Fault diagnosis: models, artificial intelligence, applications
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| Beschreibung: | Literaturangaben |
| Beschreibung: | XXIX, 920 S. graph. Darst. : 24 cm |
| ISBN: | 3540407677 |
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Datensatz im Suchindex
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| adam_text | JOZEF KORBICZ * JAN M. KOSCIELNY ZDZISTAW KOWALCZUK * WOJCIECH CHOLEWA
(EDS.) FAULT DIAGNOSIS MODELS, ARTIFICIAL INTELLIGENCE, APPLICATIONS
WITH 312 FIGURES SPRINGER CONTENTS PART I * METHODOLOGY 1 1.
INTRODUCTION * W. CHOLEWA AND J.M. KOSCIELNY 3 1.1. DIAGNOSTICS OF
PROCESSES AND ITS FUNDAMENTAL TASKS 3 1.2. MAIN CONCEPTS 6 1.3. AIMS OF
PROCESS DIAGNOSTICS 11 1.4. GENERAL DESCRIPTION OF THE DIAGNOSED OBJECT
13 1.5. BASIC CONCEPTS OF PROCESS DIAGNOSTICS 19 1.6. SUMMARY 25
REFERENCES 26 2. MODELS IN THE DIAGNOSTICS OF PROCESSES * J.M. KOSCIELNY
. 29 2.1. INTRODUCTION 29 2.2. RELATIONS IN DIAGNOSTICS 30 2.3. MODELS
APPLIED TO FAULT DETECTION 31 2.3.1. PHYSICAL EQUATIONS 32 2.3.2. STATE
EQUATIONS OF LINEAR SYSTEMS 33 2.3.3. STATE OBSERVERS 34 2.3.4. TRANSFER
FUNCTIONS OF LINEAR SYSTEMS 35 2.3.5. NEURAL MODELS 37 2.3.6. FUZZY
MODELS 40 2.4. MODELS APPLIED TO FAULT ISOLATION AND SYSTEM STATE
RECOGNITION 44 2.4.1. MODELS MAPPING THE SPACE OF BINARY DIAGNOSTIC
SIGNALS INTO THE SPACE OF FAULTS OR SYSTEM STATES . . 46 2.4.1.1. BINARY
DIAGNOSTIC MATRIX 46 * 2A.I.2. DIAGNOSTIC TREES AND GRAPHS 48 2.4.1.3.
RULES AND LOGIC FUNCTIONS 49 2.4.2. MODELS MAPPING THE SPACE OF
MULTI-VALUE DIAGNOSTIC SIGNALS INTO THE SPACE OF FAULTS OR SYSTEM STATES
. . 50 2.4.2.1. INFORMATION SYSTEM 50 2.4.2.2. OTHER MODELS 53 2.4.3.
MODELS MAPPING THE SPACE OF CONTINUOUS DIAGNOSTIC SIGNALS INTO THE SPACE
OF FAULTS OR SYSTEM STATES . . 54 2.4.3.1. PATTERN PICTURES 54 XTV
CONTENTS 2.4.3.2. NEURAL NETWORKS 55 2.4.3.3. FUZZY NEURAL NETWORKS 55
2.5. SUMMARY 55 REFERENCES 56 3. PROCESS DIAGNOSTICS METHODOLOGY * J.M.
KOSCIELNY . . . . 59 3.1. INTRODUCTION 59 3.2. FAULT DETECTION 59
3.2.1. FAULT DETECTION USING SYSTEM MODELS 60 3.2.1.1. GENERATION OF
RESIDUALS ON THE GROUNDS OF PHYSICAL EQUATIONS 61 3.2.1.2. GENERATION OF
RESIDUALS ON THE GROUNDS OF SYSTEM TRANSMITTANCE 62 3.2.1.3. GENERATION
OF RESIDUALS USING STATE EQUATIONS 64 3.2.1.4. GENERATION OF RESIDUALS
ON THE GROUNDS OF STATE OBSERVERS 66 3.2.1.5. GENERATION OF RESIDUALS
USING ON-LINE IDENTIFICATION 67 3.2.1.6. RESIDUAL GENERATION WITH NEURAL
AND FUZZY MODELS 68 3.2.1.7. ALGORITHMS FOR MAKING A DECISION ON FAULT
DETECTION USING RESIDUAL VALUE EVALUATION 70 3.2.2. FAULT DETECTION
USING TESTS OF SIMPLE RELATIONSHIPS EXISTING BETWEEN SIGNALS 72 3.2.2.1.
APPLICATION OF HARDWARE REDUNDANCY . . . 72 3.2.2.2. APPLICATION OF
FEEDBACK SIGNALS 72 3.2.2.3. TEST OF STATISTICAL RELATIONSHIPS EXISTING
BETWEEN PROCESS VARIABLES 72 3.2.2.4. TESTING THE RELATIONS EXISTING
BETWEEN PROCESS VARIABLES 73 3.2.3. METHODS OF SIGNAL ANALYSIS AND THE
TESTING OF LIMITS 74 3.2.3.1. ANALYSIS OF STATISTIC SIGNAL PARAMETERS .
. 74 1 3.2.3.2. SPECTRAL ANALYSIS 75 3.2.3.3. METHODS OF LIMIT CHECKING
76 3.3. FAULT ISOLATION 79 3.3.1. DIAGNOSING BASED ON THE BINARY
DIAGNOSTIC MATRIX . 80 3.3.1.1. RULES OF PARALLEL DIAGNOSTIC INFERENCE
ON THE ASSUMPTION ABOUT SINGLE FAULTS . . 80 3.3.1.2. RULES OF SERIES
DIAGNOSTIC INFERENCE ON THE ASSUMPTION ABOUT SINGLE FAULTS . . . . 81
3.3.1.3. INFERENCE WITH THE INCONSISTENCY OF SYMPTOMS 82 CONTENTS XV
3.3.1.4. SYSTEM STATES WITH MULTIPLE FAULTS . . . . 83 3.3.1.5. PARALLEL
INFERENCE ON THE ASSUMPTION ABOUT MULTIPLE FAULTS 85 3.3.1.6. SERIES
INFERENCE ON THE ASSUMPTION ABOUT MULTIPLE FAULTS ., 87 3.3.2.
DIAGNOSING BASED ON THE INFORMATION SYSTEM . . . . 87 3.3.2.1. PARALLEL
DIAGNOSTIC INFERENCE BASED ON THE INFORMATION SYSTEM 88 3.3.2.2. SERIES
DIAGNOSTIC INFERENCE BASED ON THE INFORMATION SYSTEM 88 3:3.3. METHODS
OF PATTERN RECOGNITION 89 3.3.4. RECOGNITION OF DIRECTIONS IN THE SPACE
OF RESIDUALS . 91 3.3.5. OTHER METHODS 95 3.4. FAULT DISTINGUISHABILITY
95 3.4.1. FAULT DISTINGUISHABILITY BASED ON THE BINARY DIAGNOSTIC MATRIX
96 3.4.2. DISTINGUISHABILITY OF SYSTEM STATES BASED ON THE BINARY TABLE
OF STATES 97 3.4.3. FAULT DISTINGUISHABILITY BASED ON THE INFORMATION
SYSTEM 98 3.4.4. FAULT DISTINGUISHABILITY BASED ON PATTERN RECOGNITION
IN THE SPACE OF DIAGNOSTIC SIGNALS . . . . 100 3.4.5. FAULT
DISTINGUISHABILITY IMPROVEMENT BY TAKING THE DYNAMICS OF SYMPTOMS INTO
ACCOUNT 101 3.5. METHODS OF THE STRUCTURAL DESIGN OF THE SET OF
DETECTION ALGORITHMS 101 3.5.1. GENERATION OF SECONDARY RESIDUALS BASED
ON PHYSICAL EQUATIONS 102 3.5.2. CHOICE OF A STRUCTURAL SET OF RESIDUALS
GENERATED ON THE BASIS OF PARITY EQUATIONS 103 3.5.3. BANKS OF OBSERVERS
105 3.5.4. DESIGN OF A STRUCTURED SET OF DETECTION ALGORITHMS BASED ON
PARTIAL MODELS 106 3.5.5. MINIMISING THE SET OF DETECTION ALGORITHMS 107
3.6. FAULT IDENTIFICATION 108 3.6.1. RESIDUAL EQUATIONS 109 3.6.2.
RESIDUALS WITHOUT THE KNOWLEDGE OF THE EFFECT OF FAULTS ILL 3.7.
MONITORING THE SYSTEM STATE 112 3.8. SUMMARY 113 REFERENCES 114 XVI
CONTENTS 4. METHODS OF SIGNAL ANALYSIS * W. CHOLEWA, J. KORBICZ, W.A.
MOCZULSKI AND A. TIMOFIEJCZUK 119 4.1. INTRODUCTION 119 4.2. SIGNAL
CLASSIFICATION 121 4.3. INITIAL PRE-PROCESSING OF SIGNALS 123 4.3.1.
ANALOGUE-TO-DIGITAL CONVERSION OF SIGNALS 124 4.3.2. FILTERING 124
4.3.3. SMOOTHING 129 4.3.4. AVERAGING 131 4.3.5. PRINCIPAL COMPONENT
ANALYSIS 132 4.4. NON-PARAMETRIC METHODS OF SIGNAL FEATURE ESTIMATION .
. . . 134 4.4.1. SCALAR FEATURE ESTIMATION 134 4.4.2. SPECTRAL ANALYSIS
135 4.4.3. HIGHER ORDER SPECTRAL ANALYSIS 139 4.4.4. ANALYSIS WITH THE
USE OF THE WAVELET TRANSFORM . . . 141 4.4.5. ANALYSIS WITH THE USE OF
THE WIGNER-VILLE TRANSFORM 145 4.5. PARAMETRIC METHODS OF SIGNAL
ESTIMATION 145 4.6. SIGNAL FEATURES ESTIMATED WITH RESPECT TO OBJECT
PROPERTIES . 147 4.7. SUMMARY 151 REFERENCES 151 5. CONTROL THEORY
METHODS IN DESIGNING DIAGNOSTIC SYSTEMS * Z. KOWALCZUK AND P. SUCHOMSKI
155 5.1. INTRODUCTION 155 5.2. TRANSFER FUNCTION APPROACH 157 5.2.1.
RESIDUE GENERATION 157 5.2.2. PROPERTIES OF THE SYSTEM MATRIX 163 5.2.3.
NON-HOMOGENEOUS RESIDUAL REACTION MODELS 167 5.2.4. HOMOGENEOUS RESIDUAL
REACTION MODELS 171 5.3. PARITY SPACE APPROACH 173 5.4. DETERMINISTIC
ASSIGNMENT OF STATE ESTIMATION 177 5.4.1. FULL-ORDER OBSERVER 177
5.4.2. MINIMAL-ORDER OBSERVER 180 5.4.3. OBSERVER MATRIX DETERMINATION
BY POLE PLACEMENT . 186 5.4.4. DETECTION OBSERVERS OF THE LUENBERGER
TYPE 191 5.5. LINEAR KALMAN FILTERS 198 5.5.1. MODELS OF ESTIMATED
PROCESSES 199 5.5.2. LINEAR KALMAN FILTERING FOUNDED ON INNOVATIONS . .
200 5.6. SUMMARY 207 APPENDIX 208 REFERENCES 213 CONTENTS XVII 6.
OPTIMAL DETECTION OBSERVERS BASED ON EIGENSTRUCTURE ASSIGNMENT * Z.
KOWALCZUK AND P. SUCHOMSKI 219 6.1. INTRODUCTION 219 6.2. SYSTEM
MODELLING 221 6.3. PRELIMINARY SYNTHESIS OF RESIDUALS 222 6.4.
CONDITIONS FOR DISTURBANCE DECOUPLING 223 6.4.1. NECESSARY CONDITION FOR
DECOUPLING 224 6.4.2. SUFFICIENT CONDITIONS FOR DECOUPLING 226 6.5.
PARAMETERISATION OF ATTAINABLE EIGENSUBSPACES 227 6.5.1. SEPARATE
SPECTRA OF THE OBSERVER AND THE OBJECT . . 229 6.5.2. MUTUALITY IN THE
SPECTRA OF THE OBSERVER AND THE OBJECT 229 6.5.3. PARTIAL OBSERVER GAIN
231 6.6. SYNTHESIS OF A NUMERICALLY ROBUST STATE OBSERVER 232 6.6.1.
SEPARATE SPECTRA OF THE OBSERVER AND THE OBJECT . . 234 6.6.2. MUTUALITY
IN THE SPECTRA OF THE OBSERVER AND THE OBJECT 235 6.7. SYNTHESIS OF A
NUMERICALLY ROBUST DECOUPLED STATE OBSERVER . 236 6.7.1. NUMERICALLY
ROBUST ATTAINABLE DECOUPLING 237 6.7.2. COMPLETE OBSERVER GAIN 238 6.8.
COMPLETELY DECOUPLED OBSERVERS 238 6.8.1. DEAD-BEAT DESIGN OF RESIDUE
GENERATORS 239 6.8.2. RESIDUE GENERATION USING PARITY EQUATIONS 242 6.9.
NUMERICAL EXAMPLE 242 6.9.1. DECOUPLED DEAD-BEAT RESIDUE GENERATOR 243
6.9.2. PROPERTIES OF DEAD-BEAD OBSERVERS 244 6.9.3. PROPERTIES OF
NON-DEAD-BEAD OBSERVERS 249 6.9.4. ROBUSTNESS OF DEAD-BEAT OBSERVERS 251
6.10. SUMMARY 251 APPENDICES 253 A. OBSERVABILITY OF DYNAMIC SYSTEMS 253
1 B. USEFUL GEOMETRIC RELATIONSHIPS 254 REFERENCES 257 7. ROBUST H
OO-OPTIMAL SYNTHESIS OF FDI SYSTEMS * P. SUCHOMSKI AND Z. KOWALCZUK 261
7.1. INTRODUCTION 261 7.2. FDI DESIGN TASK AS OPTIMAL FILTERING IN H^
263 7.2.1. OPTIMAL FILTERING BASED ON THE BASIC MODELLING OF GENERALISED
PROCESSES 264 XVM CONTENTS 7.2.2. SOLUTION USING THE BASIC MODEL 267
7.2.3. OPTIMAL FILTERING BASED ON THE DUAL MODELLING OF GENERALISED
PLANTS 272 7.2.4. SOLUTION USING THE DUAL MODEL 274 7.2.5. FDI FILTERING
WITH THE INSTRUMENTAL REFERENCE SIGNAL 280 7.3. SYNTHESIS OF PRIMARY AND
SECONDARY RESIDUAL VECTORS . . . . 283 7.4. NUMERICAL EXAMPLE 286 7.5.
SUMMARY 290 APPENDICES 291 A. DISCRETE-TIME MODELS 291 B. NORMS AND
SPACES 292 C. FACTORISATION 293 D. DISCRETE RICCATI EQUATION 294
REFERENCES 295 PART II * ARTIFICIAL INTELLIGENCE 299 8. EVOLUTIONARY
METHODS IN DESIGNING DIAGNOSTIC SYSTEMS -* A. OBUCHOWICZ AND J. KORBICZ
301 8.1. INTRODUCTION 301 8.2. EVOLUTIONARY ALGORITHMS 302 8.2.1. BASIC
CONCEPTS OF EVOLUTIONARY SEARCH 303 8.2.2. SOME EVOLUTIONARY ALGORITHMS
305 8.3. OPTIMIZATION TASKS IN DESIGNING FDI SYSTEMS 308 8.4. SYMPTOM
EXTRACTION 309 8.4.1. CHOICE OF THE GAIN MATRIX FOR THE ROBUST
NON-LINEAR OBSERVER VIA GENETIC PROGRAMMING 309 8.4.2. DESIGNING THE
ROBUST RESIDUAL GENERATOR USING MULTI-OBJECTIVE OPTIMIZATION AND
EVOLUTIONARY ALGORITHMS 312 8.4.3. EVOLUTIONARY ALGORITHMS IN THE DESIGN
OF NEURAL MODELS 314 8.5. SYMPTOM EVALUATION 323 8.5.1. GENETIC
CLUSTERING 324 8.5.2. EVOLUTIONARY ALGORITHMS IN DESIGNING THE RULE BASE
325 8.5.3. GENETIC ADAPTATION OF FUZZY SYSTEMS 327 8.6. SUMMARY 329
REFERENCES 329 CONTENTS XIX 9. ARTIFICIAL NEURAL NETWORKS IN FAULT
DIAGNOSIS * K. PATAN AND J. KORBICZ 333 9.1. INTRODUCTION 333 9.2.
STRUCTURE OF A NEURAL FAULT DIAGNOSIS SYSTEM 334 9.3. NEURAL MODELS IN
MODELLING 337 9.3.1. MULTI-LAYER PERCEPTRON . . 337 9.3.2. RECURRENT
NETWORKS 339 9.3.3. NEURAL NETWORKS OF THE GMDH TYPE 347 9.4. FAULT
CLASSIFICATION USING NEURAL NETWORKS 352 9.4.1. MULTI-LAYER PERCEPTRON
352 9.4.2. KOHONEN NETWORK 352 9.4.3. RADIAL BASIC NETWORKS 354 9.4.4.
MULTIPLE NETWORK STRUCTURE 356 9.5. SELECTED APPLICATIONS 357 9.5.1.
TWO-TANK LABORATORY SYSTEM 357 9.5.2. INSTRUMENTATION FAULT DETECTION
365 9.5.3. ACTUATOR FAULT DETECTION AND ISOLATION 369 9.6. SUMMARY 375
REFERENCES 376 10. PARAMETRIC AND NEURAL NETWORK WIENER AND HAMMERSTEIN
MODELS IN FAULT DETECTION AND ISOLATION * A. JANCZAK . . . 381 10.1.
INTRODUCTION 381 10.2. WIENER AND HAMMERSTEIN MODELS 382 10.3.
IDENTIFICATION OF WIENER AND HAMMERSTEIN SYSTEMS 384 10.4. PARAMETRIC
AND NEURAL NETWORK WIENER AND HAMMERSTEIN MODELS 386 10.4.1. PARAMETRIC
MODELS 387 10.4.2. NEURAL NETWORK MODELS 388 10.5. FAULT DETECTION.
ESTIMATING PARAMETER CHANGES 389 10.5.1. DEFINITIONS OF THE
IDENTIFICATION ERROR 390 I 10.5.2. HAMMERSTEIN SYSTEM. PARAMETER
ESTIMATION OF THE RESIDUAL EQUATION 393 10.5.3. WIENER SYSTEM. PARAMETER
ESTIMATION OF THE RESIDUAL EQUATION . . 396 10.6. FIVE-STAGE SUGAR
EVAPORATOR. IDENTIFICATION OF THE NOMINAL MODEL OF STEAM PRESSURE
DYNAMICS 402 10.6.1. THEORETICAL MODEL 402 10.6.2. EXPERIMENTAL MODELS
403 10.6.3. ESTIMATION RESULTS 404 XX CONTENTS 10.7. SUMMARY 407
REFERENCES 407 11. APPLICATION OF FUZZY LOGIC TO DIAGNOSTICS * J.M.
KOSCIELNY AND M. SYFERT 411 11.1. INTRODUCTION 411 11.2. FAULT DETECTION
412 11.2.1. WANG AND MENDEL S FUZZY MODELS 413 11.2.1.1. CONSTRUCTION OF
FUZZY MODELS USING WANG AND MENDEL S METHOD 413 11.2.1.2. MODIFICATION
OF WANG AND MENDEL S METHOD 415 11.2.1.3. CALCULATION OF A RESIDUAL ON
THE BASIS OF THE FUZZY MODEL 416 11.2.2. FUZZY NEURAL NETWORKS 417
11.2.2.1. FUZZY NEURAL NETWORKS WITH OUTPUTS IN THE FORM OF SINGLETONS
418 11.2.2.2. TSK-TYPE FUZZY NEURAL NETWORKS 420 11.2.2.3. FUZZY NEURAL
NETWORKS WITH OUTPUTS IN THE FORM OF FUZZY SETS 422 11.2.3. EXAMPLE OF
FAULT DETECTION 423 11.3. FAULT ISOLATION WITH THE USE OF FUZZY LOGIC
428 11.3.1. FUZZY EVALUATION OF RESIDUAL VALUES 429 11.3.2. RULES OF
INFERENCE 431 11.3.3. FUZZY DIAGNOSTIC INFERENCE 433 11.3.4. EXAMPLE OF
FAULT ISOLATION 437 11.3.5. UNCERTAINTY OF THE DIAGNOSTIC SIGNALS-FAULTS
RELATION 441 11.4. FAULT ISOLATION WITH THE USE OF THE FUZZY NEURAL
NETWORK . . 442 11.4.1. REALISATION OF FUZZY RESIDUAL EVALUATION BY THE
FUZZY NEURAL NETWORK 444 11.4.2. FAULT ISOLATION IN THE FUZZY NEURAL
NETWORK 445 11.4.3. EXAMPLE OF FUZZY NEURAL NETWORK APPLICATION TO FAULT
ISOLATION 449 11.5. SUMMARY 450 REFERENCES 454 12. OBSERVERS AND GENETIC
PROGRAMMING IN THE IDENTIFICATION AND FAULT DIAGNOSIS OF NON-LINEAR
DYNAMIC SYSTEMS * M. WITCZAK AND J. KORBICZ 457 12.1. INTRODUCTION 457
12.2. IDENTIFICATION OF NON-LINEAR DYNAMIC SYSTEMS 460 12.2.1. DATA
ACQUISITION AND PREPARATION 460 CONTENTS XXI 12.2.2. MODEL SELECTION
CRITERIA 461 12.2.3. INPUT-OUTPUT REPRESENTATION OF THE SYSTEM 464
12.2.4. TREE STRUCTURE DETERMINATION USING GP 466 12.2.5. STATE-SPACE
REPRESENTATION OF THE SYSTEM 470 12.3. UNKNOWN INPUT OBSERVERS 472
12.3.1. PRELIMINARIES 473 12.3.2. EXTENDED UNKNOWN INPUT OBSERVER 475
12.3.3. CONVERGENCE OF THE EUIO 475 12.3.4. INCREASING THE CONVERGENCE
RATE VIA GENETIC PROGRAMMING 479 12.3.5. EUIO-BASED SENSOR FDI 481
12.3.6. EUIO-BASED ACTUATOR FDI 481 12.4. EXPERIMENTAL RESULTS 483
12.4.1. SYSTEM IDENTIFICATION WITH GP 483 12.4.1.1. VAPOUR MODEL 484
12.4.1.2. APPARATUS MODEL 488 12.4.1.3. VALVE ACTUATOR MODEL 490
12.4.1.4. STATE ESTIMATION AND FAULT DETECTION OF AN INDUCTION MOTOR . .
*. 493 12.4.1.5. SENSOR FDI WITH EUIO 497 12.4.1.6. UNKNOWN INPUT
ESTIMATION AND DESIGN OF INSTRUMENTAL MATRICES 499 12.4.1.7. THRESHOLD
DETERMINATION AND FAULT DETECTION 500 12.5. SUMMARY 503 REFERENCES 506
13. GENETIC ALGORITHMS IN THE MULTI-OBJECTIVE OPTIMISATION OF FAULT
DETECTION OBSERVERS * Z. KOWALCZUK AND T. BIALASZEWSKI 511 13.1.
INTRODUCTION 511 13.2. MULTI-OBJECTIVE OPTIMISATION 512 13.2.1.
FORMULATION OF MULTI-OBJECTIVE OPTIMISATION PROBLEMS 513 I 13.2.2.
MULTI-OBJECTIVE OPTIMISATION METHODS 513 13.3. GENETIC ALGORITHMS 519
13.3.1. GENOTYPE, PHENOTYPE AND FITNESS OF INDIVIDUALS . . 521 13.3.2.
BASIC MECHANISMS OF GAS 521 13.3.3. GENETIC NICHING : 528 13.3.4. FULL
CYCLE OF THE GENETIC ALGORITHM WITH NICHING . . 533 13.4. GENETIC
ALGORITHMS IN THE MULTI-OBJECTIVE OPTIMISATION OF DETECTION OBSERVERS
536 13.4.1. STATE OBSERVERS IN FDI SYSTEMS 536 XXII CONTENTS 13.4.2.
DESIGN OF RESIDUE GENERATORS 537 13.4.3. DETECTION OBSERVERS FOR THE
LATERAL CONTROL SYSTEM OF A REMOTELY PILOTED AIRCRAFT 542 13.4.4. FAULT
DETECTOR FOR A SHIP PROPULSION SYSTEM 547 13.5. SUMMARY 553 REFERENCES
554 14. PATTERN RECOGNITION APPROACH TO FAULT DIAGNOSTICS * A. MARCINIAK
AND J. KORBICZ 557 14.1. INTRODUCTION 557 14.2. CLASSIFICATION IN
DIAGNOSTICS 558 14.3. SYMPTOM EXTRACTION WITH TIME-SERIES ANALYSIS 559
14.4. PATTERN RECOGNITION METHODS 561 14.4.1. MINIMAL-DISTANCE METHODS
562 14.4.1.1. MEASURES OF DISTANCE IN THE MULTI- DIMENSIONAL SYMPTOM
SPACE 562 14.4.1.2. MINIMAL-DISTANCE METHODS 565 14.4.2. STATISTICAL
METHODS 568 14.4.3. APPROXIMATION APPROACH 571 14.5. DEVELOPING RELIABLE
CLASSIFIERS THROUGH REDUNDANCY 574 14.5.1. CONCEPT OF SOFTWARE
REDUNDANCY 574 14.5.2. DIVERSIFICATION OF CLASSIFIERS 576 14.5.3. LEVELS
IN THE OUTPUT INFORMATION OF CLASSIFIERS . . . 577 14.6. EVALUATION OF
CLASSIFIERS ACCURACY 578 14.6.1. INTRODUCTION 578 14.6.2. RESUBSTITUTION
METHOD 579 14.6.3. HOLDOUT METHOD 579 14.6.4. LEAVE-ONE-OUT METHOD 580
14.6.5. LEAVE-FC-OUT METHOD 580 14.6.6. BOOTSTRAPPING METHODS 580
14.6.7. COMPARISON OF CLASSIFIERS PERFORMANCE AND CONFIDENCE INTERVALS
581 14.7. SOME CLASSIFICATION PROBLEMS 582 14.7.1. BREAST CANCER
DIAGNOSIS 583 14.7.2. DIAGNOSIS OF ERYTHEMATO-SQUAMOUS DISEASES . . . .
584 14.7.3. FAULT DIAGNOSIS IN A TWO-TANK SYSTEM 584 14.8. SUMMARY 587
REFERENCES 589 CONTENTS XXM 15. EXPERT SYSTEMS IN TECHNICAL DIAGNOSTICS
* W. CHOLEWA . . 591 15.1. INTRODUCTION 591 15.2. KNOWLEDGE
REPRESENTATION 595 15.3. STATEMENTS AND RULES 597 15.3.1. STATEMENTS 597
15.3.2. RULES 598 15.3.3. INFERENCE SCHEMES 599 15.3.4. NON-MONOTONIC
INFERENCE 600 15.3.5. OR FUNCTOR 601 15.3.6. CONTEXT . . . 601 15.3.7.
PRODUCTION RULES 603 15.3.8. EXPLANATIONS 603 15.3.9. SETS OF RULES 604
15.4. REPRESENTATION OF APPROXIMATE KNOWLEDGE 605 15.5. STATIC AND
DYNAMIC EXPERT SYSTEMS 606 15.5.1. STATIC EXPERT SYSTEMS 606 15.5.2.
DYNAMIC EXPERT SYSTEMS 607 15.5.3. BLACKBOARD 609 15.6. INFERENCE IN
NETWORKS OF APPROXIMATE STATEMENTS 611 15.6.1. PRIMARY AND SECONDARY
STATEMENTS 611 15.6.2. APPROXIMATE VALUE OF THE STATEMENT 612 15.6.3.
NECESSARY AND SUFFICIENT CONDITIONS 612 15.6.4. APPROXIMATE CONDITIONS
613 15.6.5. APPROXIMATE CONJUNCTION AND ALTERNATIVE OF STATEMENTS 614
15.6.6. EQUILIBRIUM STATE IN NETWORKS OF APPROXIMATE STATEMENTS 614
15.7. INFERENCE IN BELIEF NETWORKS 615 15.7.1. PROBABILITY CALCULUS 616
15.7.2. BAYES MODEL 617 15.7.3. BELIEF NETWORKS 618 15.7.4.
POSSIBILITIES OF APPLICATION 620 15.8. INTEGRATION OF THE COMPUTER
ENVIRONMENT . . . 621 15.8.1. DATABASES 622 15.8.2. MULTI-LAYER SOFTWARE
625 15.8.3. SPECIAL LANGUAGES 626 15.9. SYSTEM TESTING 628 15.10.SUMMARY
629 REFERENCES 630 XXTV CONTENTS 16. SELECTED METHODS OF KNOWLEDGE
ENGINEERING IN SYSTEMS DIAGNOSIS * A. LIGQZA 633 16.1. INTRODUCTION 633
16.2. REVIEW AND TAXONOMY OF KNOWLEDGE ENGINEERING METHODS FOR DIAGNOSIS
635 16.3. MODELLING CAUSAL RELATIONSHIPS IN DIAGNOSIS 638 16.4.
CONSISTENCY-BASED DIAGNOSTIC REASONING 640 16.4.1. INTRODUCTION TO LOGIC
AND CONSISTENCY-BASED REASONING 641 16.4.2. SYSTEM MODEL, SYSTEM
COMPONENTS AND OBSERVATIONS 643 16.4.3. CONFLICT SETS 646 16.4.4. THEORY
OF CONSISTENCY-BASED DIAGNOSTIC REASONING (REITER S THEORY) 648 16.4.5.
GENERATION OF CONFLICT SETS AND DIAGNOSES - A CONSTRUCTIVE APPROACH 649
16.4.6. SEARCH FOR CONFLICTS; POTENTIAL CONFLICT STRUCTURES . . 650
16.4.7. EXAMPLES OF APPLICATION 654 16.4.8. CA-EN AND TIGER SYSTEMS 655
16.5. LOGICAL CAUSAL GRAPHS 655 16.5.1. AND I OR/ NOT GRAPHS 656 16.5.2.
INFORMATION PROPAGATION IN LOGICAL CAUSAL GRAPHS; THE STATE OF THE GRAPH
658 16.5.3. DIAGNOSTIC REASONING: ABDUCTION 659 16.5.4. SOLUTION
ANALYSIS AND DIAGNOSES VERIFICATION . . . . 661 16.5.5. EXTENSIONS OF
THE BASIC FORMALISM 663 16.5.6. EXAMPLE OF APPLICATION 664 16.6.
COMPARISON OF SELECTED APPROACHES . 668 16.7. SUMMARY 669 REFERENCES 670
17. METHODS OF ACQUSITION OF DIAGNOSTIC KNOWLEDGE * W. MOCZULSKI 675
17.1. INTRODUCTION 675 17.2. KNOWLEDGE IN TECHNICAL DIAGNOSTICS 677
17.2.1. DECLARATIVE KNOWLEDGE 677 17.2.2. PROCEDURAL KNOWLEDGE 677 17.3.
PROBLEM FORMULATION 678 17.4. SELECTED METHODS OF KNOWLEDGE ACQUISITION
679 17.4.1. METHODS OF ACQUIRING DECLARATIVE KNOWLEDGE . . . . 679 4
CONTENTS XXV 17.4.1.1. METHODS OF ACQUIRING OF DECLARATIVE KNOWLEDGE
FROM EXPERTS 680 17.4.1.2. AUTOMATED METHODS OF ACQUISITION OF
DECLARATIVE KNOWLEDGE 680 17.4.1.3. METHODS OF DISCOVERING DECLARATIVE
KNOWLEDGE 694 17.4.1.4. METHODS OF ASSESSING DECLARATIVE KNOWLEDGE 700
17.4.2. METHODOLOGY OF KNOWLEDGE ACQUISITION FROM DATABASES USING
MACHINE LEARNING . 700 17.4.3. METHOD OF ACQUISITION OF PROCEDURAL
KNOWLEDGE . . 702 17.4.4. SCENARIO OF THE PROCESS OF ACQUIRING
DECLARATIVE KNOWLEDGE 703 17.5. AIDING MEANS OF THE KNOWLEDGE
ACQUISITION PROCESS 703 17.5.1. DATA AND KNOWLEDGE BASE EMPREL 704
17.5.2. MEANS OF ACQUIRING DIAGNOSTIC RELATIONSHIPS FROM EXPERTS 706
17.5.3. SYSTEM OF THE ACQUISITION OF DECLARATIVE KNOWLEDGE 708 17.5.4.
MEANS OF ACQUIRING PROCEDURAL KNOWLEDGE 709 17.6. EXAMPLES OF
APPLICATIONS 709 17.6.1. ACQUISITION OF DECLARATIVE KNOWLEDGE WITHIN THE
FRAMEWORK OF THE ACTIVE EXPERIMENT 709 17.6.2. ACQUISITION OF
DECLARATIVE KNOWLEDGE WITHIN THE FRAMEWORK OF THE NUMERICAL EXPERIMENT .
. . . 711 17.6.2.1. ACQUISITION OF KNOWLEDGE FOR AIDING THE DETECTION OF
IMBALANCE 712 17.6.2.2. DISCOVERY OF KNOWLEDGE FOR AIDING THE DIAGNOSIS
OF IMBALANCE 713 * 17.7. SUMMARY 715 REFERENCES 717 PART III *
APPLICATIONS 719 F 18. STATE MONITORING ALGORITHMS FOR COMPLEX DYNAMIC
SYSTEMS * J.M. KOSCIELNY 721 18.1. INTRODUCTION 721 18.2. PRACTICAL
PROBLEMS 722 18.2.1. DYNAMICS OF THE OCCURRENCE OF SYMPTOMS 722 18.2.2.
VARIATION OF THE DIAGNOSED SYSTEM S STRUCTURE AND THE SET OF MEASURING
DEVICES 725 18.2.3. DIAGNOSING TIME LIMIT 725 XXVI CONTENTS 18.3.
GENERAL STRATEGY OF THE CURRENT DIAGNOSTICS OF INDUSTRIAL PROCESSES 726
18.4. FAULT DETECTION 727 18.5. FAULT ISOLATION ON THE ASSUMPTION ABOUT
SINGLE FAULTS . . . . 728 18.5.1. DTS METHOD 729 18.5.2. F-DTS METHOD
735 18.5.3. T-DTS METHOD 738 18.5.3.1. EXPANSION OF THE FIS DEFINITION
738 18.5.3.2. FAULT DISTINGUISHABILITY IN THE EXPANDED FIS 738 18.5.3.3.
FAULT ISOLATION USING THE T-DTS METHOD . 740 18.6. FAULT ISOLATION ON
THE ASSUMPTION ABOUT MULTIPLE FAULTS . . 742 18.6.1. DTS METHOD 742
18.6.2. F-DTS METHOD 744 18.7. MODIFICATION OF THE SET OF AVAILABLE
DIAGNOSTIC SIGNALS . . . . 745 18.8. FAULT IDENTIFICATION 746 18.8.1.
DTS AND T-DTS METHODS 746 18.8.2. F-DTS METHOD 747 18.9. DETECTING A
COMEBACK TO THE NORMAL STATE 748 18.10. DEFINING THE WEIGHT OF GENERATED
DIAGNOSES 748 18.11. EXAMPLES OF STATE MONITORING 749 18.11.1. DTS
METHOD 749 18.11.2. F-DTS METHOD 754 18.11.3. T-DTS METHOD 757 18.12.
SUMMARY 760 REFERENCES 761 19. DIAGNOSTICS OF INDUSTRIAL PROCESSES IN
DECENTRALISED STRUCTURES * J.M. KOSCIELNY 763 19.1. INTRODUCTION 763
19.2. DECOMPOSITION OF THE DIAGNOSTIC SYSTEM 764 19.3. DIAGNOSTICS IN
ONE-LEVEL STRUCTURES 766 19.4. DIAGNOSTICS IN HIERARCHICAL STRUCTURES
769 19.4.1. HIERARCHICAL DESCRIPTION OF COMPLEX DIAGNOSTIC SYSTEMS 769
19.4.2. DIAGNOSING IN HIERARCHICAL STRUCTURES 774 19.5. SUMMARY 778
REFERENCES 778 CONTENTS XXVII 20. DETECTION AND ISOLATION OF MANOEUVRES
IN ADAPTIVE TRACKING FILTERING BASED ON MULTIPLE MODEL SWITCHING * Z.
KOWALCZUK AND M. SANKOWSKI 781 20.1. INTRODUCTION 781 20.2. MODEL OF THE
MEASUREMENT PROCESS 784 20.2.1. SAMPLING PERIOD 785 20.2.2. RADAR
MEASUREMENTS 785 20.2.3. MEASUREMENT EQUATION 786 20.3. MODELLING THE
TARGET MOVEMENT TRAJECTORY 787 20.3.1. KINEMATIC MODEL OF THE PLANAR
CURVILINEAR MOTION . 787 20.3.2. BASIC ASSUMPTIONS 789 20.3.3. MODEL OF
THE UNIFORM MOTION 790 20.3.4. MODEL OF THE UNIFORM SPEED CHANGE 792
20.3.5. MODEL OF THE STANDARD TURN 793 20.4. STATE ESTIMATION DURING
UNIFORM MOTIONS 795 20.4.1. BASE KALMAN FILTER 795 20.4.2. INITIATION OF
THE TRACKING FILTER 796 20.5. IDENTIFICATION OF THE CONTROL SIGNAL 797
20.5.1. INPUT ESTIMATION METHOD 797 20.5.2. BASIC PROPERTIES OF THE
INPUT ESTIMATION METHOD . . 800 20.6. DETECTION AND ISOLATION OF
MANOEUVRES 800 20.6.1. DETECTION OF MANOEUVRES 801 20.6.2. ISOLATION OF
MANOEUVRES 803 20.6.3. IDENTIFICATION OF MANOEUVRE MODEL PARAMETERS . .
. 806 20.7. EVALUATION OF THE PROPOSED METHODS 808 20.7.1. PROPERTIES OF
MODELS OF THE STANDARD TURN 808 20.7.2. SIMULATION TESTS 809 20.7.3.
PARAMETRIC IDENTIFICATION OF MANOEUVRE MODELS . . . 810 20.7.4.
MANOEUVRE RECOGNITION 812 20.7.5. MANOEUVRE DETECTION 813 20.7.6.
OPTIMAL WINDOW LENGTH OF THE IE/NIE ESTIMATOR . 815 20.8. SUMMARY 816
REFERENCES 817 21. DETECTING AND LOCATING LEAKS IN TRANSMISSION
PIPELINES * Z. KOWALCZUK AND K. GUNAWICKRAMA 821 21.1. INTRODUCTION 821
21.2. TRANSMISSION PIPELINE PROCESS 824 21.2.1. PIPE INSTRUMENTATION 824
21.2.2. TECHNICAL PARAMETERS OF THE PIPE 825 XXVM CONTENTS 21.2.3.
TECHNOLOGICAL EFFECTS OF LEAKS ON PIPELINE MEASUREMENTS 826 21.2.4.
PHYSICAL MODEL OF FLUID FLOW IN THE PIPE 828 21.3. ANALYTICAL LEAK
DETECTION AND ISOLATION METHODS FOR PIPELINES 829 21.3.1. VOLUME
BALANCING APPROACH 830 21.3.2. FAULT-SENSITIVE AND FAULT MODEL
APPROACHES 830 21.4. FAULT-SENSITIVE APPROACH 831 21.4.1. MODEL OF THE
TRANSMISSION PIPELEG 833 21.4.2. RESIDUE GENERATION VIA NONLINEAR STATE
OBSERVATION 834 21.4.3. LEAK PARAMETERS 835 21.4.4. ON-LINE ESTIMATION
OF THE FRICTION COEFFICIENT . . . . 837 21.4.5. EXEMPLARY MONITORING
WITH THE USE OF THE FSA-LDS 839 21.5. FAULT MODEL APPROACH 845 21.5.1.
MATHEMATICAL MODEL OF THE PIPELEG 846 21.5.2. DETERMINING THE LEAK SIZE
AND LOCATION 850 21.5.3. MINIMISATION OF MODELLING ERRORS VIA THE
EXTENDED KALMAN FILTERING 853 21.5.4. EXEMPLARY MONITORING THROUGH THE
FMA-LDS . . . 856 21.6. SUMMARY 861 REFERENCES 862 22. MODELS IN THE
DIAGNOSTICS OF PROCESSES * J.M. KOSCIELNY, M. BARTYS, M. SYFERT AND M.
PAWLAK 865 22.1. INTRODUCTION 865 22.2. FAULT DIAGNOSIS OF THE
STEAM-WATER LINE OF THE POWER BOILER . 866 22.2.1. SYSTEM DESCRIPTION
866 22.2.2. FAULT DETECTION OF THE WATER-STEAM LINE OF THE POWER BOILER
869 22.2.3. FAULT ISOLATION OF THE WATER-STEAM LINE OF THE POWER BOILER
871 22.3. FAULT DIAGNOSIS OF THE EVAPORATION STATION IN A SUGAR FACTORY
875 22.3.1. SYSTEM DESCRIPTION 875 22.3.2. FAULT DETECTION OF THE
EVAPORATOR 878 22.3.3. FAULT ISOLATION IN THE EVAPORATOR 882 22.4. FAULT
DIAGNOSIS OF THE PNEUMATIC ACTUATOR-POSITIONER-CONTROL VALVE ASSEMBLY
883 22.4.1. INTRODUCTION - THE AIMS OF THE DIAGNOSTICS OF FINAL CONTROL
ELEMENTS 883 CONTENTS XXIX 22.4.2. FAULT DETECTION OF THE FINAL CONTROL
ELEMENT 885 22.4.3. FAULT ISOLATION OF THE FINAL CONTROL ELEMENT 888
22.5. FAULT DIAGNOSIS OF THE CONDENSATION POWER TURBINE CONTROLLER
TOLERATING INSTRUMENTATION FAULTS 893 22.5.1. CONDENSATION TURBINE
CONTROLLER 893 22.5.2. DIAGNOSTICS OF INSTRUMENTATION 895 22.6. SUMMARY
900 REFERENCES 900 23. DIAGNOSTIC SYSTEMS * J.M. KOSCIELNY, P.
RZEPIEJEWSKI AND P. WASIEWICZ 903 23.1. INTRODUCTION 903 23.2. ALARM
SYSTEMS IN CONTROL SYSTEMS 904 23.3. DIAGNOSTIC SYSTEMS FOR INDUSTRIAL
PROCESSES 905 23.4. DIAG SYSTEM FOR DIAGNOSING INDUSTRIAL PROCESSES 908
23.5. DIAGNOSTIC SYSTEMS FOR ACTUATORS 912 23.6. V-DIAG DIAGNOSTIC
SYSTEM FOR ACTUATORS 914 23.7. SUMMARY . 918 REFERENCES 918
|
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| callnumber-label | TA169 |
| callnumber-raw | TA169.6 |
| callnumber-search | TA169.6 |
| callnumber-sort | TA 3169.6 |
| callnumber-subject | TA - General and Civil Engineering |
| classification_rvk | ST 301 ZG 9270 |
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| dewey-raw | 620/.0044 |
| dewey-search | 620/.0044 |
| dewey-sort | 3620 244 |
| dewey-tens | 620 - Engineering and allied operations |
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| id | DE-604.BV019687274 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T20:03:50Z |
| institution | BVB |
| isbn | 3540407677 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-013015141 |
| oclc_num | 52858492 |
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| owner | DE-1043 DE-706 DE-83 |
| owner_facet | DE-1043 DE-706 DE-83 |
| physical | XXIX, 920 S. graph. Darst. : 24 cm |
| publishDate | 2004 |
| publishDateSearch | 2004 |
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| publisher | Springer |
| record_format | marc |
| series2 | Engineering online library |
| spelling | Fault diagnosis models, artificial intelligence, applications Józef Korbicz ... (ed.) Berlin ; Heidelberg ; New York ; Hong Kong ; London ; Milan ; Pa Springer 2004 XXIX, 920 S. graph. Darst. : 24 cm txt rdacontent n rdamedia nc rdacarrier Engineering online library Literaturangaben Falhas computacionais larpcal Inteligência artificial larpcal Fault location (Engineering) System failures (Engineering) Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Fehlererkennung (DE-588)4133764-5 gnd rswk-swf Diagnosesystem (DE-588)4149458-1 gnd rswk-swf Prozessmodell (DE-588)4237203-3 gnd rswk-swf Kontrolltheorie (DE-588)4032317-1 gnd rswk-swf Signaltheorie (DE-588)4054945-8 gnd rswk-swf Diagnosesystem (DE-588)4149458-1 s Fehlererkennung (DE-588)4133764-5 s Künstliche Intelligenz (DE-588)4033447-8 s Signaltheorie (DE-588)4054945-8 s Kontrolltheorie (DE-588)4032317-1 s Prozessmodell (DE-588)4237203-3 s DE-604 Korbicz, Józef Sonstige oth HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013015141&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fault diagnosis models, artificial intelligence, applications Falhas computacionais larpcal Inteligência artificial larpcal Fault location (Engineering) System failures (Engineering) Künstliche Intelligenz (DE-588)4033447-8 gnd Fehlererkennung (DE-588)4133764-5 gnd Diagnosesystem (DE-588)4149458-1 gnd Prozessmodell (DE-588)4237203-3 gnd Kontrolltheorie (DE-588)4032317-1 gnd Signaltheorie (DE-588)4054945-8 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4133764-5 (DE-588)4149458-1 (DE-588)4237203-3 (DE-588)4032317-1 (DE-588)4054945-8 |
| title | Fault diagnosis models, artificial intelligence, applications |
| title_auth | Fault diagnosis models, artificial intelligence, applications |
| title_exact_search | Fault diagnosis models, artificial intelligence, applications |
| title_full | Fault diagnosis models, artificial intelligence, applications Józef Korbicz ... (ed.) |
| title_fullStr | Fault diagnosis models, artificial intelligence, applications Józef Korbicz ... (ed.) |
| title_full_unstemmed | Fault diagnosis models, artificial intelligence, applications Józef Korbicz ... (ed.) |
| title_short | Fault diagnosis |
| title_sort | fault diagnosis models artificial intelligence applications |
| title_sub | models, artificial intelligence, applications |
| topic | Falhas computacionais larpcal Inteligência artificial larpcal Fault location (Engineering) System failures (Engineering) Künstliche Intelligenz (DE-588)4033447-8 gnd Fehlererkennung (DE-588)4133764-5 gnd Diagnosesystem (DE-588)4149458-1 gnd Prozessmodell (DE-588)4237203-3 gnd Kontrolltheorie (DE-588)4032317-1 gnd Signaltheorie (DE-588)4054945-8 gnd |
| topic_facet | Falhas computacionais Inteligência artificial Fault location (Engineering) System failures (Engineering) Künstliche Intelligenz Fehlererkennung Diagnosesystem Prozessmodell Kontrolltheorie Signaltheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013015141&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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