transform (ORDWT) enjoys attractive properties such as better shift-invariance, adjustable time-frequency distributions and flexible wavelet atoms of tunable oscillation in comparison with classical dyadic wavelet transform (DWT). Due to these advantages, ORDWT is presented as a versatile tool that can be adapted to analysis of gearbox fault features of different types, especially in analyzing the non-stationary and transient characteristics of the signals. Aiming to extract the various types of fault features confronted in gearbox fault diagnosis, a fault feature extraction technique based on ORDWT is proposed in this paper. In the routine of the proposed technique, ORDWT is used as the pre-processing decomposition tool, and a corresponding post-processing method is combined with ORDWT to extract the fault feature of a specific type. For extracting periodical impulses in the signal, an impulse matching algorithm is presented. In this algorithm, ORDWT bases of varied time-frequency distributions and varied oscillatory natures are adopted, moreover an improved signal impulsiveness measure derived from kurtosis is developed for choosing optimal ORDWT bases that perfectly match the hidden periodical impulses. For demodulation purpose, an improved instantaneous time-frequency spectrum (ITFS), based on the combination of ORDWT and Hilbert transform, is presented. For signal denoising applications, ORDWT is enhanced by neighboring coefficient shrinkage strategy as well as subband selection step to reveal the buried transient vibration contents. The proposed fault feature extraction technique is applied in a range of engineering applications, and the processing results demonstrate that the ORDWT-based feature extraction technique successfully identifies the incipient fault features in the cases where DWT and empirical mode decomposition method are less effective.

Chen, Binqiang; Zhang, Zhousuo; Sun, Chuang; Li, Bing; Zi, Yanyang; He, Zhengjia

2012-11-01

293

Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants  

Microsoft Academic Search

The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory

Anand P. Narayan

1998-01-01

294

A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix  

NASA Astrophysics Data System (ADS)

Vibration signal analysis is the most widely used technique in condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for rolling element bearing fault diagnosis based on near-field acoustic holography (NAH) and gray level co-occurrence matrix (GLCM) is presented in this paper. It focuses on applying the distribution information of sound field to bearing fault diagnosis. A series of rolling element bearings with different types of fault are experimentally studied. Sound fields and corresponding acoustic images in different bearing conditions are obtained by fast Fourier transform (FFT) based NAH. GLCM features are extracted for capturing fault pattern information underlying sound fields. The optimal feature subset selected by improved F-score is fed into multi-class support vector machine (SVM) for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with the traditional ABD method. Considering test cost, the quantized level and the number of GLCM features for each characteristic frequency is suggested to be 4 and 32, respectively, with the satisfactory accuracy rate 97.5%.

Lu, Wenbo; Jiang, Weikang; Wu, Haijun; Hou, Junjian

2012-07-01

295

Fault indications in MV-3B vibrator power supply  

SciTech Connect

In accordance with standard requirements in hydroelectric power plants, the vibration conditions of electric generators is regularly inspected. A vibration sensor is described for field measurement of the generator vibration. The performance of the vibration testing unit is discussed.

Markovskii, Yu.G.; Minaev, E.K.; Polyakov, V.I.; Sychev, V.I.

1983-01-01

296

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

SciTech Connect

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.

Hosseini, S.H.

1989-07-01

297

Performance monitoring, fault detection, and diagnosis of reciprocating chillers  

SciTech Connect

This paper presents a methodology that uses a combination of techniques: thermodynamic modeling, pattern recognition, and expert knowledge to determine the health of a reciprocating chiller and to diagnose selected faults. The system is composed of three modules. The first one deals with the detection of faults that are more discernible when the chiller is off, such as sensor drift. The second module detects faults during start-up and deals with those related to refrigerant flow characteristics, which are generally more apparent during the transient period. Finally, the third module detects deterioration in performance followed by diagnosis when the unit is operating in a steady-state condition. The approach has been experimentally tested on one laboratory unit and results presented. It is emphasized that further data are required to establish the repeatability of the emerging patterns and validate the applicability of the approach to reciprocating chillers in general.

Stylianou, M.; Nikanpour, D. [EDRL-CANMET, Varennes, Quebec (Canada)

1996-11-01

298

Fault diagnosis in orbital refueling operations  

NASA Technical Reports Server (NTRS)

Usually, operation manuals are provided for helping astronauts during space operations. These manuals include normal and malfunction procedures. Transferring operation manual knowledge into a computerized form is not a trivial task. This knowledge is generally written by designers or operation engineers and is often quite different from the user logic. The latter is usually a compiled version of the former. Experiments are in progress to assess the user logic. HORSES (Human - Orbital Refueling System - Expert System) is an attempt to include both of these logics in the same tool. It is designed to assist astronauts during monitoring and diagnosis tasks. Basically, HORSES includes a situation recognition level coupled to an analytical diagnoser, and a meta-level working on both of the previous levels. HORSES is a good tool for modeling task models and is also more broadly useful for knowledge design. The presentation is represented by abstract and overhead visuals only.

Boy, Guy A.

1988-01-01

299

The envelope order spectrum based on generalized demodulation time-frequency analysis and its application to gear fault diagnosis  

NASA Astrophysics Data System (ADS)

The generalized demodulation time-frequency analysis is a novel signal processing method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM-FM) signals as it can decompose a multi-component signal into a set of single-component signals whose instantaneous frequencies own physical meaning. While fault occurs in gear, the vibration signals measured from gearbox would exactly display AM-FM characteristics. Therefore, targeting the modulation feature of gear vibration signal in run-ups and run-downs, a fault diagnosis method in which generalized demodulation time-frequency analysis and envelope order spectrum technique are combined is put forward and applied to the transient analysis of gear vibration signal. Firstly the multi-component vibration signal of gear is decomposed into some mono-component signals using the generalized demodulation time-frequency analysis approach; secondly the envelope analysis is performed to each single-component signal; thirdly each envelope signal is re-sampled in angle domain; finally the spectrum analysis is applied to each re-sampled signal and the corresponding envelope order spectrum can be obtained. Furthermore, the gear working condition can be identified according to the envelope order spectrum. The analysis results from the simulation and experimental signals show that the proposed algorithm was effective in gear fault diagnosis.

Cheng, Junsheng; Yang, Yu; Yu, Dejie

2010-02-01

300

Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis  

Microsoft Academic Search

Principal component analysis (PCA) is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares (DPLS) has been shown to improve fault diagnosis for

Leo H. Chiang; Evan L. Russell; Richard D. Braatz

2000-01-01

301

Partitioning of large multicomputer systems for efficient fault diagnosis  

SciTech Connect

Fault diagnosis for large VLSI multicomputer systems is considered. The multicomputer system is assumed to employ a lattice structure and it is partitoned into m subsystems. Each subystem has at least k units which can achieve one-step t-diagnosability where t is less than or equal to the diagnosability of the system without partitioning. The partitioning of a system with centralized control and a system with distributed control are considered in the PMC model as well as in the comparison model. The result shows that the partitioning allows efficient fault diagnosis in a system with a large number of units and may increase overall system diagnosability to m*t in an ideal case. 18 references.

Malek, M.; Maeng, J.

1982-01-01

302

Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight  

Microsoft Academic Search

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

Amitabh Barua; Khashayar Khorasani

2011-01-01

303

A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection  

NASA Astrophysics Data System (ADS)

A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection is developed. The coupled rotor/fuselage/vibration absorbers (bifilar type) system incorporates consistent structural, aerodynamic and inertial couplings. The aeroelastic analysis is based on finite element methods in space and time. The coupled rotor, absorbers and fuselage equations are transformed into the modal space and solved in the fixed coordinate system. A coupled trim procedure is used to solve the responses of rotor, fuselage and vibration absorber, rotor trim control and vehicle orientation simultaneously. Rotor system faults are modeled by changing blade structural, inertial and aerodynamic properties. Both adjustable and component faults, such as misadjusted trim-tab, misadjusted pitch-control rod (PCR), imbalanced mass and pitch-control bearing freeplay, are investigated. Detailed SH-60 helicopter fuselage NASTRAN model is integrated into the analysis. Validation study was performed using SH-60 helicopter flight test data. The prediction of fuselage natural frequencies show fairly large error compared to shake test data. Analytical predictions of fuselage baseline (without fault) 4/rev vibration and fault-induced 1/rev vibration and blade displacement deviations are compared with SH-60 flight test (with prescribed fault) data. The fault-induced 1/rev fuselage vibration (magnitude and phase) predicted by present analysis generally capture the trend of the flight test data, although prediction under-predicts. The large discrepancy of fault-induced 1/rev vibration magnitude at hover between prediction and flight test data partially comes from the variation of flight condition (not perfect hover) and partially due to the effect of the rotor-fuselage aerodynamic interaction (wake effect) at low speed which is not considered in the analysis. Also the differences in the phase prediction is not clear since only the magnitude and phase information were given instead of the original vibration time-history. The imbalanced mass fault causes higher 1/rev roll vibration that is insensitive to the airspeed. The misadjusted trim-tab fault induced 1/rev vertical vibration increases with airspeed. The misadjusted pitch-control rod fault causes high vibration at hover. A parametric study was conducted to identify key factors that affect the fault-induced fuselage vibration. Analysis show that elastic fuselage model and precise hub modeling (inclusion of vibration absorbers) are essential to the vibration pre diction. The analysis shows that a compound fault can be expressed as a linear combination of individual faults involved. Aircraft operational parameters, such as gross-weight; center of gravity location, flight speed, flight path and aircraft configuration, have significant impact on the fault-induced 1/rev vibration. Prediction show that there are certain patterns in the fault-induced 1/rev hub-loads. Thus measuring both fuselage vibration and hub loads may benefit rotor system fault detection.

Yang, Mao

304

Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis  

NASA Astrophysics Data System (ADS)

Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

Jiang, Li; Xuan, Jianping; Shi, Tielin

2013-12-01

305

LMD method and multi-class RWSVM of fault diagnosis for rotating machinery using condition monitoring information.  

PubMed

Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM. PMID:23881133

Liu, Zhiwen; Chen, Xuefeng; He, Zhengjia; Shen, Zhongjie

2013-01-01

306

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

Microsoft Academic Search

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

R. Isermann; P. Ballé

1997-01-01

307

Intelligent fault diagnosis system of induction motor based on transient current signal  

Microsoft Academic Search

This paper presents a method for induction motor fault diagnosis based on transient signal using component analysis and support vector machine (SVM). The start-up transient current signal is selected as features source for fault diagnosis. Preprocessing of transient current signal is performed using smoothing and discrete wavelet transform to highlight the salient features of faults. In this work, independent component

Achmad Widodo; Bo-Suk Yang; Dong-Sik Gu; Byeong-Keun Choi

2009-01-01

308

On the use of adaptive updating rules for actuator and sensor fault diagnosis  

Microsoft Academic Search

A novel approach is presented for the fault detection and diagnosis (FDD) of faults in actuators and sensors via the use of adaptive updating rules. The system considered is linear time-invariant and is subjected to an unknown input that represents either model uncertainty or unmeasurable disturbances. First, fault detection and diagnosis for linear actuators and sensors is considered, where a

Hong Wang; Zhen J. Huang; Steve Daley

1997-01-01

309

Lazy Suspect-Set Computation: Fault Diagnosis for Deep Electrical Bugs  

E-print Network

Lazy Suspect-Set Computation: Fault Diagnosis for Deep Electrical Bugs Dipanjan Sengupta Dept. Elec diagnosis. ATPG and other methods from manufacturing test explicitly consider faults, but are also test methods are highly effective at sensitiz- ing and propagating most electrical faults

Veneris, Andreas

310

Fault detection, diagnosis, and data-driven modeling in HVAC chillers  

Microsoft Academic Search

Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and

Setu M. Namburu; Jianhui Luo; Mohammad Azam; Kihoon Choi; Krishna R. Pattipati

2005-01-01

311

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

Microsoft Academic Search

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

Jinyu Zhang; Xianxiang Huang

2008-01-01

312

Design of a fault diagnosis system for next generation nuclear power plants  

SciTech Connect

A new design approach for fault diagnosis is developed for next generation nuclear power plants. In the nuclear reactor design phase, data reconciliation is used as an efficient tool to determine the measurement requirements to achieve the specified goal of fault diagnosis. In the reactor operation phase, the plant measurements are collected to estimate uncertain model parameters so that a high fidelity model can be obtained for fault diagnosis. The proposed algorithm of fault detection and isolation is able to combine the strength of first principle model based fault diagnosis and the historical data based fault diagnosis. Principal component analysis on the reconciled data is used to develop a statistical model for fault detection. The updating of the principal component model based on the most recent reconciled data is a locally linearized model around the current plant measurements, so that it is applicable to any generic nonlinear systems. The sensor fault diagnosis and process fault diagnosis are decoupled through considering the process fault diagnosis as a parameter estimation problem. The developed approach has been applied to the IRIS helical coil steam generator system to monitor the operational performance of individual steam generators. This approach is general enough to design fault diagnosis systems for the next generation nuclear power plants. (authors)

Zhao, K.; Upadhyaya, B.R. [The University of Tennessee, Nuclear Engineering Department, Knoxville, TN 37996-2300 (United States); Wood, R.T. [Oak Ridge National Laboratory, Nuclear Science and Technology Division, Oak Ridge, TN 37831-6010 (United States)

2004-07-01

313

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

E-print Network

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

Paris-Sud XI, Université de

314

Automated fault diagnosis in nonlinear multivariable systems using a learning methodology  

Microsoft Academic Search

The paper presents a robust fault diagnosis scheme for detecting and approximating state and output faults occurring in a class of nonlinear multiinput-multioutput dynamical systems. Changes in the system dynamics due to a fault are modeled as nonlinear functions of the control input and measured output variables. Both state and output faults can be modeled as slowly developing (incipient) or

Alexander B. Trunov; Marios M. Polycarpou

2000-01-01

315

DIAGNOSIS OF DYNAMITRON ACCELERATOR FAULTS THROUGH THE OBSERVATION OF NARROW NUCLEAR RESONANCES  

E-print Network

1419 DIAGNOSIS OF DYNAMITRON ACCELERATOR FAULTS THROUGH THE OBSERVATION OF NARROW NUCLEAR in discovering and identifying accelerator fault conditions. Short-term energy stability (over e few minutes often leading to the identification and correction of faults. Typical faults usually produce increased

Paris-Sud XI, Université de

316

Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants  

NASA Astrophysics Data System (ADS)

The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory in conjunction with advanced fluid mechanics to achieve the objective of developing an implementable comprehensive monitoring systems, suitable for use on space missions. First, a three-dimensional transport model is developed in order to model the dispersion of air contaminants. The flow field, which is an important input to the transport model, is obtained by solving the Navier Stokes equations for the cabin geometry and the appropriate boundary conditions, using a finite element method. Steady flow fields are computed for various conditions for both laminar and turbulent cases. Contamination dispersion studies are undertaken both for routine substances introduced through the inlet ducts and for emissions of toxics inside the cabin volume. The dispersion studies indicate that lumped models and even a two-dimensional model are sometimes inadequate to assure that the Spacecraft Maximum Allowable Concentrations (SMACs) are not exceeded locally. Since the research was targeted at real-time application aboard Spacecraft, a state estimation routine is implemented using Implicit Kalman Filtering. The routine makes use of the model predictions and measurements from the sensor system in order to arrive at an optimal estimate of the state of the system for each time step. Fault detection is accomplished through the use of analytical redundancy, where error residuals from the Kalman filter are monitored in order to detect any faults in the system, and to distinguish between sensor and process faults. Finally, a fault diagnosis system is developed, which is a combination of sensitivity analysis and an Extended Kalman Filter, which is used to estimate the location and capacity of an unknown source emission in the system. The sensitivity analysis involves pre-calculating sensitivity coefficients, which measure the response of each sensor to a source emission at each location in the cabin, and in the event of a fault, current measurements are used and inverted to arrive at an initial guess for the unknown source that is causing the fault. An Extended Implicit Kalman filter, developed especially for this application then makes use of the initial guess to arrive at an optimal estimate for the unknown source, by minimizing the squared estimation error. The fault diagnosis procedure is successfully tested for various test cases.

Narayan, Anand P.

1998-07-01

317

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

E-print Network

-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...

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

2012-01-01

318

An implementation of a hybrid intelligent tool for distribution system fault diagnosis  

SciTech Connect

The common fault in distribution systems due to line outages consists of single-line-to-ground (SLG) faults, with low or high fault impedance. The presence of arcing is commonplace in high impedance SLG faults. Recently, artificial intelligence (AI) based techniques have been introduced for low/high impedance fault diagnosis in ungrounded distribution systems and high impedance fault diagnosis in grounded distribution systems. So far no tool has been developed to identify, locate and classify faults on grounded and ungrounded systems. This paper describes an integrated package for fault diagnosis in either grounded or ungrounded distribution systems. It utilizes rule based schemes as well as artificial neural networks (ANN) to detect, classify and locate faults. Its application on sample test data as well as field test data are reported in the paper.

Momoh, J.A.; Dias, L.G. [Howard Univ., Washington, DC (United States). Dept. of Electrical Engineering] [Howard Univ., Washington, DC (United States). Dept. of Electrical Engineering; Laird, D.N. [Los Angeles Dept. of Water and Power, CA (United States)] [Los Angeles Dept. of Water and Power, CA (United States)

1997-04-01

319

Hypothetical Scenario Generator for Fault-Tolerant Diagnosis  

NASA Technical Reports Server (NTRS)

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

James, Mark

2007-01-01

320

A representation scheme for fault diagnosis of physical systems  

SciTech Connect

For the most part those of use who operate simple machines do so in a run-till-breakdown mode. But, because of safety and and economic concerns, society cannot afford to operate complex machines in this run-till-breakdown mode. An alternate to operating in this mode is the predictive-corrective mode. A principal element in the predictive-corrective mode is the determination of system behavior and the analysis of the implications of that behavior. In this paper, we discuss a representation scheme used to model and execute knowledge for reasoning about fault behavior in physical systems. The representation scheme consists of a function hierarchy of the physical system, object hierarchies of components found in physical systems, a generalized inference methodology for determining component and system performance, and a task control scheme that allows concurrent and integrated reasoning during fault diagnosis. 11 refs., 6 figs.

Stratton, R.C.

1990-06-01

321

Combined approach to fault diagnosis based on principal components and influence matrix  

Microsoft Academic Search

This paper presents an on-line combined fault diagnosis approach for diagnosis of multiplicative (parametric) faults in dynamical systems. The fault detection task is performed applying the principal component analysis to the parameters of a discrete-time model. The fault isolation task is based on an influence matrix method, assuming that exist a relationship between the discrete-time model parameters and the physical

Luis Brito Palma; Fernando Vieira Coito; Rui Neves da Silva

2005-01-01

322

Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert Huang transform  

NASA Astrophysics Data System (ADS)

A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier transform (DFT) has been known to be less efficient. One of the most suited time-frequency approach, wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert-Huang Transform (HHT) technique provides multi-resolution in various frequency scales and takes the signal's frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). However, use of Hilbert transform (HT)-based time domain approach in HHT for analysis of bearing vibration signature leads to scope for subjective error in calculation of characteristic defect frequencies (CDF) of the rolling element bearings. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In the present work, FFT of IMFs from HHT process has been incorporated to utilise efficiency of HT in frequency domain. The comparative analysis presented in this paper indicates the effectiveness of using frequency domain approach in HHT and its efficiency as one of the best-suited techniques for bearing fault diagnosis (BFD).

Rai, V. K.; Mohanty, A. R.

2007-08-01

323

Research on Fault Diagnosis Expert System of Weapon Equipment Based on Rules-Ratiocination  

Microsoft Academic Search

This paper indicating to the actual question of a surface-to-air missile system failure diagnosis, researched the fault intelligent diagnosis question through the expert system method. A surface-to-air missile equipment Fault Diagnosis Expert System based on the rules-ratiocination technique is conceived by the notation of equipment, fault knowledge based on the rules-ratiocination technique, and the repository is conceived based on the

Zhang Lin; Luo Shan; Liang Jian-bo; Pang You-bing

2010-01-01

324

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

Microsoft Academic Search

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

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

1999-01-01

325

An expert system for fault diagnosis in FASTBUS network initialization  

SciTech Connect

This paper discusses FBNEXPERT, an expert system designed to help operators in controlling and maintaining a FASTBUS data acquisition system; it can also assist human experts during trouble-shooting and fault diagnosis. It is based on a shell (NEXPERT, by Neuron Data) which interacts with a knowledge base, where all the information about the FASTBUS system is collected, including the description of the configuration (from the files used for the initialization procedure) and the results of tests and previous diagnoses. During the diagnostic process, FBNEXPERT spans several levels of description of the FASTBUS system and applies various co-operating strategies.

Corazziari, F.; Falciano, S.; Luminari, L.; Savarese, M.; Trasatti, E. (INFN, Dipartimento di Fisica, P.le A. Moro 2, I-00185 Roma (IT)); Rimmer, E.M. (CERN, ECP Div., 1211 Geneva 23 (CH))

1992-04-01

326

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

NASA Astrophysics Data System (ADS)

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

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

2015-02-01

327

Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes  

NASA Astrophysics Data System (ADS)

The vibration signals of faulty planetary gearboxes have complicated spectral structures due to the amplitude modulation and frequency modulation (AMFM) nature of gear damage induced vibration and the additional multiplicative amplitude modulation (AM) effect caused by the time-varying vibration transfer paths (for local gear damage case) and the passing planets (for distributed gear damage case). The spectral complexity leads to the difficulty in fault diagnosis of planetary gearboxes. Observing that both the amplitude envelope and the instantaneous frequency of planetary gearbox vibration signals are associated with the characteristic frequency of the faulty gear, a joint amplitude and frequency demodulation method is proposed for fault diagnosis of planetary gearboxes. In order to satisfy the mono-component requirement by instantaneous frequency estimation, a signal is firstly decomposed into product functions (PF) using the local mean decomposition (LMD) method. Then, the earliest extracted PF that has an instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is chosen for further analysis, because it contains most of the information about the gear fault. The amplitude demodulation analysis can be accomplished through Fourier transforming the amplitude envelope of the chosen PF. For the frequency demodulation analysis, Fourier transform is applied to the estimated instantaneous frequency of the chosen PF to reveal its fluctuating frequency, thus obtaining the spectrum of the instantaneous frequency. By joint application of the amplitude and frequency demodulation methods, planetary gearbox faults can be diagnosed by matching the dominant peaks in the envelope spectrum and the spectrum of instantaneous frequency with the theoretical characteristic frequencies of faulty gears. The performance of the proposed method is illustrated by simulated signal analysis, and is validated by experimental signal analysis of a lab planetary gearbox with intentionally created pitting and naturally developed wear.

Feng, Zhipeng; Zuo, Ming J.; Qu, Jian; Tian, Tao; Liu, Zhiliang

2013-10-01

328

Detrended fluctuation analysis of vibration signals for bearing fault detection  

Microsoft Academic Search

Rolling element bearings are widely used in various rotary machines. Accordingly, a reliable bearing fault detection technique is critically needed in industries to prevent these machines' performance degradation, malfunction, or even catastrophic failures. Although a number of approaches have been reported in the literature, bearing fault detection, however, still remains a very challenging task because most of the bearing fault

Jie Liu

2011-01-01

329

Nuclear power plant fault-diagnosis using artificial neural networks  

SciTech Connect

Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses.

Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

1992-01-01

330

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

PubMed Central

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

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

2014-01-01

331

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

PubMed Central

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

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

2014-01-01

332

Complexity of system-level fault diagnosis and diagnosability  

SciTech Connect

It is now possible to design and build systems that incorporate a large number of processing elements. For this reason, fault-diagnosis at the system level, a research area pioneered by the work of Preparata, Metze, and Chien, is of increasing importance. The formalization of their model utilizes directed graphs together with labelings on edges and vertices. The two central problems introduced by the model are called the diagnosis and diagnosability problems. In the diagnosis problem, an algorithm must identify the faulty units of a system based on test results. In the diagnosability problem, an algorithm must determine the maximum number of faulty units a system can contain and still be guaranteed capable of successfully testing itself. One of the main open questions is resolved for this model by presenting the first polynomial time algorithm for the diagnosability problem. The solution uses network-flow techniques and runs in O(absolute value E absolute value V/sup 3/2/) time. Also presented is a new time-complexity bound of O(min(t absolute value E, t/sup 3/ + absolute value E)) for the diagnosis problem, where t is the maximum number of faulty units.

Sullivan, G.F.

1986-01-01

333

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

E-print Network

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

Paris-Sud XI, Université de

334

A Bayesian network approach for fixture fault diagnosis in launch of the assembly process  

Microsoft Academic Search

Verification and correction of faults related to tooling design and tooling installation are important in the auto body assembly process launch. This paper introduces a Bayesian network (BN) approach for quick detection and localisation of assembly fixture faults based on the complete measurement data set. Optimal sensor placement for effective diagnosis of multiple faults, structure learning of the Bayesian network

Sun Jin; Yinhua Liu; Zhongqin Lin

2011-01-01

335

Fault diagnosis analysis with support vector regression and particle swarm optimization algorithm  

Microsoft Academic Search

The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection method

WenJie Tian; JiCheng Liu

2010-01-01

336

Fault Diagnosis of Regenerative Water Heater Based-On Multi-class Support Vector Machines  

Microsoft Academic Search

The main idea of multi-class support vector machines (SVMs) is described. a multi-class model for regenerative water heater fault diagnosis is presented combining the fuzzy logic and SVMs. The typical faults set of regenerative water heater is built after thoroughly analyzing the relationships between performance parameters and faults. Finally, the model is inspected and verified by an example in a

Lei Wang; Rui-qing Zhang

2009-01-01

337

Analytic Redundancy, Possible Conflicts, and TCG-based Fault Signature Diagnosis  

E-print Network

Analytic Redundancy, Possible Conflicts, and TCG-based Fault Signature Diagnosis applied evaluation steps in fault detection and isolation in dynamic systems. This paper compares three different-based approaches for online fault detection, isolation, and identi- fication (FDII) in complex nonlinear systems

Koutsoukos, Xenofon D.

338

Simultaneous Sensor and Actuator Fault Reconstruction and Diagnosis using Generalized Sliding Mode Observers  

E-print Network

Simultaneous Sensor and Actuator Fault Reconstruction and Diagnosis using Generalized Sliding Mode Observers R. Raoufi and H. J. Marquez Abstract-- A new filter for state and fault estimation in a class and singular systems theory. The novelty of this approach is based upon dealing with systems prone to faults

Marquez, Horacio J.

339

Evaluation of Integrated Error Processing and Fault Diagnosis in Multiprocessor Systems  

E-print Network

Evaluation of Integrated Error Processing and Fault Diagnosis in Multiprocessor Systems F. Di systems required to provide both high performance and good figures of dependability attributes. Fault, with simple instances of redundancy-based error processing structures. The resulting fault tolerance

Firenze, Università degli Studi di

340

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

E-print Network

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 systems that feature dependencies. Traditional Fault Tree is a tool used for system diagnostics

Paris-Sud XI, Université de

341

Development of rules for single-line fault diagnosis in delta-delta connected distribution systems  

SciTech Connect

Single-line fault diagnosis in delta-delta connected distribution systems suffers due to the low fault currents associated with such faults. Simulation tests on this type of system reveals that rule based decision support can be used of such diagnosis. This paper describes the development of rules for single-line fault diagnosis utilizing simulation test results. The key parameters used are the voltage magnitude of each phase at the bus bar and the currents on the feeders including their sequence components.

Momoh, J.A.; Dias, L.G.; Thor, T. [Howard Univ., Washington, DC (United States). Dept. of Electrical Engineering; Laird, D.N. [Los Angeles Dept. of Water and Power, CA (United States)

1994-12-31

342

Fault Diagnosis in Mixed-Signal Low Testability System Jing Pang Janusz A. Starzyk  

E-print Network

-0007 jingpang@bobcat.ent.ohiou.edu starzyk@bobcat.ent.ohiou.edu KEY WORDS: ambiguity groups, fault diagnosis University Athens, OH 45701, U. S. A. Tel.(740) 593-1580 Fax (740) 593-0007 jingpang@bobcat.ent.ohiou.edu starzyk@bobcat.ent.ohiou.edu ABSTRACT This paper describes a new approach for fault diagnosis of analog

Starzyk, Janusz A.

343

Data based construction of Bayesian network for fault diagnosis of event-driven systems  

Microsoft Academic Search

This paper presents a decentralized fault diagnosis strategy of event-driven systems based on probabilistic inference and a method to construct the inference network, Bayesian network (BN), structure. First of all, the controlled plant is decomposed into some subsystems, and the global diagnosis is formulated using the Bayesian Network, which represents the causal relationship between the fault and observation in subsystems.

Takuma Yamaguchi; Shinkichi Inagaki; Tatsuya Suzuki

2010-01-01

344

Fault diagnosis model research based on support vector regression and principal components analysis  

Microsoft Academic Search

To overcome the deficiencies of low accuracy and high false alarm rate in fault diagnosis system, a new optimization method for f the fault diagnosis model is proposed based on support vector regression (SVR) and principal components analysis. Utilizing the character that principal components analysis algorithm can keep the discernability of original dataset after reduction, the reduces of the original

WenJie Tian; JiCheng Liu

2010-01-01

345

Comparison-Based System-Level Fault Diagnosis in Ad-Hoc Networks REGULAR PAPER  

E-print Network

protocol. Keywords. Ad-hoc networks, fault-tolerance, system-level diagnosis, diagnostic model, comparisonComparison-Based System-Level Fault Diagnosis in Ad-Hoc Networks REGULAR PAPER Stefano Chessa1 protocols for ad-hoc networks, the different nature of the communication medium has to be taken into account

Santi, Paolo

346

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine  

Microsoft Academic Search

Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of

Chenglin Zhao; Xuebin Sun; Songlin Sun; Ting Jiang

2011-01-01

347

Application of ant colony optimization-SVM in fault diagnosis for rectifier circuit  

Microsoft Academic Search

Failure of rectifier circuit has the characteristics of latency and complexity, which leads to the difficulty to fault diagnosis for rectifier circuit. A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the paper. The experimental object is provided and the six ACO-SVM classifiers are developed

Xu Binghui

2010-01-01

348

Survey on uncertainty support vector machine and its application in fault diagnosis  

Microsoft Academic Search

Because of the superiority on processing the uncertain information and fuzzy information, the uncertainty mathematical theory has been widely applied in fault diagnosis of complex system. In this paper, first, the combination of basic uncertainty mathematics theory and support vector machine (SVM) and its application in fault diagnosis are introduced in detail. Then, some of the key technologies are also

Yi-Bo Li; Ye Li

2010-01-01

349

Machine condition monitoring and fault diagnosis based on support vector machine  

Microsoft Academic Search

Due to the importance of rotating machinery as one of the most widely used industrial element, development a proper monitoring and fault diagnosis technique to prevent malfunction and failure of machine during operation is necessary. This paper presents a method for gearbox fault diagnosis based on feature extraction technique, distance evaluation technique and the support vector machines (SVMs) ensemble. The

Jianhua Zhong; Zhixin Yang; S. F. Wong

2010-01-01

350

Built-in self-test (BIST) structure for analog circuit fault diagnosis  

Microsoft Academic Search

An analog built-in self-test (BIST) structure for analog circuit fault diagnosis is described that increases the numbers of test points while still keeping low pin overhead. The BIST structure allows access to some internal nodes so that the fault diagnosis process can be significantly simplified. The BIST structure also allows designers to use one channel of an oscilloscope to simultaneously

C.-L. Wey

1990-01-01

351

A neural network approach for identification and fault diagnosis on dynamic systems  

Microsoft Academic Search

The possibilities offered by neural networks for system identification and fault diagnosis problems in dynamic systems are investigated. In particular, an original “neural” procedure is illustrated: its sensitivity and response time enable it to be used in on-line fault diagnosis applications. Some examples are also reported. Even though they pertain to a simple linear dynamic system, these examples highlight the

A. Bernieri; M. D'Apuzzo; L. Sansone; M. Savastano

1994-01-01

352

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

E-print Network

Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine Induction Assad, R. Karam, and S. Farah Abstract--This paper deals with the diagnosis of Wind Tur- bines based simulation experiments and compared for several types of fault, including air-gap eccentricities, broken

Paris-Sud XI, Université de

353

Fault diagnosis in discrete-event systems: framework and model reduction  

Microsoft Academic Search

A state-based approach for online passive fault diagnosis in systems modelled as finite-state automata is presented. In this framework, the system and the diagnoser (the fault detection system) do not have to be initialized at the same time. Furthermore, no information about the state or even the condition (failure status) of the system before the initiation of diagnosis is required.

S. Hashtrudi Zad; R. H. Kwong; W. M. Wonham

1998-01-01

354

Alternate path reasoning in intelligent instrument fault diagnosis for gas chromatography  

Microsoft Academic Search

Intelligent instrument fault diagnosis is addressed using expert networks, a hybrid technique which blends traditional rule-based expert systems with neural network style training. One of the most difficult aspects of instrument fault diagnosis is developing an appropriate rule base for the expert network. Beginning with an initial set of rules given by experts, a more accurate representation of the reasoning

Kristin L. Adair; Susan I. Hruska; John W. Elling

1996-01-01

355

Review of parity space approaches to fault diagnosis for aerospace systems  

Microsoft Academic Search

This paper provides a tutorial review of the state of the art in parity space fault diagnosis approaches with particular emphasis on aerospace systems. The basic concepts and definitions are given and a consistent framework is presented to draw together the important links amongst the known methods for fault diagnosis. Residual generation in the parity space has been recognized as

R. J. Patton; J. Chen

1994-01-01

356

Low-cost motor drive embedded fault diagnosis systems  

E-print Network

Electric motors are used widely in industrial manufacturing plants. Bearing faults, insulation faults, and rotor faults are the major causes of electric motor failures. Based on the line current analysis, this dissertation mainly deals with the low...

Akin, Bilal

2009-05-15

357

A Modified Discrete Binary Ant Colony Optimization and Its Application in Chemical Process Fault Diagnosis  

Microsoft Academic Search

Considering fault diagnosis is a small sample problem in real chemical process industry, Support Vector Machines (SVM) is\\u000a adopted as classifier to discriminate chemical process steady faults. To improve fault diagnosis performance, it is essential\\u000a to reduce the dimensionality of collected data. This paper presents a modified discrete binary ant colony optimization (MDBACO)\\u000a to optimize discrete combinational problems, and then

Ling Wang; Jinshou Yu

2006-01-01

358

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

NASA Astrophysics Data System (ADS)

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.

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

2005-03-01

359

Fault diagnosis of an air-handling unit using artificial neural networks  

SciTech Connect

The objective of this study is to describe the application of artificial neural networks to the problem of fault diagnosis in an air-handling unit. Initially, residuals of system variables that can be used to quantify the dominant symptoms of fault modes of operation are selected. Idealized steady-state patterns of the residuals are then defined for each fault mode of operation. The steady-state relationship between the dominant symptoms and the faults is learned by an artificial neural network using the backpropagation algorithm. The trained neural network is applied to experimental data for various faults and successfully identifies each fault.

Lee, W.Y. [Korea Inst. of Energy Research, Taejon (Korea, Republic of); House, J.M.; Park, C.; Kelly, G.E. [National Inst. of Standards and Technology, Gaithersburg, MD (United States)

1996-11-01

360

Development of parameter based fault detection and diagnosis technique for energy efficient building management system  

Microsoft Academic Search

This paper presents a complete methodology for detection and diagnosis of faults in variable air volume air handling units. Three cases are considered: (a) an off-line fault detection technique for existing buildings, (b) an automatic on-line fault detection technique for integration in building management systems (BMSs) of upcoming not very complex buildings and (c) an automatic on-line fault detection as

Sanjay Kumar; S. Sinha; T. Kojima; H. Yoshida

2001-01-01

361

An expert system for fault detection and diagnosis  

E-print Network

: Line Voltage Amplitudes at Bus 1 AB Fault at M12: Line Voltages at Bus 8 137 138 AB Fault at M12: Line Voltage Amplitudes at Bus 8 138 xvn FIGURE I'age 97 ABG Fault at NBELT: Curreul, s st, KING 144 98 AHG Fault at NHELT: Current Arnplitucles... at KliJG 144 99 AHG Fault at WiiART: Currents at KING 145 100 ABG I'suit at WIIART: Current Amplitudes at, KING 145 101 ABG Fault at M12: Currents at Bus 1 I46 102 ABG Fault at M12: Current Amplitudes at Bus 1 146 103 AHG Fault at MI 2: Currents...

Spasojevic, Predrag

2012-06-07

362

Real-time antenna fault diagnosis experiments at DSS 13  

NASA Technical Reports Server (NTRS)

Experimental results obtained when a previously described fault diagnosis system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.

Mellstrom, J.; Pierson, C.; Smyth, P.

1992-01-01

363

Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis  

Microsoft Academic Search

At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. The transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time index)

Shibin Wang; Weiguo Huang; Z. K. Zhu

2011-01-01

364

Gearbox fault diagnosis using adaptive zero phase time-varying filter based on multi-scale chirplet sparse signal decomposition  

NASA Astrophysics Data System (ADS)

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Wu, Chunyan; Liu, Jian; Peng, Fuqiang; Yu, Dejie; Li, Rong

2013-07-01

365

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

PubMed

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

Li, Chaoshun; Zhou, Jianzhong

2014-09-01

366

SPECTRUM CLASSIFICATION FOR EARLY FAULT DIAGNOSIS OF THE LP GAS PRESSURE REGULATOR BASED ON THE KULLBACK-LEIBLER KERNEL  

E-print Network

SPECTRUM CLASSIFICATION FOR EARLY FAULT DIAGNOSIS OF THE LP GAS PRESSURE REGULATOR BASED The present paper describes a frequency spectrum classi- fication method for fault diagnosis of the LP gas, such as polyno- mial or Gaussian kernels, or the conventional fault diagnosis method and Gaussian Mixture Model

Higuchi, Tomoyuki

367

A robust algebraic approach to fault diagnosis of uncertain linear Abdouramane Moussa Ali, Cedric Join and Frederic Hamelin  

E-print Network

A robust algebraic approach to fault diagnosis of uncertain linear systems Abdouramane Moussa Ali diagnosis for uncertain linear systems. The main advantage of this new approach is to realize fault examples are provided and discussed to illustrate the efficiency of the proposed fault diagnosis method. I

Paris-Sud XI, Université de

368

1-4244-1506-3/08/$25.00 2008 IEEE Network Calculus Based Fault Diagnosis Decision-Making  

E-print Network

1-4244-1506-3/08/$25.00 ©2008 IEEE Network Calculus Based Fault Diagnosis Decision, robustness. 1. Introduction A new trend in the field of fault diagnosis for techni- cal systems and conclusion and further work are discussed in the last section. 2. FDI principle Fault diagnosis implies

Paris-Sud XI, Université de

369

H Dynamic observer design with application in fault diagnosis A. M. Pertew , H. J. Marquez and Q. Zhao  

E-print Network

H Dynamic observer design with application in fault diagnosis A. M. Pertew , H. J. Marquez and Q-based methods applied in fault detection and diagnosis (FDD) schemes use the classical two- degrees of freedom. This structure offers extra degrees of freedom and we show how it can be used for the sensor faults diagnosis

Marquez, Horacio J.

370

Nuclear power plant fault-diagnosis using artificial neural networks  

SciTech Connect

Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant`s training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses.

Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

1992-12-31

371

A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis  

NASA Astrophysics Data System (ADS)

Since machinery fault vibration signals are usually multicomponent modulation signals, how to decompose complex signals into a set of mono-components whose instantaneous frequency (IF) has physical sense has become a key issue. Local mean decomposition (LMD) is a new kind of time-frequency analysis approach which can decompose a signal adaptively into a set of product function (PF) components. In this paper, a modulation feature extraction method-based LMD is proposed. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. The computed IF and IA are displayed together in the form of time-frequency representation (TFR). Modulation features can be extracted from the spectrum analysis of the IA and IF. In order to make the IF have physical meaning, the phase-unwrapping algorithm and IF processing method of extrema are presented in detail along with a simulation FM signal example. Besides, the dependence of the LMD method on the signal-to-noise ratio (SNR) is also investigated by analyzing synthetic signals which are added with Gaussian noise. As a result, the recommended critical SNRs for PF decomposition and IF extraction are given according to the practical application. Successful fault diagnosis on a rolling bearing and gear of locomotive bogies shows that LMD has better identification capacity for modulation signal processing and is very suitable for failure detection in rotating machinery.

Chen, Baojia; He, Zhengjia; Chen, Xuefeng; Cao, Hongrui; Cai, Gaigai; Zi, Yanyang

2011-05-01

372

Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines  

NASA Astrophysics Data System (ADS)

This study presents a novel procedure based on ensemble empirical mode decomposition (EEMD) and optimized support vector machine (SVM) for multi-fault diagnosis of rolling element bearings. The vibration signal is adaptively decomposed into a number of intrinsic mode functions (IMFs) by EEMD. Two types of features, the EEMD energy entropy and singular values of the matrix whose rows are IMFs, are extracted. EEMD energy entropy is used to specify whether the bearing has faults or not. If the bearing has faults, singular values are input to multi-class SVM optimized by inter-cluster distance in the feature space (ICDSVM) to specify the fault type. The proposed method was tested on a system with an electric motor which has two rolling bearings with 8 normal working conditions and 48 fault working conditions. Five groups of experiments were done to evaluate the effectiveness of the proposed method. The results show that the proposed method outperforms other methods both mentioned in this paper and published in other literatures.

Zhang, Xiaoyuan; Zhou, Jianzhong

2013-12-01

373

On the application of a machine learning technique to fault diagnosis of power distribution lines  

SciTech Connect

This paper presents one method for fault diagnosis of power distribution lines by using a decision tree. The conventional method, using a decision tree, applies only to discrete attribute values. To apply it to fault diagnosis of power distribution lines, in practice it must be revised in order to treat attributes whose values range over certain widths. This is because the sensor value or attribute value varies owing to the resistance of the fault point or is influenced by noise. The proposed method is useful when the attribute value has such a property, and it takes into consideration the cost of acquiring the information and the probability of the occurrence of a fault.

Togami, Masato [Nagoya MFG (Japan)] [Nagoya MFG (Japan); Abe, Norihiro [Kyushu Inst. of Tech., Iizuka (Japan)] [Kyushu Inst. of Tech., Iizuka (Japan); Kitahashi, T. [Osaka Univ., Ibaraki (Japan)] [Osaka Univ., Ibaraki (Japan); Ogawa, Harunao [Togami Electric, Saga (Japan)] [Togami Electric, Saga (Japan)

1995-10-01

374

The use of hybrid automata for fault-tolerant vibration control for parametric failures  

NASA Astrophysics Data System (ADS)

The purpose of this work is to make use of hybrid automata for vibration control reconfiguration under system failures. Fault detection and isolation (FDI) filters are used to monitor an active vibration control system. When system failures occur (specifically parametric faults) the FDI filters detect and identify the specific failure. In this work we are specifically interested in parametric faults such as changes in system physical parameters; however this approach works equally well with additive faults such as sensor or actuator failures. The FDI filter output is used to drive a hybrid automaton, which selects the appropriate controller and FDI filter from a library. The hybrid automata also implements switching between controllers and filters in order to maintain optimal performance under faulty operating conditions. The biggest challenge in developing this system is managing the switching and in maintaining stability during the discontinuous switches. Therefore, in addition to vibration control, the stability associated with switching compensators and FDI filters is studied. Furthermore, the performance of two types of FDI filters is compared: filters based on parameter estimation methods and so called "Beard-Jones" filters. Finally, these simulations help in understanding the use of hybrid automata for fault-tolerant control.

Byreddy, Chakradhar; Frampton, Kenneth D.; Yongmin, Kim

2006-03-01

375

A time domain approach to diagnose gearbox fault based on measured vibration signals  

NASA Astrophysics Data System (ADS)

Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.

Hong, Liu; Dhupia, Jaspreet Singh

2014-03-01

376

Fault Feature Selection Based on Modified Binary PSO with Mutation and Its Application in Chemical Process Fault Diagnosis  

Microsoft Academic Search

\\u000a In large scale industry systems, especially in chemical process industry, large amounts of variables are monitored. When all\\u000a variables are collected for fault diagnosis, it results in poor fault classification because there are too many irrelevant\\u000a variables, which also increase the dimensions of data. A novel optimization algorithm, based on a modified binary Particle\\u000a Swarm Optimization with mutation (MBPSOM) combined

Ling Wang; Jinshou Yu

2005-01-01

377

Fault detection and diagnosis of power converters using artificial neural networks  

SciTech Connect

Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission systems.

Swarup, K.S.; Chandrasekharaiah, H.S. [Indian Inst. of Science, Bangalore (India). Dept. of High Voltage Engineering

1995-12-31

378

Development of fault diagnosis system for transformer based on multi-class support vector machines  

NASA Astrophysics Data System (ADS)

The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and having high generalization ability. It is strong to solve the problem with small sample, nonlinear and high dimension. The fundamental theory of DGA (Dissolved Gas Analysis, DGA) and fault characteristic of transformer is firstly researched in this paper, and then the disadvantages of traditional method of transformer fault diagnosis are analyzed, finally, a new fault diagnosis method using multi-class support vector machines (M-SVMs) based on DGA theory for transformer is put forward. Then the fault diagnosis model based on M-SVMs for transformer is established. At the same time, the fault diagnosis system based on M-SVMs for transformer is developed. The system can realize the acquisition of the dissolving gas in the transformer oil and data timely and low cost transmission by GPRS (General Packet Radio Service, GPRS). And it can identify out the transformer running state according to the acquisition data. The test results show that the method proposed has an excellent performance on correct ratio. And it can overcome the disadvantage of the traditional three-ratio method which lacks of fault coding and no fault types in the existent coding. Combining the wireless communication technology with the monitoring technology, the designed and developed system can greatly improve the real-time and continuity for the transformer' condition monitoring and fault diagnosis.

Cao, Jian; Qian, Suxiang; Hu, Hongsheng; Yan, Gongbiao

2007-12-01

379

Observer-Based Fault Diagnosis of Power Electronics Systems  

E-print Network

supply is paramount in many applications, ranging from safety- and mission-critical systems for aircraft component, and iii) a severity assessment determines the extent of the fault. In general, methods for fault

Liberzon, Daniel

380

Fault diagnosis of analog circuit based on support vector machines  

Microsoft Academic Search

An innovative method based on support vector machines is presented to diagnose the fault of analog circuit. Firstly, in order to get enough fault samples, the circuit program is compiled in MATLAB software to obtain expressions of output signals. Secondly, fault samples are sent into Support Vector Machines to train Support Vector Machines. Thirdly, the test samples are classified by

Yehui Liu; Yuye Yang; Liang Huang

2009-01-01

381

Incipient fault diagnosis of chemical processes via artificial neural networks  

Microsoft Academic Search

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

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

1989-01-01

382

Development of a fault diagnosis method for heating systems using neural networks  

SciTech Connect

The application of artificial neural networks (ANNs) for developing a fault diagnosis (FD) method in complex heating systems is presented in this paper. The six operating modes with faults used to develop this FD method came from the results of a detailed investigation in cooperation with heating system maintenance experts and are among the most important operating faults for this type of system. Because a daily diagnosis is generally sufficient, the ANNs have been developed using the daily values obtained by a preprocessing of the numerical simulation data. This paper presents the first step of the method development. It demonstrates the feasibility of using ANNs for fault diagnosis of a specific heating, ventilating, and air-conditioning (HVAC) system provided training data representative of the behavior of the system with and without faults are available. The next step will consist of developing a generic method that requires less training data.

Li, X.; Vaezi-Nejad, H.; Visier, J.C. [Centre Scientifique et Technique du Batiment, Marne La Vallee (France). HVAC Dept.

1996-11-01

383

Early Oscillation Detection for DC/DC Converter Fault Diagnosis  

NASA Technical Reports Server (NTRS)

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

Wang, Bright L.

2011-01-01

384

Application of neural networks for fault diagnosis in plant production  

SciTech Connect

Sixth-generation computers, in Japan, have been announced as natural intelligence computers that would display behaviors based on biological rather than silicon models. These systems are derived from neurological models or neural networks that consist of a number of simple, highly interconnected processing elements which process information by their dynamic-state response to external inputs. The neural network is made by specifying interconnections, transfer functions, and training laws of the network. Then appropriate inputs are applied to the network, and it is allowed to react. The overall state of the network after it has reacted to the input will be the desired response pattern. This paper explores the potential for using simple networks for data interpretation. The ultimate goal of our study includes the interpretation of industrial plant monitoring data for diagnosis of chemical process or other process problems. Known data about normal operations, as well as abnormal operations and its causes, can be taught to the system. The system will apply a learning mechanism to the data in order to acquire knowledge. By supplying operational data, the system will indicate the nature of the fault, if any exists. 5 refs., 10 figs.

Ferrada, J.J.; Gordon, M.D.; Osborne-Lee, I.W. (Oak Ridge National Lab., TN (USA); Naval Academy, Annapolis, MD (USA); Oak Ridge National Lab., TN (USA))

1989-10-01

385

Gearbox fault diagnosis method based on wavelet packet analysis and support vector machine  

Microsoft Academic Search

This paper presents an intelligent method for gear fault diagnosis based on wavelet packet analysis and support vector machine (SVM). For this purpose, two experiments were selected to verify the proposed method. One is a spur gear of the motorcycle gearbox system. Slight-worn, medium-worn, and broken-tooth were selected as the faults. In fault simulating, two very similar models of worn

Jianshe Kanga; Xinghui Zhanga; Jianmin Zhaoa; Hongzhi Teng; Duanchao Caoa

2012-01-01

386

Decoupling features for diagnosis of reversing and check valve faults in heat pumps  

Microsoft Academic Search

Recently, a decoupling-based (DB) fault detection and diagnosis (FDD) method was developed for diagnosing multiple-simultaneous faults in air conditioners (AC) and was shown to have very good performance. The method relies on identifying diagnostic features that are decoupled (i.e., insensitive) to other faults and operating conditions. The current paper extends the DB FDD methodology to heat pumps. Heat pumps have

H. Li; J. E. Braun

2009-01-01

387

Compact Dictionaries for Diagnosis of Unmodeled Faults in Scan-BIST Chunsheng Liu  

E-print Network

Compact Dictionaries for Diagnosis of Unmodeled Faults in Scan-BIST Chunsheng Liu , Kumar N. Dwarakanath , Krishnendu Chakrabarty and Ronald D. (Shawn) Blanton Department of Computer & Electronic University, Pittsburgh, PA 15213 Abstract We address the problem of generating compact dictionaries

Chakrabarty, Krishnendu

388

A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator  

E-print Network

A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator Wail Rezgui observations distributed over classes is used for simulation purposes. Keywords--Photovoltaic generator, SVM, k-NN, short-circuit fault, smart classification, linear programming. NOMENCLATURE PV = Photovoltaic; SVM

Paris-Sud XI, Université de

389

Analog circuit fault diagnosis based on fuzzy support vector machine and kernel density estimation  

Microsoft Academic Search

Because analog circuits such as abnormal noise contained in the information, to the support vector machine to build up the optimal classification brings difficulties, this paper proposes a new method for analog circuit fault diagnosis. First of all, time-domain signal extraction circuit statistical parameters, a set of fault characteristics and then use kernel density estimation method, proposed a form of

Jing Tang; Yun'an Hu; Tao Lin; Yu Chen

2010-01-01

390

Algorithms comparison of feature extraction and multi-class classification for fault diagnosis of analog circuit  

Microsoft Academic Search

For the novelties or anomalies of faulty signals occur in a damage circuit and fault signals vary with different circuit damages. To ensure the accuracy and reliability of diagnosis, it is very important to extract the characteristic features of fault signals. Two feature extraction methods based on wavelet packet transform is proposed to treat transient signals: optimal wavelet packet transform

An-Na Wang; Jun-Fang Liu; Wen-Jing Yuan; Hua Li

2007-01-01

391

Hybrid intelligent fault diagnosis based on adaptive lifting wavelet and multi-class support vector machine  

Microsoft Academic Search

To diagnose compound faults of rotating machine, this paper presents a novel hybrid intelligent fault diagnosis model based on adaptive lifting wavelet and multi-class support vector machine. First of all, the adaptive lifting wavelet is constructed to mach the signal local characteristics. The original signal is decomposed into approximation signal and detail signal. Secondly, 32 time-domain statistical features are evaluated

Zhong-Jie Shen; Xue-Feng Cheng; Zheng-Jia He

2010-01-01

392

Condition monitoring and fault diagnosis of electrical machines-a review  

Microsoft Academic Search

Research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this

S. Nandi; H. A. Toliyat

1999-01-01

393

ANN based transformer fault diagnosis using gas-in-oil analysis  

SciTech Connect

This paper presents an artificial neural network (ANN) approach to detect faults in oil-filled power transformers. The relationship between transformer faults of power transformers and gases dissolved in insulating oil is reviewed. ANN based data processing and diagnostic techniques are described. Preliminary simulation results show a 95% correct diagnosis rate for the ANN based method with modest amount of training data.

Ding, X.; Yao, E.; Liu, Y. [Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States). Bradley Dept. of Electrical Engineering; Griffin, P.J. [Doble Engineering Co., Watertown, MA (United States)

1995-10-01

394

Model-based fault-detection and diagnosis – status and applications  

Microsoft Academic Search

For the improvement of reliability, safety and efficiency advanced methods of supervision, fault-detection and fault diagnosis become increasingly important for many technical processes. This holds especially for safety related processes like aircraft, trains, automobiles, power plants and chemical plants. The classical approaches are limit or trend checking of some measurable output variables. Because they do not give a deeper insight

Rolf Isermann

2005-01-01

395

Fault diagnosis for air contamination events in three-dimensional environments  

Microsoft Academic Search

We propose an Extended Implicit Kalman Filter for fault diagnosis of air contaminants in three-dimensional environments. In order to accurately detect and diagnose air contamination faults in environments such as a space station, we combine the use of an Extended Implicit Kalman Filter with the use of sensitivity matrices that provide the filter with an initial guess of the capacity

Anand P. Narayan; W. Fred Ramirez

2000-01-01

396

Computational Analysis of Sparse Datasets for Fault Diagnosis in Large Tribological Mechanisms  

Microsoft Academic Search

This paper presents the most up-to-date methods for the task of designing a system to accurately classify abnormal events, or faults, in a complex tribological mechanism, using el- emental analysis of lubrication oil as an indicator of engine con- dition. The discussion combines perspectives from numerous fault diagnosis applications, both online and offline, to focus upon the task of offline

Ian Morgan; Honghai Liu

2011-01-01

397

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

Microsoft Academic Search

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

Didier CAYRAC; Didier DUBOIS; Henri PRADE

1996-01-01

398

Nonlinear Estimation for Model Based Fault Diagnosis of Nonlinear Chemical Systems  

E-print Network

NONLINEAR ESTIMATION FOR MODEL BASED FAULT DIAGNOSIS OF NONLINEAR CHEMICAL SYSTEMS A Dissertation by CHUNYAN QU Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR... OF PHILOSOPHY December 2009 Major Subject: Chemical Engineering NONLINEAR ESTIMATION FOR MODEL BASED FAULT DIAGNOSIS OF NONLINEAR CHEMICAL SYSTEMS A Dissertation by CHUNYAN QU Submitted to the Office of Graduate Studies of Texas A&M University in partial...

Qu, Chunyan

2011-02-22

399

On-line fault diagnosis in a nuclear reactor by sequential testing  

SciTech Connect

A sequential test technique for on-line fault diagnosis of sensor signals has been developed and successfully demonstrated in an operating nuclear reactor. The methodology provides a systematic procedure for detection and isolation of sensor failures by taking into account consistencies among all available measurements of a given process variable. Fault diagnosis is accomplished on the basis of the cumulative information derived from the measurement history that includes the past and current observations.

Ray, A.; Deyst, J.; DeSai, M.

1983-06-01

400

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

SciTech Connect

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)

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

401

Incipient multiple fault diagnosis in real time with applications to large-scale systems  

SciTech Connect

By using a modified signed directed graph (SDG) together with the distributed artificial neutral networks and a knowledge-based system, a method of incipient multi-fault diagnosis is presented for large-scale physical systems with complex pipes and instrumentations such as valves, actuators, sensors, and controllers. The proposed method is designed so as to (1) make a real-time incipient fault diagnosis possible for large-scale systems, (2) perform the fault diagnosis not only in the steady-state case but also in the transient case as well by using a concept of fault propagation time, which is newly adopted in the SDG model, (3) provide with highly reliable diagnosis results and explanation capability of faults diagnosed as in an expert system, and (4) diagnose the pipe damage such as leaking, break, or throttling. This method is applied for diagnosis of a pressurizer in the Kori Nuclear Power Plant (NPP) unit 2 in Korea under a transient condition, and its result is reported to show satisfactory performance of the method for the incipient multi-fault diagnosis of such a large-scale system in a real-time manner.

Chung, H.Y.; Bien, Z.; Park, J.H.; Seon, P.H. (Korea Advanced Inst. of Science and Technology, Taejon (Korea, Republic of). Dept. of Electrical Engineering)

1994-08-01

402

Fault diagnosis of pneumatic systems with artificial neural network algorithms M. Demetgul a,*, I.N. Tansel b  

E-print Network

Fault diagnosis of pneumatic systems with artificial neural network algorithms M. Demetgul a,*, I) Back propagation (Bp) Fault diagnosis Pneumatic Modular production system a b s t r a c t Pneumatic the pneumatic system worked perfectly and had some faults including empty magazine, zero vacuum, inappropriate

Rucci, Michele

403

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

E-print Network

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

Koutsoukos, Xenofon D.

404

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

Microsoft Academic Search

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.

Jian-da Wu; Chiu-hong Liu

2009-01-01

405

Model-Based Fault Diagnosis of Induction Motors Using Non-Stationary Signal Segmentation  

NASA Astrophysics Data System (ADS)

Effective detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance and improved operational efficiency of induction motors running off the power supply mains. In this paper, an empirical model-based fault diagnosis system is developed for induction motors using recurrent dynamic neural networks and multiresolution signal processing methods. In addition to nameplate information required for the initial set-up, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2, 373 and 597 kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on an new motor, significantly reducing the system development time.

Kim, K.; Parlos, A. G.

2002-03-01

406

Research on vibration response of a multi-faulted rotor system using LMD-based time-frequency representation  

NASA Astrophysics Data System (ADS)

Unbalance, fatigue crack and rotor-stator rub are the three common and important faults in a rotor-bearing system. They are originally interconnected each other, and their vibration behaviors do often show strong nonlinear and transient characteristic, especially when more than one of them coexist in the system. This article is aimed to study the vibration response of the rotor system in presence of multiple rotor faults such as unbalance, crack, and rotor-stator rub, using local mean decomposition-based time-frequency representation. Equations of motion of the multi-faulted Jeffcott rotor, including unbalance, crack, and rub, are presented. By solving the motion equations, steady-state vibration response is obtained in presence of multiple rotor faults. As a comparison, Hilbert-Huang transformation, based on empirical mode decomposition, is also applied to analyze the multi-faults data. By the study some diagnostic recommendations are derived.

Jiao, Weidong; Qian, Suxiang; Chang, Yongping; Yang, Shixi

2012-12-01

407

An approach to acquiring quantitative and qualitative knowledge for fault diagnosis  

SciTech Connect

Operation and maintenance activities associated with complex systems require knowledge of the physical system and knowledge of the cognitive task. This paper discusses our methodology for acquiring physical process knowledge necessary for reasoning about faults. Acquisition of process knowledge used in fault diagnosis consists of (1) problem definition and (2) model development. Problem definition activity determines the constraints, physics, physical structure, function, and fault cases associated with the physical system. Model development activity builds knowledge models via fault-case analysis and qualitative analysis. We discuss problem definition and model development activities in context of a single-pass heat exchanger. 6 refs., 4 figs., 3 tabs.

Stratton, R.C.; Jarrell, D.B.

1990-06-01

408

Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework  

NASA Technical Reports Server (NTRS)

This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

1994-01-01

409

Fault diagnosis and temperature sensor recovery for an air-handling unit  

SciTech Connect

The presence of faults and the influence they have on system operation is a real concern in the heating, ventilating, and air-conditioning (HVAC) community. A fault can be defined as an inadmissible or unacceptable property of a system or a component. Unless corrected, faults can lead to increased energy use, shorter equipment life, and uncomfortable and/or unhealthy conditions for building occupants. This paper describes the use of a two-stage artificial neural network for fault diagnosis in a simulated air-handling unit. The stage one neural network is trained to identify the subsystem in which a fault occurs. The stage two neural network is trained to diagnose the specific cause of a fault at the subsystem level. Regression equations for the supply and mixed-air temperatures are obtained from simulation data and are used to compute input parameters to the neutral networks. Simulation results are presented that demonstrate that, after a successful diagnosis of a supply air temperature sensor fault, the recovered estimate of the supply air temperature obtained from the regression equation can be used in a feedback control loop to bring the supply air temperature back to the setpoint value. Results are also presented that illustrate the evolution of the diagnosis of the two-stage artificial neural network from normal operation to various fault modes of operation.

Lee, W.Y.; Shin, D.R. [Korea Inst. of Energy Research, Taejon (Korea, Republic of); House, J.M. [National Inst. of Standards and Technology, Gaithersburg, MD (United States)

1997-12-31

410

On-line fault diagnosis of power substation using connectionist expert system  

SciTech Connect

This paper proposes a new connectionist (or neural network) expert system for on-line fault diagnosis of a power substation. The Connectionist Expert Diagnosis System has similar profile of an expert system, but can be constructed much more easily from elemental samples. These samples associate the faults with their protective relays and breakers as well as the bus voltages and feeder currents. Through an elaborately designed structure, alarm signals are processed by different connectionist models. The output of the connectionist models is then integrated to provide the final conclusion with a confidence level. The proposed approach has been practically verified by testing on a typical Taiwan Power (Taipower) secondary substation. The test results show that rapid and exactly correct diagnosis is obtained even for the fault conditions involving multiple faults or failure operation of protective relay and circuit breaker. Moreover, the system can be transplanted into various substations with little additional implementation effort.

Yang, H.T.; Chang, W.Y.; Huang, C.L. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Dept. of Electrical Engineering

1995-02-01

411

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

SciTech Connect

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

Kim, K.; Bartlett, E.B.

1994-12-31

412

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

PubMed

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

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

413

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

PubMed Central

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

Yu, Bing; Liu, Dongdong; Zhang, Tianhong

2011-01-01

414

Fault diagnosis of gas turbine engines from transient data  

SciTech Connect

The desirability of being able to extract relevant fault diagnostic information from transient gas turbine data records is discussed. A method is outlined from estimating the effects of unmeasured fault parameters from input/output measurements. The resultant sensitivity of the technique depends on the sampling rate and the measurement noise.

Merrington, G.L. (Aeronautical Research Defence Science and Technology Organization, Melboourne (AU))

1989-04-01

415

System Fault Diagnosis: Masking, Exposure, and Diagnosability Without Repair  

Microsoft Academic Search

Diagnosability without fault repair of a digital system containing at most t faults is considered. A system-level diagnostic model defined in an earlier paper [1] is employed. The model is to an extent independent of the means used to implement diagnostic procedures, i.e., whether the tests are accomplished via hardware, software, or combinations thereof. Two parameters, the masking and exposure

Jeffrey D. Russell; Charles R. Kime

1975-01-01

416

A Table-Based Approach To Expert Fault Diagnosis Networks  

Microsoft Academic Search

In an implementation of expert networks for diagnosing machine and sample faults in automated gaschromatography, we present a technology for constructing a network based on a knowledge table ratherthan the traditional rule based expert system. This system uses linguistic qualifiers to relate symptomsobserved in the chromatogram to faults in the chromatograph or sample preparation and diagnoses thepossible cause of those

Alan P. Levis; Kristin L. Adair; Susan I. Hruska

1996-01-01

417

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

NASA Astrophysics Data System (ADS)

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.

Cox, J.; Anusonti-Inthra, P.

2014-11-01

418

Engine Fault Diagnosis using DTW, MFCC and FFT  

NASA Astrophysics Data System (ADS)

. In this paper we have used a combination of three algorithms: Dynamic time warping (DTW) and the coefficients of Mel frequency Cepstrum (MFC) and Fast Fourier Transformation (FFT) for classifying various engine faults. Dynamic time warping and MFCC (Mel Frequency Cepstral Coefficients), FFT are used usually for automatic speech recognition purposes. This paper introduces DTW algorithm and the coefficients extracted from Mel Frequency Cepstrum, FFT for automatic fault detection and identification (FDI) of internal combustion engines for the first time. The objective of the current work was to develop a new intelligent system that should be able to predict the possible fault in a running engine at different-different workshops. We are doing this first time. Basically we took different-different samples of Engine fault and applied these algorithms, extracted features from it and used Fuzzy Rule Base approach for fault Classification.

Singh, Vrijendra; Meena, Narendra

419

Developing a new transformer fault diagnosis system through evolutionary fuzzy logic  

Microsoft Academic Search

To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC\\/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and

Yann-Chang Huang; C. L. Huang; H. T. Yang

1997-01-01

420

Joint faults detection in LV switchboard and its global diagnosis, through a Temperature Monitoring System.  

E-print Network

Joint faults detection in LV switchboard and its global diagnosis, through a Temperature Monitoring of monitoring and diagnosis of LV switchboards based on the measurements of currents, ambient temperatures and local temperatures of electrical joints. This system meets the needs to prevent the breakdowns of LV

Paris-Sud XI, Université de

421

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

Microsoft Academic Search

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

Yang Yu; Ping Yang

2011-01-01

422

An expert system for fault section diagnosis of power systems using fuzzy relations  

SciTech Connect

This paper proposes an expert system using fuzzy relations to deal with uncertainties imposed on fault section diagnosis of power systems. The authors build sagittal diagrams which represent the fuzzy relations for power systems, and diagnose fault sections using the sagittal diagrams. Next, they examine the malfunction or wrong alarm of relays and circuit breakers based on the alarm information and the estimated fault section. The proposed system provides the fault section candidates in terms of the degree of membership and the malfunction or wrong alarm. An operator monitors these candidates and is able to diagnose the fault section, coping with uncertainties. Experimental studies for real power systems reveal usefulness of the proposed technique to diagnose faults that have uncertainty.

Cho, H.J. [LG Electronics Research Center, Seoul (Korea, Republic of); Park, J.K. [Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering

1997-02-01

423

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

NASA Astrophysics Data System (ADS)

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.

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

2014-12-01

424

New diagnosis theory as the basis of intermittent-fault/transient-upset tolerant system design  

SciTech Connect

Multiple-unit computer systems which are to be tolerant of intermittently faulty units or transiently upset units are considered in this paper. Designs for such systems, which expoloit a new, so called Greedy diagnosis theory, are developed. Using Greedy diagnosis, assessments on the condition of a unit (intermittent-fault case) or the integrity of data (transient-upset case) can be made on the basis of syndromes formed from comparisons of the results of jobs performed by pairs of units. Greedy diagnosis avoids the requirement that for such syndromes to be useful, they must be interpretable from a permanent-fault/continuous-upset perspective. 10 references.

Dahbura, A.T.; Masson, G.M.

1982-01-01

425

Greedy diagnosis as the basis of an intermittent-fault/transient-upset tolerant system design  

SciTech Connect

Multiple-unit computer systems which are to be tolerant of intermittently faulty units or transiently upset units are considered. Designs for such systems are developed which exploit a new so-called greedy diagnosis theory. Using greedy diagnosis, assessments on the condition of a unit (intermittent-fault case) or the integrity of data (transient-upset case) can be made on the basis of syndromes formed from comparisons of the results of jobs performed by pairs of units. Greedy diagnosis avoids the requirement that for such syndromes to be useful, they must be interpretable from a permanent-fault/continuous-upset perspective. 11 references.

Dahbura, A.T.; Masson, G.M.

1983-10-01

426

Rolling element bearing fault diagnosis using wavelet packets  

Microsoft Academic Search

A method is proposed for the analysis of vibration signals resulting from bearings with localized defects using the wavelet packet transform (WPT) as a systematic tool. A time–frequency decomposition of vibration signals is provided and the components carrying the important diagnostic information are selected for further processing. The proposed method is designed in such a way that it can exploit

N. G. Nikolaou; I. A. Antoniadis

2002-01-01

427

Resonant-frequency band choice for bearing fault diagnosis based on EMD and envelope analysis  

Microsoft Academic Search

This study presents a novel method based on the empirical mode decomposition (EMD) and the envelope analysis for the fault detection of rolling bearings. The main purpose is to overcome the traditional envelope method in the choosing of the resonant frequency band. First, the EMD method is adaptively to decompose the vibration signals into a series of the intrinsic mode

Wen-Chang Tsao; Yi-Fan Li; Min-Chun Pan

2010-01-01

428

Poirot: Applications of a Logic Fault Diagnosis Tool  

Microsoft Academic Search

The Poirot tool isolates and diagnoses defects through fault modeling and simulation. Along with a carefully selected partitioning strategy, functional and sequential test pattern applications show success with circuits having a high degree of observability

Srikanth Venkataraman; Scott Brady Drummonds

2001-01-01

429

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

NASA Astrophysics Data System (ADS)

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.

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

2014-12-01

430

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

NASA Astrophysics Data System (ADS)

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.

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

2014-12-01

431

Shannon wavelet spectrum analysis on truncated vibration signals for machine incipient fault detection  

NASA Astrophysics Data System (ADS)

Although a variety of methods have been proposed in the literature for machine fault detection, it still remains a challenge to extract prominent features from random and nonstationary vibratory signals, a typical representative of which are the resonance signatures generated by incipient defects on the rolling elements of ball bearings. Due to its random and nonstationary nature, the involved signal generally possesses a low signal-to-noise ratio, where the classical signal processing methods cannot be effectively applied and the extracted features are usually submerged into the severe background noise. In this paper, a novel random and nonstationary vibratory signature analysis (R&N-VSA) technique is presented to address this challenge. The original vibration signal is decomposed into fault-related and non-fault-related signal segments, and multi-level exponential moving average power filtering is suggested to guide this decomposition. Instead of analyzing the whole vibratory signal, the developed Shannon wavelet spectrum analysis is more efficiently applied on the truncated fault-related signal segments so as to enhance the features' characteristics. The effectiveness of the proposed technique is examined through a series of tests with two experimental setups, and the investigation results show that the developed R&N-VSA technique is an effective signal processing technique for incipient machine fault detection.

Liu, Jie

2012-05-01

432

Conditional Fault Diagnosis of Bubble Sort Graphs under the PMC Model  

E-print Network

As the size of a multiprocessor system increases, processor failure is inevitable, and fault identification in such a system is crucial for reliable computing. The fault diagnosis is the process of identifying faulty processors in a multiprocessor system through testing. For the practical fault diagnosis systems, the probability that all neighboring processors of a processor are faulty simultaneously is very small, and the conditional diagnosability, which is a new metric for evaluating fault tolerance of such systems, assumes that every faulty set does not contain all neighbors of any processor in the systems. This paper shows that the conditional diagnosability of bubble sort graphs $B_n$ under the PMC model is $4n-11$ for $n \\geq 4$, which is about four times its ordinary diagnosability under the PMC model.

Zhou, Shuming; Xu, Xirong; Xu, Jun-Ming

2012-01-01

433

Fault diagnosis based on signed directed graph and support vector machine  

NASA Astrophysics Data System (ADS)

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.

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

2012-01-01

434

Fault diagnosis based on signed directed graph and support vector machine  

NASA Astrophysics Data System (ADS)

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.

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

2011-12-01

435

Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty Gears  

PubMed Central

Most present studies on the acoustic emission signals of rotating machinery are experiment-oriented, while few of them involve on-spot applications. In this study, a method of redundant second generation wavelet transform based on the principle of interpolated subdivision was developed. With this method, subdivision was not needed during the decomposition. The lengths of approximation signals and detail signals were the same as those of original ones, so the data volume was twice that of original signals; besides, the data redundancy characteristic also guaranteed the excellent analysis effect of the method. The analysis of the acoustic emission data from the faults of on-spot low-speed heavy-duty gears validated the redundant second generation wavelet transform in the processing and denoising of acoustic emission signals. Furthermore, the analysis illustrated that the acoustic emission testing could be used in the fault diagnosis of on-spot low-speed heavy-duty gears and could be a significant supplement to vibration testing diagnosis. PMID:22346592

Gao, Lixin; Zai, Fenlou; Su, Shanbin; Wang, Huaqing; Chen, Peng; Liu, Limei

2011-01-01

436

Neural Networks and Fault Probability Evaluation for Diagnosis Issues  

PubMed Central

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

Lefebvre, Dimitri; Guersi, Noureddine

2014-01-01

437

A wavelet decomposition analysis of vibration signal for bearing fault detection  

NASA Astrophysics Data System (ADS)

This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transformed into frequency domain using a frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level was calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show significant deviation in retaining RMS value after a few levels of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended in on-line monitoring while the machine is on operation.

Nizwan, C. K. E.; Ong, S. A.; Yusof, M. F. M.; Baharom, M. Z.

2013-12-01

438

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

Microsoft Academic Search

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

Niu Wu; Xu Liangfa; Hu Sanguo

2010-01-01

439

The fault diagnosis technology based on fractal geometry for logging truck engine  

Microsoft Academic Search

The paper discusses the fundamental conceptions and properties of fractal geometry. The definitions of fractal dimension are\\u000a described and the methods of calculating fractal dimension are introduced. The paper researches the peculiarities of fault\\u000a diagnosis for logging truck engine and puts forward the technical way of diagnosing the faults with the help of the fractal\\u000a geometry.

Du Yuanhu; Zhu Jianxin; Wu Yuecheng

1996-01-01

440

Dynamic model-based fault detection and diagnosis residual considerations for vapor compression systems  

Microsoft Academic Search

This paper presents a first look at the dynamic impact of faults on vapor compression systems. Low-order control-oriented dynamic models of subcritical vapor compression cycles are used to develop sensitivity tools that enhance the residual design procedure of dynamic model-based fault detection and diagnosis algorithms. Also, experimental results are presented that confirm the sensitive outputs usefulness in an FDD algorithm.

Michael C. Keir; Andrew G. Alleyne

2006-01-01

441

Fault detection and diagnosis using neural network approaches  

NASA Technical Reports Server (NTRS)

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.

Kramer, Mark A.

1992-01-01

442

Model-Based Fault Diagnosis for Turboshaft Engines  

NASA Technical Reports Server (NTRS)

Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

1998-01-01

443

Developing a new transformer fault diagnosis system through evolutionary fuzzy logic  

SciTech Connect

To improve the diagnosis accuracy of the conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. Using the IEC/IEEE DGA criteria as references, a preliminary framework of the fuzzy diagnosis system is first built. Based on previous dissolved gas test records and their actual fault types, the proposed EP-based development technique is then employed to automatically modify the fuzzy if-then rules and simultaneously adjust the corresponding membership functions. In comparison to results of the conventional DGA and the artificial neural networks (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

Huang, Y.C.; Huang, C.L. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Dept. of Electrical Engineering] [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Dept. of Electrical Engineering; Yang, H.T. [Chung Yuan Christian Univ., Chung-Li (Taiwan, Province of China). Dept. of Electrical Engineering] [Chung Yuan Christian Univ., Chung-Li (Taiwan, Province of China). Dept. of Electrical Engineering

1997-04-01

444

A neural network prototype for fault detection and diagnosis of heating systems  

SciTech Connect

An artificial neural network (ANN) prototype for fault detection and diagnosis (FDD) in complex heating systems is presented in this paper. The six operating modes with faults used to develop this prototype stemmed from a detailed investigation in cooperation with heating systems maintenance experts, and are among the most important operating faults for this type of system. The prototype has been developed by using the daily values obtained by a preprocessing procedure of the simulation data of one reference heating system, and then generalizing to four heating systems not used during the training phase. This paper demonstrates the feasibility of using ANNs for detecting and diagnosing faults in heating systems provided that training data representative of the behavior of the systems with and without faults are available.

Li, X.; Visier, J.C.; Vaezi-Nejad, H. [French Scientific and Technical Building Center, Marne-la-Vallee (France). HVAC Dept.

1997-12-31

445

Customized multiwavelets for planetary gearbox fault detection based on vibration sensor signals.  

PubMed

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

446

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals  

PubMed Central

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

447

Fault Features of Large Rotating Machinery and Diagnosis Using Sensor Fusion  

NASA Astrophysics Data System (ADS)

Large rotating machinery such as turbines and compressors are the key equipment in oil refineries, power plants, and chemical engineering plants. To minimize the economic loss incurred because of the defects of malfunctions of these machines, diagnosis is very important. Currently, diagnosis is carried out mainly using spectral analysis. In spite of being effective in detecting the faults (monitoring), spectral analysis is often ineffective in pin-pointing what the fault is (diagnosis). This is due to the fact that it cannot clarify the spatial and temporal features in the sensor signals that are correlated to different types of faults. In this paper, phase spectra, holospectra, purified orbit diagrams, and filtered orbit diagrams are used in searching for fault features. From the data obtained from more than 50 practical machines, distinct fault features and diagnostic induces are found for 11 different types of faults including unbalance, cracks, misalignment, rub, loose bearing caps, oil whirl, surge, fluid excitation, rotating stall, electric power supply fluctuation, and pipe excitation. Accordingly, a diagnostic procedure is proposed.

Chen, Y. D.; Du, R.; Qu, L. S.

1995-11-01

448

Fault diagnosis engineering in molecular signaling networks: an overview and applications in target discovery.  

PubMed

Fault diagnosis engineering is a key component of modern industrial facilities and complex systems, and has gone through considerable developments in the past few decades. In this paper, the principles and concepts of molecular fault diagnosis engineering are reviewed. In this area, molecular intracellular networks are considered as complex systems that may fail to function, due to the presence of some faulty molecules. Dysfunction of the system due to the presence of a single or multiple molecules can ultimately lead to the transition from the normal state to the disease state. It is the goal of molecular fault diagnosis engineering to identify the critical components of molecular networks, i.e., those whose dysfunction can interrupt the function of the entire network. The results of the fault analysis of several signaling networks are discussed, and possible connections of the findings with some complex human diseases are examined. Implications of molecular fault diagnosis engineering for target discovery and drug development are outlined as well. PMID:20491069

Abdi, Ali; Emamian, Effat S

2010-05-01

449

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

NASA Technical Reports Server (NTRS)

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.

Bosworth, Edward L., Jr.

1987-01-01

450

Application of a radial basis function (RBF) neural network for fault diagnosis in a HVDC system  

SciTech Connect

The application of a Radial Basis Function (RBF) Neural Network (NN) for fault diagnosis in a HVDC system is presented in this paper. To provide a reliable pre-processed input to the RBF NN, a new pre-classifier is proposed. This pre-classifier consists of an adaptive filter (to track the proportional values of the fundamental and average components of the sensed system variables), and a signal conditioner which uses an expert Knowledge Base (KB) to aid the pre-classification of the signal. The proposed method of fault diagnosis is evaluated using simulations performed with the EMTP package.

Narendra, K.G.; Khorasani, K.; Patel, R. [Concordia Univ., Montreal, Quebec (Canada). Electrical and Computer Engineering Dept.; Sood, V.K. [Hydro-Quebec, Varennes, Quebec (Canada)

1995-12-31

451

Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings  

Microsoft Academic Search

Localized damage of rolling element bearings generates periodic impulses during running. The repeating frequency of impulses is a key indicator for diagnosing the localized damage of bearings. A new method, called Teager energy spectrum, is proposed to diagnose the faults of rolling element bearings. It exploits the unique advantages of Teager energy operator in detecting transient components in signals to

Zhipeng Feng; Tianjin Wang; Ming J. Zuo; Fulei Chu; Shaoze Yan

2011-01-01

452

Compression of test responses techniques in fault detection and diagnosis  

SciTech Connect

The compressor's total error-masking probability Q{sub t} (the expected probability that manifestation of faults as errors in a test response will be undetected under the compression process) is defined. It follows that the drawback of the linear feedback shift register (LFSR) compressors reside in the evaluation of Q{sub t} which involved laborious computation in obtaining the distribution of errors. However, due to the simple structure of a programmable logic array (PLA), it is shown that almost all single cross-point faults in PLAs are detected by the test procedure using LFSRs such that the distribution of errors need not be obtained. A quadratic compression technique is developed to overcome the drawback of the LFSRs technique. The approach is based the concept of robust compressors which incorporates the prior knowledge on the statistics of fault-free responses to achieve a guaranteed error-masking probability independent on distributions of errors. A construction of optimal codes for the minimax criterion on error detection based on bent functions is presented. Quadratic codes provide equal protection against all error patterns, hence, offer an efficient technique for design of fault-tolerant-computing VLSI devices.

Nagvajara, P.

1989-01-01

453

Transformer Fault Diagnosis by Dissolved-Gas Analysis  

Microsoft Academic Search

The great majority of incipient faults occurring in power transformers gives evidence of their presence early in their developmental stages. Oil and oil-impregnated electrical insulating materials can decompose under the influence of thermal and electrical stresses generating gaseous decomposition products which dissolve in the mineral oil. The nature and the amount of the individual component gases extracted from the oil

Joseph J. Kelly

1980-01-01

454

Node-fault diagnosis and a design of testability  

Microsoft Academic Search

A concept ofk-node-fault testability is introduced. A sufficient and almost necessary condition for testability as well as the test procedure is presented. This condition is further evolved to a necessary and almost sufficient topological condition for testability. A unique feature of this condition is that it depends only on the graph of the circuit, not on the element values. Based

ZHENG F. HUANG; CHEN-SHANG LIN; RUEY-WEN LIU

1983-01-01

455

Neural-network-based motor rolling bearing fault diagnosis  

Microsoft Academic Search

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

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

2000-01-01

456

Robust Fault Diagnosis for Atmospheric Reentry Vehicles: A Case Study  

Microsoft Academic Search

This paper deals with the design of robust model-based fault detection and isolation (FDI) systems for atmospheric reentry vehicles. This work draws expertise from actions undertaken within a project at the European level, which develops a collaborative effort between the University of Bordeaux, the European Space Agency, and European Aeronautic Defence and Space Company Astrium on innovative and robust strategies

Alexandre Falcoz; David Henry; Ali Zolghadri

2010-01-01

457

Predicting Future States With Dimensional Markov Chains for Fault Diagnosis  

Microsoft Academic Search

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

Ian Morgan; Honghai Liu

2009-01-01

458

Application of fault diagnosis expert system in grinding process  

Microsoft Academic Search

Directing at the complex structure, cockamamie production technology and great operation difficulty of vertical roller mill, this paper constructs the fault expert system to guide and optimize the grinding process. First, the expert database is established with fuzzy cluster analysis, principal component analysis (PCA) and tendency analysis. With support vector machine (SVM) regression, the multiple regression equations of main operation

Wei Qin; Wenjun Yan; Jing Xu

2010-01-01

459

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

E-print Network

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

Paris-Sud XI, Université de

460

FDTC '08. 5th Workshop on Fault Diagnosis and Tolerance in Cryptography, pages 9298, August 2008. IEEE-CS Press.  

E-print Network

FDTC '08. 5th Workshop on Fault Diagnosis and Tolerance in Cryptography, pages 92­98, August 2008. IEEE-CS Press. Errata 2008-09-10. Fault Attack on Elliptic Curve with Montgomery Ladder Implementation Marguerite, 35174 Bruz, France Frederic.Valette@m4x.org Abstract In this paper, we present a new fault attack

Lercier, Reynald

461

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

Microsoft Academic Search

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,

Peng Chen; Toshio Toyota; Zhengja He

2001-01-01

462

Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration signals emanated from defective rolling element bearings  

NASA Astrophysics Data System (ADS)

Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.

Paliwal, Deepak; Choudhur, Achintya; Govandhan, T.

2014-06-01

463

Artificial neural network application for space station power system fault diagnosis  

NASA Technical Reports Server (NTRS)

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

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

1995-01-01

464

An approach for the state estimation of Takagi-Sugeno models and application to sensor fault diagnosis  

E-print Network

, sensor fault diagnosis. I. INTRODUCTION The Takagi-Sugeno (TS) fuzzy model is a popular and importantAn approach for the state estimation of Takagi-Sugeno models and application to sensor fault diagnosis Dalil Ichalal, Beno^it Marx, Jos´e Ragot, Didier Maquin Abstract-- In this paper, a new method

Paris-Sud XI, Université de

465

512 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 Fault Detection and Diagnosis in an Induction  

E-print Network

the diagnosis process focuses on the operation of the inverter. Faults that may occur within the machine512 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 Fault Detection and Diagnosis in an Induction Machine Drive: A Pattern Recognition Approach Based on Concordia Stator Mean

Paris-Sud XI, Université de

466

Development and implementation of a power system fault diagnosis expert system  

SciTech Connect

This paper describes a fault diagnosis expert system installed at the tohoku Electric Power Company. The main features of this system are careful selection of the inferencing input data, rapid inferencing, integration of the expert system with other systems in a practical structure, and the adoption of a domain shell. This system aims for improved practicability by using time-tagged data from circuit breakers, protective relays, and automatic reclosing relays in addition to the input data used in earlier systems. Furthermore, this system also uses data from fault detection systems that locate fault points within electric stations. This system uses an AI-specific back-end processor to perform inferencing rapidly. Additionally, this fault diagnosis expert system is interfaced and integrated with a restorative operations expert system, an intelligent alarm processing system, and a protective relay setting and management system. Authors developed and adopted a power system fault diagnosis domain shell to ease system development, and used the protective relay operation simulation function of a protective relay setting and management system for system verification.

Minakawa, T.; Ichikawa, Y. [Tohoku Electric Power Co., Sendai (Japan)] [Tohoku Electric Power Co., Sendai (Japan); Kunugi, M.; Wada, N.; Shimada, K.; Utsunomiya, M. [Toshiba Corp., Tokyo (Japan)] [Toshiba Corp., Tokyo (Japan)

1995-05-01

467

A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems  

E-print Network

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

Bresolin, Davide; 10.4204/EPTCS.54.17

2011-01-01

468

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

NASA Astrophysics Data System (ADS)

In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a facility, respectively. The set-covering matrix encapsulates the relationship among the rows (tests or demand points) and columns (faults or locations) of the system at each time. By relaxing the coupling constraints using Lagrange multipliers, the DSC problem can be decoupled into independent subproblems, one for each column. Each subproblem is solved using the Viterbi decoding algorithm, and a primal feasible solution is constructed by modifying the Viterbi solutions via a heuristic. The proposed Viterbi-Lagrangian relaxation algorithm (VLRA) provides a measure of suboptimality via an approximate duality gap. As a major practical extension of the above problem, we also consider the problem of diagnosing faults with delayed test outcomes, termed delay-dynamic set-covering (DDSC), and experiment with real-world problems that exhibit masking faults. Also, we present simulation results on OR-library datasets (set-covering formulations are predominantly validated on these matrices in the literature), posed as facility location problems. Finally, we implement these algorithms to solve problems in aerospace and automotive applications. Firstly, we address the diagnostic ambiguity problem in aerospace and automotive applications by developing a dynamic fusion framework that includes dynamic multiple fault diagnosis algorithms. This improves the correct fault isolation rate, while minimizing the false alarm rates, by considering multiple faults instead of the traditional data-driven techniques based on single fault (class)-single epoch (static) assumption. The dynamic fusion problem is formulated as a maximum a posteriori decision problem of inferring the fault sequence based on uncertain outcomes of multiple binary classifiers over time. The fusion process involves three steps: the first step transforms the multi-class problem into dichotomies using error correcting output codes (ECOC), thereby solving the concomitant binary classification problems; the second step fuses the outcomes of multiple binary classifiers over time using a sliding window or block dynamic fusi

Kodali, Anuradha

469

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

NASA Technical Reports Server (NTRS)

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

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

2010-01-01

470

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

NASA Astrophysics Data System (ADS)

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

Yuan, Shengfa; Chu, Fulei

2007-04-01

471

Data Mining in Multi-Dimensional Functional Data for Manufacturing Fault Diagnosis  

SciTech Connect

Multi-dimensional functional data, such as time series data and images from manufacturing processes, have been used for fault detection and quality improvement in many engineering applications such as automobile manufacturing, semiconductor manufacturing, and nano-machining systems. Extracting interesting and useful features from multi-dimensional functional data for manufacturing fault diagnosis is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of functional data types, high correlation, and nonstationary nature of the data. This chapter discusses accomplishments and research issues of multi-dimensional functional data mining in the following areas: dimensionality reduction for functional data, multi-scale fault diagnosis, misalignment prediction of rotating machinery, and agricultural product inspection based on hyperspectral image analysis.

Jeong, Myong K [ORNL; Kong, Seong G [ORNL; Omitaomu, Olufemi A [ORNL

2008-09-01

472

An expert system for fault diagnosis integrated in existing SCADA systems  

SciTech Connect

The operators of Hydro-Quebec's 9 Regional Control Centers (RCC) are sometimes overloaded by the number of alarm messages produced when automatic controls operate to clear faults. To help operators, Hydro-Quebec has developed an expert system to perform a continuous analysis of alarm messages, automatically detect the application of the protection or restoration control and then produced a concise, real-time diagnosis identifying the origin and consequences of the fault. This paper describes the fault diagnosis problem faced by operators and the requirements met by the LANGAGE Expert System. It gives an overview of the knowledge representation technique used and of the actual distribution of the application that allows the execution of the various modules of LANGAGE in many computers and workstations installed in parallel with the existing SCADA system.

Bernard, J.P.; Durocher, D. (Hydro-Quebec, Montreal, Quebec (Canada))

1994-02-01

473

The application and research of the intelligent fault diagnosis for marine diesel engine  

Microsoft Academic Search

The marine diesel engine is a complex system, which has the important function to guarantee the marine security. In this paper a novel approach of optimizing and training fuzzy neural network based on the ant colony algorithm is proposed for the intelligent fault diagnosis of this kind of diesel engine. The structure and the parameter of fuzzy neural network for

Li Peng; Liu Lei

2008-01-01

474

Condition monitoring and fault diagnosis of electrical motors-a review  

Microsoft Academic Search

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

Subhasis Nandi; Hamid A. Toliyat; Xiaodong Li

2005-01-01

475

Generating and exploiting bayesian networks for fault diagnosis in airplane engines  

Microsoft Academic Search

Bayesian Networks has been proven to be successful tool for fault diagnosis. There are a variety of approaches for learning the structure of Bayesian Networks from data. This learning problem has been proven to be NP-hard hence none of the approaches are exact when no prior knowledge about the domain of the variables exists. Our approach is based on searching

M. Çetin Yavuz; Ferat Sahin; Ziya Arnavut; Önder Uluyol

2006-01-01

476

Fault Diagnosis in an Industrial Process Using Bayesian Networks: Application of the Junction Tree Algorithm  

Microsoft Academic Search

In this paper we present a Bayesian Network for fault diagnosis used in an industrial tanks system. We obtain the Bayesian Network first and later based on this, we build a defined structure as Junction Tree. This tree is where we spread the probabilities with the algorithm known as LAZYAR (also Junction Tree). Nowadays the state of the art in

Julio C. Ramírez; G. Muoz; Ludivina Gutierrez

2009-01-01

477

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

E-print Network

model is applied to rank the suspects so that the most suspicious one will be ranked at or near the top. Several fast filtering methods are used to prune unrelated suspects. Finally, to refine the diagnosis, fault simulation is performed on the top...

Wu, Lei

2009-05-15

478

Design error diagnosis in digital circuits with stuck-at fault model  

Microsoft Academic Search

In this paper we describe in detail a new method for the single gate-level design error diagnosis in combinational circuits. Distinctive features of the method are hierarchical approach (the localizing procedure starts at the macro level and finishes at the gate level), use of stuck-at fault model (it is mapped into design error domain only in the end), and design

A. Jutman; R. Ubar

2000-01-01

479

An Integrated Process for System Maintenance, Fault Diagnosis and Support1  

Microsoft Academic Search

This paper presents an overview of an integrated process for system maintenance, fault diagnosis and support. The solution is based on Qualtech System, Inc.'s (QSI's) TEAMS toolset for integrated diagnostics and involves several key innovations. As a showcase of the integrated solution, QSI, along with Antech Systems and Carnegie Mellon University (CMU), have recently completed a research project for the

Sudipto Ghoshal; Roshan Shrestha; Anindya Ghoshal; Venkatesh Malepati; Somnath Deb; Krishna Pattipati

1999-01-01

480

Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker  

Microsoft Academic Search

Based on empirical mode decomposition (EMD) method and support vector machine (SVM), a new method for the fault diagnosis of high voltage circuit breaker (CB) is proposed. The feature extraction method based on improved EMD energy entropy is detailedly analyzed and SVM is employed as a classifier. Radial basis function (RBF) is adopted as the kernel function of SVM and

Jian Huang; Xiaoguang Hu; Fan Yang

2011-01-01

481

An algorithm of soft fault diagnosis for analog circuit based on the optimized SVM by GA  

Microsoft Academic Search

A soft fault diagnosis method for analog circuits based on support vector machine (SVM)is developed in this paper. SVM is a novel machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The multi-classification SVM methods including one versus rest, one versus one, and decision

Hua Li; Yong Xin Zhang

2009-01-01

482

Power transformer fault diagnosis based on support vector machine with cross validation and genetic algorithm  

Microsoft Academic Search

Support vector machine (SVM) classifier has been successfully applied to power transformer fault diagnosis. However, there is no theoretical basis or effective method to select appropriate SVM classifier parameters which have a crucial influence on the classification accuracy. Currently, the main method is cut and try based on experience. In this study, genetic algorithm (GA) is employed to optimize the

JinLiang Yin; YongLi Zhu; GuoQin Yu

2011-01-01

483

Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers  

Microsoft Academic Search

Chillers constitute a significant portion of energy consumption equipment in heating, ventilating and air-conditioning (HVAC) systems. The growing complexity of building systems has become a major challenge for field technicians to troubleshoot the problems manually; this calls for automated ldquosmart-service systemsrdquo for performing fault detection and diagnosis (FDD). The focus of this paper is to develop a generic FDD scheme

Setu Madhavi Namburu; Mohammad S. Azam; Jianhui Luo; Kihoon Choi; Krishna R. Pattipati

2007-01-01

484

Fault diagnosis of turbo-generator based on support vector machine and genetic algorithm  

Microsoft Academic Search

Support vector machine (SVM) can overcome the drawbacks of artificial neural network, which has been widely used for pattern recognition in recent years. In the study, a novel method based on support vector machine and genetic algorithm (GA-SVM) model is adopted to fault diagnosis of turbo-generator, in which genetic algorithm (GA) dynamically optimizes the values of SVM's parameters C and

Shen Xiao-feng; Shen Yu; Guo Lin

2009-01-01

485

On Electronic Equipment Fault Diagnosis Using Least Squares Wavelet Support Vector Machines  

Microsoft Academic Search

A systematic approach for fault diagnosis of analog circuits based on least squares wavelet support vector machines and wavelet lifting transform is presented, and is used in the scout radar electronic equipment. Firstly, output voltage signals under faulty conditions are obtained from analog circuits test points and noise is removed from signals with wavelet lifting transform. Then wavelet coefficients of

Zhiyong Luo; Zhongke Shi

2006-01-01

486

The application of chaos support vector machines in transformer fault diagnosis  

Microsoft Academic Search

Due to the lack of typical damage samples in the transformer fault diagnosis, a new method based on chaos support vector machines (CSVMs) was proposed. According to the method, the five characteristic gases dissolved in transformer oil were extracted by the K-means clustering (KMC) method as feature vectors, which were input to chaotic optimal multi-classified SVMs for training. Then the

Jisheng Li; Xuefeng Zhao; Zhenquan Sun; Yanming Li

2009-01-01

487

Support vector regression and particle swarm optimization algorithm for intelligent electronic circuit fault diagnosis  

Microsoft Academic Search

In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables

Wen-Jie Tian; Yue Tian; Lan Ai; Ji-Cheng Liu

2009-01-01

488

Research of fault diagnosis method of analog circuit based on improved support vector machines  

Microsoft Academic Search

This paper propose improved support vector machine algorithm. The algorithm includes preprocessing the sample training set, improvement of the binary tree classification algorithm and incremental sample learning algorithm. Considering the specific classification precision requirements of analog circuit fault diagnosis, the three algorithms are integrated, and achieve good results. The simulation of analog circuit demonstrate that the improved algorithm has higher

Hua Li; Bin Yin; Nan Li; Jianhua Guo

2010-01-01

489

Decision Tree Support Vector Machine based on Genetic Algorithm for fault diagnosis  

Microsoft Academic Search

Decision tree support vector machine (DTSVM), which combines SVM and decision tree using the concept of dichotomy, is proposed to solve the multi-class fault diagnosis tasks. Since the classification performance of DTSVM is closely related to its structure, genetic algorithm is introduced into the formation of decision tree, to cluster the multi-classes with maximum distance between the clustering centers of

Qiang Wang; Huanhuan Chen; Yi Shen

2008-01-01

490

Adaptive Set Observers Design for Nonlinear Continuous-Time Systems: Application to Fault Detection and Diagnosis  

E-print Network

The paper deals with joint state and parameter estimation for nonlinear continuous-time systems. Based on a guaranteed LPV approximation, the set adaptive observers design problem is solved avoiding the exponential complexity obstruction usually met in the set-membership parameter estimation. Potential application to fault diagnosis is considered. The efficacy of the proposed set adaptive observers is demonstrated on several examples.

Efimov, Denis; Zolghadri, Ali

2010-01-01

491

An expert system for transformer fault diagnosis using dissolved gas analysis  

Microsoft Academic Search

A prototype of an expert system based on the dissolved gas analysis (DGA) technique for diagnosis of a suspected transformer faults and their maintenance actions is developed. The synthetic method is proposed to assist the popular gas ratio method. Moreover, the uncertainty of key gas analysis, norms threshold and gas ratio boundaries are managed by using a fuzzy set concept.

C. E. Lin; J.-M. Ling; C.-L. Huang

1993-01-01

492

Model Based Building Chilled Water Loop Delta-T Fault Diagnosis  

E-print Network

distribution loop by the building's chilled water system under various loading conditions. The results show model-based building chilled water Loop delta-T fault diagnosis is an effective way to evaluate existing building chilled water loop delta-T performance...

Wang, L.; Watt, J.; Zhao, J.

2012-01-01

493

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

E-print Network

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 bioassay operations. Moreover, since disposable biochips are being targeted for a highly competitive

Chakrabarty, Krishnendu

494

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

Microsoft Academic Search

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

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

2008-01-01

495

Research of the Lifting Wavelet Arithmetic and Applied in Rotary Mechanic Fault Diagnosis  

Microsoft Academic Search

The lifting wavelet transform is completely based on the space-time area instead of relying on Fourier transform, so it can construct wavelet in non-shift area to achieve the separation of signal in different frequency bands. In this paper, the lifting scheme of wavelet and its multiphase expression are analysed, and applied to fault diagnosis of gears rolling bearings and rotor

S Q Zhang; N He; J T Lv; X H Xu; X Y Zang

2006-01-01

496

A review of recent advances in wind turbine condition monitoring and fault diagnosis  

Microsoft Academic Search

The state-of-the-art advancement in wind turbine condition monitoring and fault diagnosis for the recent several years is reviewed. Since the existing surveys on wind turbine condition monitoring cover the literatures up to 2006, this review aims to report the most recent advances in the past three years, with primary focus on gearbox and bearing, rotor and blades, generator and power

Bin Lu; Yaoyu Li; Xin Wu; Zhongzhou Yang

2009-01-01

497

Hierarchical Fault Diagnosis and Fuzzy Rule-Based Reasoning for Satellites Formation Flight  

Microsoft Academic Search

Formation flying is an emerging area in the Earth and space science and technology domains that utilize multiple inexpensive spacecraft by distributing the functionalities of a single platform spacecraft among miniature inexpensive platforms. Traditional spacecraft fault diagnosis and health monitoring practices involve around-the-clock monitoring, threshold checking, and trend analysis of a large amount of telemetry data by human experts that

A. Barua; K. Khorasani

2011-01-01

498

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

NASA Astrophysics Data System (ADS)

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.

Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

2014-12-01

499