Fault-Diagnosis Systems

An Introduction from Fault Detection to Fault Tolerance

By: Rolf Isermann

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Paperback | 13 October 2005

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With increasing demands for efficiency and product quality and progressing integration of automatic control systems in high-cost mechatronic and safety-critical processes, the field of supervision (or monitoring), fault detection and fault diagnosis plays an important role. The book gives an introduction into advanced methods of fault detection and diagnosis (FDD). After definitions of important terms, the reliability, availability, safety and systems integrity of technical processes is considered. Then fault-detection methods for single signals without models like limit and trend checking and with harmonic and stochastic models, like Fourier analysis, correlation and wavelets are treated. This is followed by fault detection with process models using the relationships between signals like parameter estimation, parity equations, observers and principal component analysis. The treated fault-diagnosis methods include classification methods from Bayes classification to neural networks with decision trees and inference methods from approximate reasoning with fuzzy logic to hybrid fuzzy-neuro systems.Especially for safety-critical processes fault-tolerant systems are required. Basic redundant structures like n-out-of-m systems, cold and hot standby are considered and ways to design fault-tolerant sensors, actuators and control systems are outlined.Several practical examples for fault detection and diagnosis of DC motor drives, a centrifugal pump, automotive suspension and tire show applications.

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Non-Fiction Computing & I.T.Computer Science Systems Analysis & Design Engineering & Technology Energy Technology & Engineering Electrical Engineering Technology in General Engineering in General Electronics & Communications Engineering Electronics Engineering Automatic Control Engineering

Robotics Maths for Engineers Mechanical Engineering & Materials Production & Industrial Engineering Industrial Quality Control

Introduction	p. 1
Process automation and process supervision	p. 1
Contents	p. 3
Historical notes	p. 8
Fundamentals
Supervision and fault management of processes - tasks and terminology	p. 13
Basic tasks of supervision	p. 13
Faults, failures, malfunctions	p. 20
Reliability, availability, safety	p. 21
Fault tolerance and redundancy	p. 24
Knowledge-based fault detection and diagnosis	p. 25
Analytic symptom generation	p. 26
Heuristic symptom generation	p. 27
Fault diagnosis	p. 27
Implementation issues	p. 27
Problems	p. 29
Reliability, Availability and Maintainability (RAM)	p. 31
Reliability	p. 31
Type of faults	p. 31
Reliability estimation	p. 32
Connected elements	p. 37
Maintainability	p. 39
Availability	p. 42
Fault management for total life cycles	p. 42
Some failure statistics	p. 42
Statistics of components	p. 43
Statistics of systems	p. 45
Problems	p. 47
Safety, Dependability and System Integrity	p. 49
Reliability analysis	p. 50
Event tree analysis (ETA) and fault tree analysis (FTA)	p. 50
Failure mode and effects analysis (FMEA)	p. 51
Hazard-analysis (HA)	p. 54
Risk classification	p. 54
Integrated reliability and safety design	p. 57
Problems	p. 57
Fault-Detection Methods
Process Models and Fault Modelling	p. 61
Fault models	p. 63
Basic fault models	p. 63
Examples for fault models	p. 64
Process models	p. 71
Theoretical and experimental modelling	p. 71
Static process models	p. 73
Linear dynamic process models	p. 74
Nonlinear process models	p. 81
Problems	p. 82
Signal models	p. 83
Harmonic oscillations	p. 83
Single oscillations	p. 83
Superposition	p. 84
Amplitude modulation	p. 84
Frequency and phase modulation	p. 85
Beating (Libration)	p. 85
Superposition and nonlinear characteristics	p. 88
Stochastic signals	p. 88
Continuous-time stochastic signals	p. 89
Discrete-time stochastic signals	p. 91
Problems	p. 93
Fault detection with limit checking	p. 95
Limit checking of absolute values	p. 95
Trend checking	p. 95
Change detection with binary thresholds	p. 97
Estimation of mean and variance	p. 97
Statistical tests for change detection	p. 100
Change detection with fuzzy thresholds	p. 107
Adaptive thresholds	p. 107
Plausibility checks	p. 108
Problems	p. 109
Fault detection with signal models	p. 111
Analysis of periodic signals	p. 111
Bandpass filtering	p. 113
Fourier analysis	p. 113
Correlation Functions	p. 114
Fourier transformation	p. 115
Fast Fourier transformation (FFT)	p. 117
Maximum entropy spectral estimation	p. 122
Cepstrum analysis	p. 127
Analysis of non-stationary periodic signals	p. 129
Short-time Fourier transform	p. 129
Wavelet transform	p. 129
Analysis of stochastic signals	p. 131
Correlation analysis	p. 131
Spectrum analysis	p. 133
Signal parameter estimation with ARMA-models	p. 133
Vibration analysis of machines	p. 134
Vibrations of rotating machines	p. 134
Vibration signal models	p. 135
Vibration analysis methods	p. 139
Speed signal analysis of combustion engines	p. 141
Problems	p. 146
Fault detection with process-identification methods	p. 147
Identification with correlation functions	p. 149
Estimation of correlation functions	p. 149
Convolution	p. 150
Parameter estimation for linear processes	p. 151
Method of least squares (LS)	p. 152
Extended least squares (ELS) method	p. 158
Modifications of basic recursive estimators	p. 159
Parameter estimation of time-varying processes	p. 164
Parameter estimation for continuous-time signals	p. 167
Parameter estimation in closed loop	p. 169
Identification of nonlinear processes	p. 170
Parameter estimation for nonlinear static processes	p. 171
Parameter Estimation with Classical Nonlinear Models	p. 172
Artificial Neural Networks for Identification	p. 173
Identification with Grid-based Look-up Tables for Static Process	p. 188
Parameter Estimation for Non-continuously Differentiable Nonlinear Processes (Friction and Backlash)	p. 190
Symptom generation with identification models	p. 193
Problems	p. 195
Fault detection with parity equations	p. 197
Parity equations with transfer functions	p. 197
Parity equations with state-space models	p. 203
Continuous-time parity approach	p. 203
Discrete-time parity approach	p. 207
Properties of residuals	p. 210
Generation of enhanced residuals	p. 210
Generation of structured residuals	p. 213
Sensitivity of parity equations	p. 219
Parity equations for nonlinear processes	p. 220
Parity equations for special nonlinear processes	p. 220
Parity equation for nonlinear, local linear models	p. 221
Parameter estimation with parity equations	p. 226
Problems	p. 228
Fault detection with state observers and state estimation	p. 231
State observers	p. 231
Additive faults	p. 232
Multiplicative faults	p. 234
Fault isolation with state observers	p. 234
State estimation (Kalman filter)	p. 237
Output observers	p. 243
Comparison of the parity- and observer-based approaches	p. 246
Comparison of residual equations	p. 246
Comparison by simulations	p. 247
Problems	p. 252
Fault detection of control loops	p. 253
Effects of faults on the closed loop performance	p. 253
Signal-based methods for closed-loop supervision	p. 254
Methods for the detection of oscillations in closed loops	p. 260
Model-based methods for closed-loop supervision	p. 261
Problems	p. 265
Fault detection with Principal Component Analysis (PCA)	p. 267
Principal components	p. 267
Fault detection with PCA	p. 273
Comparison and combination of fault-detection methods	p. 279
Assumptions of model-based fault detection	p. 279
Suitability of model-based fault-detection methods	p. 282
Combination of different fault-detection methods	p. 283
Fault-Diagnosis Methods
Diagnosis procedures and problems	p. 287
Introduction to fault diagnosis	p. 287
Problems of fault diagnosis	p. 289
Diagnosis knowledge representation	p. 290
Prior knowledge	p. 290
Typical statistical symptom distributions	p. 291
Data size	p. 291
Symptom representation	p. 292
Problems	p. 292
Fault diagnosis with classification methods	p. 295
Simple pattern classification methods	p. 295
Bayes Classification	p. 296
Geometric classifiers	p. 299
Polynomial classification	p. 300
Decision trees	p. 302
Neural Networks for fault diagnosis	p. 305
Multi-layer perceptron networks	p. 306
Radial-basis function networks	p. 307
Clustering and self-organizing networks	p. 309
Problems	p. 310
Fault diagnosis with inference methods	p. 311
Fault trees	p. 312
Approximate reasoning	p. 317
Forward chaining	p. 318
Backward chaining	p. 322
Summary and comparison	p. 323
Hybrid neuro-fuzzy systems	p. 324
Structures	p. 324
Identification of membership functions	p. 329
Identification of rules with predefined membership functions	p. 330
Optimization methods	p. 332
Self-learning classification tree (SELECT)	p. 333
Problems	p. 343
Fault-Tolerant Systems
Fault-tolerant design	p. 347
Basic redundant structures	p. 347
Degradation steps	p. 351
Problems	p. 353
Fault-tolerant components and control	p. 355
Fault-tolerant sensors	p. 355
Hardware sensor redundancy	p. 355
Analytical sensor redundancy	p. 356
Fault-tolerant actuators	p. 358
Fault-tolerant communication	p. 359
Fault-tolerant control systems	p. 360
Automatic fault-management system	p. 362
Problems	p. 365
Application Examples
Fault detection and diagnosis of DC motor drives	p. 369
DC motor	p. 369
DC motor test bench	p. 369
Parity equations	p. 371
Parameter estimation	p. 373
Experimental results for fault detection	p. 373
Experimental results for fault diagnosis with SELECT	p. 376
Electrical throttle valve actuator	p. 382
Actuator setup	p. 382
Parameter estimation	p. 384
Parity equations	p. 387
Diagnostic equipment for quality control	p. 388
Fault detection and diagnosis of a centrifugal pump-pipe-system	p. 391
The pump-pipe-tank system	p. 391
Mathematical models of the centrifugal pump	p. 392
Parity equations and parameter estimation	p. 393
Fault detection and diagnosis of an automotive suspension and the tire pressures	p. 401
Mathematical model of a suspension and the test rig	p. 401
Parameter estimation (test rig)	p. 402
Parity equations (test rig)	p. 404
Experimental results with a driving vehicle	p. 405
Shock absorber fault detection during driving	p. 405
Tire pressure supervision with spectral analysis	p. 409
Appendix	p. 413
Terminology in fault detection and diagnosis	p. 413
State variable filtering of noisy signals to obtain signal derivations	p. 415
Fuzzy logic - a short introduction	p. 416
Basics	p. 416
Simplification for Fault Diagnosis	p. 420
Estimation of physical parameters for dynamic processes	p. 420
Introduction	p. 420
On the model structure for processes with lumped parameters	p. 422
Calculation of the physical process coefficients	p. 424
Example: Second order electrical circuit	p. 425
From Parallel to Hierarchical Rule Structures	p. 426
Parallel Rule Bases	p. 427
Hierarchical Rule Bases	p. 428
References	p. 435
Index	p. 469
Table of Contents provided by Ingram. All Rights Reserved.

Fault-Diagnosis Systems

An Introduction from Fault Detection to Fault Tolerance

At a Glance

Paperback

Shipping

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