
Fault Diagnosis
Models, Artificial Intelligence, Applications
By: Jszef Korbicz (Editor), Jan M. Koscielny (Editor), Zdzislaw Kowalczuk (Editor)
Hardcover | 1 December 2003
At a Glance
954 Pages
23.5 x 16.51 x 3.81
Hardcover
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| Methodology | p. 1 |
| Introduction | p. 3 |
| Diagnostics of process and its fundamental tasks | p. 3 |
| Main concepts | p. 6 |
| Aims of process diagnostics | p. 11 |
| General description of the diagnosed object | p. 13 |
| Basic concepts of process diagnostics | p. 19 |
| Summary | p. 25 |
| References | p. 26 |
| Moldels in the diagnostics of processes | p. 29 |
| Introduction | p. 29 |
| Relations in diagnostics | p. 30 |
| Moldels applied to fault detection | p. 31 |
| Physical equations | p. 32 |
| State equations of linear systems | p. 33 |
| State observers | p. 34 |
| Transfer functions of linear systems | p. 35 |
| Neural models | p. 37 |
| Fuzzy modles | p. 40 |
| Models applied to fault isolation and system state recognition | p. 44 |
| Models mapping the space of binary diagnostic signals into the space of faults or system states | p. 46 |
| Binary diagnostic matrix | p. 46 |
| Diagnostic trees and graphs | p. 48 |
| Rules and logic functions | p. 49 |
| Models mapping the space of multi-value diagnostic signals into the space of faults or systtem states | p. 50 |
| Information system | p. 50 |
| Other models | p. 53 |
| Modles mapping the space of continuous diagnostic signals into the space of faults or system states | p. 54 |
| Pattern pictures | p. 54 |
| Neural networks | p. 55 |
| Fuzzy neural networks | p. 55 |
| Summary | p. 55 |
| References | p. 56 |
| Process diagnostics methodology | p. 59 |
| Introduction | p. 59 |
| Fault detection | p. 59 |
| Fault detection using system models | p. 60 |
| Generation of residuals on the grounds of physical equations | p. 61 |
| Generation of residuals on the grounds of system transmittance | p. 62 |
| Generation of residuals using state equations | p. 64 |
| Generation of residuals on the grounds of state observers | p. 66 |
| Generation of residuals using on-line identification | p. 67 |
| Residual generation with neural and fuzzy models | p. 68 |
| Algorithms for making a decision on fault detection using residual value evaluation | p. 70 |
| Fault detection using tests of simple relationships existing between signals | p. 72 |
| Application of hardware redundancy | p. 72 |
| Application of feedback signals | p. 72 |
| Test of statistical relationships existing between process variables | p. 72 |
| Testing the relations existing between process variables | p. 73 |
| Methods of signal analysis and the testing of limits | p. 74 |
| Analysis of statistic signal parameters | p. 74 |
| Spectral analysis | p. 75 |
| Methods of limit checking | p. 76 |
| Fault isolation | p. 79 |
| Diagnosing based on the binary diagnostic matrix | p. 80 |
| Rules of parallel diagnostic inference on the assumption about single faults | p. 80 |
| Rules of series diagnostic inference on the assumption about single faults | p. 81 |
| Inference with the inconsistency of symptoms | p. 82 |
| System states with multiple faults | p. 83 |
| Parallel inference on the assumption about multiple faults | p. 85 |
| Series inference on the assumption about multiple faults | p. 87 |
| Diagnosing based on the information system | p. 87 |
| Parallel diagnostic inference based on the information system | p. 88 |
| Series diagnostic inference based on the information system | p. 88 |
| Methods of pattern recognition | p. 89 |
| Recognition of directions in the space of residuals | p. 91 |
| Other methods | p. 95 |
| Fault distinguishability | p. 95 |
| Fault distinguishability based on the binary diagnostic matrix | p. 96 |
| Distinguishability of system states based on the binary table of states | p. 97 |
| Fault distinguishability based on the information system | p. 98 |
| Fault distinguishability based on pattern recognition in the space of diagnostic signals | p. 100 |
| Fault distinguishability improvement by taking the dynamics of symptoms into account | p. 101 |
| Methods of the structural design of the set of detection algorithms | p. 101 |
| Generation of secondary residuals based on physical equations | p. 102 |
| Choice of a structural set of residuals generated on the basis of parity equations | p. 103 |
| Banks of observers | p. 105 |
| Design of a structured set of detection algorithms based on partial models | p. 106 |
| Minimising the set of detection algorithms | p. 107 |
| Fault identification | p. 108 |
| Residual equations | p. 109 |
| Residuals without the knowledge of the effect of faults | p. 1ll |
| Monitoring the system state | p. 112 |
| Summary | p. 113 |
| References | p. 114 |
| Methods of signal analysis | p. 119 |
| Introduction | p. 119 |
| Signal classification | p. 121 |
| Initial pre-processing of signals | p. 123 |
| Analogue-to-digital conversion of signals | p. 124 |
| Filtering | p. 124 |
| Smoothing | p. 129 |
| Averaging | p. 131 |
| Principal component analysis | p. 132 |
| Non-parametric methods of signal feature estimation | p. 134 |
| Scalar feature estimation | p. 134 |
| Spectral analysis | p. 135 |
| Higher order spectral analysis | p. 139 |
| Analysis with the use of the wavelet transform | p. 141 |
| Analysis with the use of the Wigner-Ville transform | p. 145 |
| Parametric methods of signal estimation | p. 145 |
| Signal features estimated with respect to object properties | p. 147 |
| Summary | p. 151 |
| References | p. 151 |
| Control theory methods in designing diagnostic systems | p. 155 |
| Introduction | p. 155 |
| Transfer function approach | p. 157 |
| Residue generation | p. 157 |
| Properties of the system matrix | p. 163 |
| Non-homogeneous residual reaction models | p. 167 |
| Homogeneous residual reaction models | p. 171 |
| Parity space approach | p. 173 |
| Deterministic assignment of state estimation | p. 177 |
| Full-order observer | p. 177 |
| Minimal-order observer | p. 180 |
| Observer matrix determination by pole placement | p. 186 |
| Detection observers of the Luenberger type | p. 191 |
| Linear Kalman filters | p. 198 |
| Models of estimated processes | p. 199 |
| Linear Kalman filtering founded on innovations | p. 200 |
| Summary | p. 207 |
| Appendix | p. 208 |
| References | p. 213 |
| Optimal detection observers based on eigenstructure assignment | p. 219 |
| Introduction | p. 219 |
| System modelling | p. 221 |
| Preliminary synthesis of residuals | p. 222 |
| Conditions for disturbance decoupling | p. 223 |
| Necessary condition for decoupling | p. 224 |
| Sufficient conditions for decoupling | p. 226 |
| Parameterisation of attainable eigensubspaces | p. 227 |
| Separate spectra of the observer and the object | p. 229 |
| Mutuality in the spectra of the observer and the object | p. 229 |
| Partial observer gain | p. 231 |
| Synthesis of a numerically robust state observer | p. 232 |
| Separate spectra of the observer and the object | p. 234 |
| Mutuality in the spectra of the observer and the object | p. 235 |
| Synthesis of a numerically robust decoupled state observer | p. 236 |
| Numerically robust attainable decoupling | p. 237 |
| Complete observer gain | p. 238 |
| Completely decoupled observers | p. 238 |
| Dead-beat design of residue generators | p. 239 |
| Residue generation using parity equations | p. 242 |
| Numerical example | p. 242 |
| Decoupled dead-beat residue generator | p. 243 |
| Properties of dead-bead observers | p. 244 |
| Properties of non-dead-bead observers | p. 249 |
| Robustness of dead-beat observers | p. 251 |
| Summary | p. 251 |
| Appendices | p. 253 |
| Observability of dynamic systems | p. 253 |
| Useful geometric relationships | p. 254 |
| References | p. 257 |
| Robust H∞-optimal synthesis of FDI systems | p. 261 |
| Introduction | p. 261 |
| FDI design task as optimal filtering in H∞ | p. 263 |
| Optimal filtering based on the basic modelling of generalised processes | p. 264 |
| Solution using the basic model | p. 267 |
| Optimal filtering based on the dual modelling of generalised plants | p. 272 |
| Solution using the dual model | p. 274 |
| FDI filtering with the instrumental reference signal | p. 280 |
| Synthesis of primary and secondary residual vectors | p. 283 |
| Numerical example | p. 286 |
| Summary | p. 290 |
| Appendices | p. 291 |
| Discrete-time models | p. 291 |
| Norms and spaces | p. 292 |
| Factorisation | p. 293 |
| Discrete Riccati equation | p. 294 |
| References | p. 295 |
| Artificial Intelligence | p. 299 |
| Evolutionary methods in designing diagnostic systems | p. 301 |
| Introduction | p. 301 |
| Evolutionary algorithms | p. 302 |
| Basic concepts of evolutionary search | p. 303 |
| Some evolutionary algorithms | p. 305 |
| Optimization tasks in designing FDI systems | p. 308 |
| Symptom extraction | p. 309 |
| Choice of the gain matrix for the robust non-linear observer via genetic programming | p. 309 |
| Designing the robust residual generator using multi-objective optimization and evolutionary algorithms | p. 312 |
| Evolutionary algorithms in the design of neural models | p. 314 |
| Symptom evaluation | p. 323 |
| Genetic clustering | p. 324 |
| Evolutionary algorithms in designing the rule base | p. 325 |
| Genetic adaptation of fuzzy systems | p. 327 |
| Summary | p. 329 |
| References | p. 329 |
| Artificial neural networks in fault diagnosis | p. 333 |
| Introduction | p. 333 |
| Structure of a neural fault diagnosis system | p. 334 |
| Neural models in modelling | p. 337 |
| Multi-layer perceptron | p. 337 |
| Recurrent networks | p. 339 |
| Neural networks of the GMDH type | p. 347 |
| Fault classification using neural networks | p. 352 |
| Multi-layer perceptron | p. 352 |
| Kohonen network | p. 352 |
| Radial basic networks | p. 354 |
| Multiple network structure | p. 356 |
| Selected applications | p. 357 |
| Two-tank laboratory system | p. 357 |
| Instrumentation fault detection | p. 365 |
| Actuator fault detection and isolation | p. 369 |
| Summary | p. 375 |
| References | p. 376 |
| Parametric and neural network Wiener and Hammerstein models in fault detection and isolation | p. 381 |
| Introduction | p. 381 |
| Wiener and Hammerstein models | p. 382 |
| Identification of Wiener and Hammerstein systems | p. 384 |
| Parametric and neural network Wiener and Hammerstein models | p. 386 |
| Parametric models | p. 387 |
| Neural network models | p. 388 |
| Fault detection. Estimating parameter changes | p. 389 |
| Definitions of the identification error | p. 390 |
| Hammerstein system. Parameter estimation of the residual equation | p. 393 |
| Wiener system. Parameter estimation of the residual equation | p. 396 |
| Five-stage sugar evaporator. Identification of the nominal model of steam pressure dynamics | p. 402 |
| Theoretical model | p. 402 |
| Experimental models | p. 403 |
| Estimation results | p. 404 |
| Summary | p. 407 |
| References | p. 407 |
| Application of fuzzy logic to diagnostics | p. 411 |
| Introduction | p. 411 |
| Fault detection | p. 412 |
| Wang and Mendel's fuzzy models | p. 413 |
| Construction of fuzzy models using Wang and Mendel's method | p. 413 |
| Modification of Wang and Mendel's method | p. 415 |
| Calculation of a residual on the basis of the fuzzy model | p. 416 |
| Fuzzy neural networks | p. 417 |
| Fuzzy neural networks with outputs in the form of singletons | p. 418 |
| TSK-type fuzzy neural networks | p. 420 |
| Fuzzy neural networks with outputs in the form of fuzzy sets | p. 422 |
| Example of fault detection | p. 423 |
| Fault isolation with the use of fuzzy logic | p. 428 |
| Fuzzy evaluation of residual values | p. 429 |
| Rules of inference | p. 431 |
| Fuzzy diagnostic inference | p. 433 |
| Example of fault isolation | p. 437 |
| Uncertainty of the diagnostic signals-faults relation | p. 441 |
| Fault isolation with the use of the fuzzy neural network | p. 442 |
| Realisation of fuzzy residual evaluation by the fuzzy neural network | p. 444 |
| Fault isolation in the fuzzy neural network | p. 445 |
| Example of fuzzy neural network application to fault isolation | p. 449 |
| Summary | p. 450 |
| References | p. 454 |
| Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems | p. 457 |
| Introduction | p. 457 |
| Identification of non-linear dynamic systems | p. 460 |
| Data acquisition and preparation | p. 460 |
| Table of Contents provided by Publisher. All Rights Reserved. |
ISBN: 9783540407676
ISBN-10: 3540407677
Published: 1st December 2003
Format: Hardcover
Language: English
Number of Pages: 954
Audience: Professional and Scholarly
Publisher: Springer Nature B.V.
Country of Publication: DE
Dimensions (cm): 23.5 x 16.51 x 3.81
Weight (kg): 1.5
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