Freyermuth / Isermann Fault Detection, Supervision and Safety for Technical Processes 1991

Selected Papers from the IFAC/IMACS Symposium, Baden-Baden, Germany, 10-13 September 1991
1. Auflage 2014
ISBN: 978-1-4832-9903-7
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 638 Seiten, Web PDF

Reihe: IFAC Symposia Series

These Proceedings provide a general overview as well as detailed information on the developing field of reliability and safety of technical processes in automatically controlled processes. The plenary papers present the state-of-the-art and an overview in the areas of aircraft and nuclear power stations, because these safety-critical system domains possess the most highly developed fault management and supervision schemes. Additional plenary papers covered the recent developments in analytical redundancy. In total there are 95 papers presented in these Proceedings.

Autoren/Hrsg.

Freyermuth, B.

Isermann, R.

Inhaltsverzeichnis

1;Front Cover;1
2;Fault Detection, Supervision and Safety for Technical Processes;4
3;Copyright Page;5
4;Table of Contents;10
5;Preface;8
6;PART I: PLENARY PAPERS;18
6.1;CHAPTER 1. FAULT MANAGEMENT IN A MODERN AIRLINER;18
6.1.1;INTRODUCTION;18
6.1.2;SAFETY;18
6.1.3;QUALITY;19
6.1.4;COST-EFFECTIVENESS;19
6.1.5;OPERATING ENVIRONMENT;19
6.1.6;CERTIFICATION REQUIREMENTS;20
6.1.7;MAINTENANCE SYSTEM;21
6.1.8;DESIGN CRITERIA;22
6.1.9;SYSTEM REDUNDANCY;22
6.1.10;CENTRALIZED WARNING SYSTEM;23
6.1.11;CENTRAL MAINTENANCE COMPUTER;24
6.1.12;CONCLUSION;25
6.2;CHAPTER 2. ANALYTICAL REDUNDANCY METHODS IN FAULT DETECTION AND ISOLATION;26
6.2.1;1. INTRODUCTION;26
6.2.2;2. SYSTEM DESCRIPTION;27
6.2.3;3. BASIC RESIDUAL GENERATORS;28
6.2.4;4. DESIGN FOR STRUCTURED RESIDUALS;30
6.2.5;5. DESIGN FOR FIXED DIRECTION RESIDUALS;33
6.2.6;6. MODELLING ERROR ROBUSTNESS;35
6.2.7;7. CONCLUSION;37
6.2.8;ACKNOWLEDGEMENTS;37
6.2.9;REFERENCES;37
6.3;CHAPTER 3. SUPERVISION AND FAULT DIAGNOSIS FOR THE SAFE OPERATION OF NUCLEAR POWER PLANTS;40
6.3.1;INTRODUCTION;40
6.3.2;PLANT AUTOMATION;40
6.3.3;PREREQUISITS OF GOOD AUTOMATION;41
6.3.4;THE PROCESS INFORMATION SYSTEM (PRINS) FOR THE ENTIRE NUCLEAR POWER UNIT,COMPUTER AIDED (PRISCA);41
6.3.5;SURVEILLANCE SYSTEMS FORCONDITION MONITORING AND FAULTDIAGNOSIS OF COMPONENTS;43
6.3.6;CONCLUSION;45
7;Part 2: SURVEY PAPERS;10
7.1;CHAPTER 4. ASPECTS OF ACHIEVING TOTALSYSTEMS AVAILABILITY;52
7.1.1;I INTRODUCTION;52
7.1.2;II BASIC CONCEPTS;53
7.1.3;Ill AVAILABILITY AND SAFETY;54
7.1.4;IV AVAILABILITY AND MODIFIABILITY;55
7.1.5;V SOFTWARE ASPECTS;55
7.1.6;VI SUMMARY;57
7.1.7;REFERENCES;58
7.2;CHAPTER 5. FAULT DIAGNOSIS OF MACHINES VIA PARAMETER ESTIMATION ANDKNOWLEDGE PROCESSING;60
7.2.1;1. SUPERVISORY FUNCTIONS;60
7.2.2;2. MODEL BASED FAULT DETECTION - ANALYTICAL REDUNDANCY;61
7.2.3;3. STATIC AND DYNAMIC MODELS OF MACHINES FOR DIFFERENT OPERATION MODES;63
7.2.4;4. SYMPTOM GENERATION BY PARAMETER ESTIMATION;66
7.2.5;5. FAULT DIAGNOSIS BASED ON SYMPTOM PROCESSING;67
7.2.6;6. APPLICATION: FAULT DETECTION AND DIAGNOSIS OF AN ELECTRICAL FEED DRIVE OF A MACHINE TOOL;69
7.2.7;LITERATURE;71
7.3;CHAPTER 6. A STANDARD INTERFACE FOR SELF-VALIDATING SENSORS;74
7.3.1;SENSOR VALIDATION;74
7.3.2;VALIDITY INDICES;76
7.3.3;APPLICATION;79
7.3.4;CONCLUSION;81
7.3.5;REFERENCES;81
7.4;CHAPTER 7. A REVIEW OF PARITY SPACE APPROACHES TO FAULT DIAGNOSIS;82
7.4.1;1. INTRODUCTION;82
7.4.2;2. BASIC CONCEPTS OF THE PARITY SPACE;83
7.4.3;3. CLOSED-LOOP RESIDUAL GENERATION STRATEGIES;85
7.4.4;4. OPEN-LOOP STRATEGIES FOR RESIDUALGENERATION;86
7.4.5;5. RELATIONSHIP BETWEEN CLOSED-LOOP AND OPEN-LOOP STRATEGIES;89
7.4.6;6. ROBUST PROBLEMS IN FAULT DIAGNOSIS;90
7.4.7;7. ROBUST CLOSED-LOOP STRATEGIES FOR FDI;91
7.4.8;8. ROBUST OPEN-LOOP STRATEGIES;94
7.4.9;9. BRIEF REVIEW OF RELATED ROBUST FDI METHODS;96
7.4.10;10. DISCUSSION;96
7.4.11;11. ACKNOWLEDGEMENTS;97
7.4.12;12. REFERENCES;97
7.5;CHAPTER 8. SAFETY MANAGEMENT IN A CHEMICAL PLANT;100
7.5.1;Introduction;100
7.5.2;Reducing the hazard potential;102
7.5.3;Risk. Safety. Hazard;103
7.5.4;Strategies to Reduce Risk;103
7.5.5;Systematic hazard analysis;104
7.5.6;Hazard source, protective task and protecting measures;104
7.5.7;Realization of protective measures;105
7.6;CHAPTER 9. ADVANCED FAULT DETECTION FOR SENSORS AND ACTUATORS IN PROCESS CONTROL;108
7.6.1;INTRODUCTION;108
7.6.2;ADVANCED FAULT DETECTION FORSENSORS AND ACTUATORS IN PROCESS CONTROL;108
7.6.3;THEORETICAL APPROACHES;109
7.6.4;APPLICATIONS;111
7.6.5;DISCUSSION;111
7.6.6;LITERATURE;111
7.7;CHAPTER 10. ENHANCEMENT OF ROBUSTNESS INOBSERVER-BASED FAULT DETECTION;116
7.7.1;Introduction;116
7.7.2;The Observer-Based FD I Concept;117
7.7.3;Principle of Robust Residual Generation;117
7.7.4;Methods of Robust Residual Generation;119
7.7.5;Methods of Robust Residual Evaluation;124
7.7.6;Conclusions;126
7.7.7;References;126
8;PART 3:
RELIABILITY AND SAFETY ANALYSIS;10
8.1;CHAPTER 11. RELIABILITY AND SAFETY EVALUATION TECHNIQUES FOR COMPONENTS AND PROCESSES;130
8.1.1;1 Introduction;130
8.1.2;2 Fault Trees;131
8.1.3;3 Markov chains;131
8.1.4;4 Stochastic Petri Nets;133
8.1.5;5 Results;135
8.1.6;6 Conclusions;135
8.1.7;References;135
8.2;CHAPTER 12. ASPECTS OF THE METHODS APPLYING TO A SAFETY ANALYSIS OF A LIQUID NATURAL GAS PLANT;140
8.2.1;INTRODUCTION;140
8.2.2;TASK;140
8.2.3;CONDITIONS COVERING THE METHODS;141
8.2.4;APPLICATION OF THE HAZOP/PAAG METHOD -A CASE STUDY;141
8.2.5;CONCLUSIONS;142
8.2.6;REFERENCES;142
8.3;CHAPTER 13. SENSOR FAULT TOLERANT CONTROL AND ITS APPLICATION;146
8.3.1;INTRODUCTION;146
8.3.2;DESCRIPTION OF THE SLAB HEATING PROCESS;146
8.3.3;SENSOR FAULT DETECTION;147
8.3.4;FAULT TOLERANT CONTROL STRATEGY;148
8.3.5;CONCLUSION;148
8.3.6;REFERENCE;148
8.4;CHAPTER 14. HUMAN RELIABILITY ANALYSIS OF LPG TRUCK LOADING OPERATION;152
8.4.1;INTRODUCTION;152
8.4.2;PROBLEM DEFINITION;152
8.4.3;DISCUSSION;156
8.4.4;CONCLUSION;156
8.4.5;REFERENCES;156
9;PART 4
: FAULT DETECTION AND DIAGNOSIS;11
9.1;Section I:
Fault Diagnosis Based on Signal Analysis;11
9.1.1;CHAPTER 15. FAULT DIAGNOSIS OF ELECTRIC LOW-POWER MOTORS BY ANALYZING THE CURRENT SIGNAL;158
9.1.1.1;INTRODUCTION;158
9.1.1.2;THE FAULT DIAGNOSIS SYSTEM;158
9.1.1.3;THE MOTOR TEST STAND;160
9.1.1.4;THE TESTED UNIVERSAL MOTORS;160
9.1.1.5;RESULTS;160
9.1.1.6;CONCLUSION;163
9.1.1.7;REFERENCES;163
9.1.2;CHAPTER 16. FAULT DIAGNOSIS OF MACHINE-TOOLS BY ESTIMATION OF SIGNAL SPECTRA;164
9.1.2.1;INTRODUCTION;164
9.1.2.2;SIGNAL SPECTRA OF SAMPLED SIGNALS;164
9.1.2.3;ESTIMATION OF SIGNIFICANT FREQUENCIES;165
9.1.2.4;ESTIMATION OF AMPLITUDES;166
9.1.2.5;ESTIMATION OF STOCHASTIC NOISE PARAMETERS;167
9.1.2.6;PRACTICAL RESULTS;168
9.1.2.7;CONCLUSIONS;169
9.1.2.8;REFERENCES;169
9.1.3;CHAPTER 17. CRACK DETECTION IN BEAMS BY RANK-ORDERING OF EIGEN-FREQUENCY SHIFTS;170
9.1.3.1;1. Introduction;170
9.1.3.2;2. THEORY;170
9.1.3.3;3 Experimental investigation;173
9.1.3.4;4. Conclusions;175
9.1.3.5;References;175
10;Section II:
Statistical Fault Detection and Decision Methods;11
10.1;CHAPTER 18. DIAGNOSING MECHANICAL CHANGES IN VIBRATING SYSTEMS;176
10.1.1;1 Introduction;176
10.1.2;2 Global change detection;176
10.1.3;3 The diagnosis problem;177
10.1.4;4 Synthetical jacobians;179
10.1.5;5 Numerical results;179
10.1.6;6 Conclusion;180
10.1.7;References;180
10.2;CHAPTER 19. SELECTING THRESHOLDS FORSEQUENTIAL FAULT DETECTION TESTS;182
10.2.1;INTRODUCTION;182
10.2.2;A SEMI-MARKOV PERFORMANCE MODEL EXAMPLE;183
10.2.3;OPTIMUM THRESHOLD APPROXIMATION;184
10.2.4;NUMERICAL RESULTS;187
10.2.5;CONCLUDING REMARKS;188
10.2.6;ACKNOWLEDGMENT;188
10.2.7;REFERENCES;188
10.3;CHAPTER 20. ESTIMATION OF MEASUREMENT ERROR VARIANCES FROM N-LINEAR PROCESS DATA;190
10.3.1;1 - INTRODUCTION;190
10.3.2;2 - FIRST APPROACH;190
10.3.3;3 - VARIANCE ESTIMATION AND RECONCILIATION;191
10.3.4;4 - RESOLUTION USING DIRECT ITERATION;191
10.3.5;5 - EXTENSION TO N-LINEAR MODEL;192
10.3.6;6 - GENERALIZED N-LINEAR MODEL;192
10.3.7;7 - PARTIALLY OBSERVED SYSTEMS;193
10.3.8;8 - NUMERICAL APPLICATIONS;193
10.3.9;9 - CONCLUSION;194
10.3.10;REFERENCES;194
10.4;CHAPTER 21. DETECTING CHANGES IN STATISTICAL MODELS WITH APPLICATION TO WELL-LOGGING SIGNAL PROCESSING;196
10.4.1;INTRODUCTION;196
10.4.2;PROBLEM FORMULATION;196
10.4.3;BASIC MEASURE OF DISTANCE BETWEEN LOCAL MODEL;197
10.4.4;DETECTING CHANGES PROCEDURE;197
10.4.5;EXAMPLE;198
10.4.6;CONCLUSION;199
10.4.7;REFERENCES;199
11;CHAPTER 22. MODEL BASED FAULT DIAGNOSIS AND SUPERVISION OF THE MAIN AND FEED DRIVES OF A FLEXIBLE MILLING CENTER;202
11.1;1. Introduction;202
11.2;2 Theoretical modeling of the milling machine;202
11.3;3. Parameter estimation of continaus-time processes;204
11.4;4. Physically based model reduction;204
11.5;5. Identification of the parameters of the reduced models;205
11.6;Conclusions:;207
11.7;Acknowledgement:;207
11.8;References;207
11.9;SECTION III
: Model Based Fault Detection via Parameter Estimation;11
11.10;CHAPTER 23. PARAMETER ESTIMATION BY NONLINEAR SMOOTHING FOR FAULT MONITORING ON ROCKET ENGINES;208
11.10.1;INTRODUCTION;208
11.10.2;NOMENCLATURE FOR SSME;209
11.10.3;FULL ORDER SSME SIMULATION;209
11.10.4;REDUCED ORDER MODEL FOR ESTIMATOR DESIGN;210
11.10.5;ALGORITHM DESCRIPTION;210
11.10.6;RESULTS USING SIMULATED SSME DATA;211
11.10.7;RESULTS USING SSME HOT FIRE DATA;212
11.10.8;CONCLUSIONS;212
11.10.9;REFERENCES;213
12;CHAPTER 24. ROBUST FAULT DETECTION BASED ON LOW ORDER MODELS;216
12.1;1 Introduction;216
12.2;2 Failure Detection;216
12.3;3 Use of Low Order Models;218
12.4;4 Avoiding Bias Problems;218
12.5;5 Simulation Example;219
12.6;6 Conclusion;220
12.7;REFERENCES;221
13;CHAPTER 25. A MODEL-BASED FAULT DETECTION CONCEPT FOR MECHANICAL SYSTEMS;222
13.1;INTRODUCTION;222
13.2;CANONICAL FORMS;222
13.3;CONVENTIONAL IDENTIFICATION;223
13.4;DIFFERENTIAL IDENTIFICATION;224
13.5;RESIDUAL GENERATION;225
13.6;RESIDUAL EVALUATION;226
13.7;SIMULATION RESULTS;226
13.8;CONCLUSIONS;227
13.9;ACKNOWLEDGEMENT;227
13.10;REFERENCES;227
14;CHAPTER 26. MODEL BASED FAULT DIAGNOSIS AND SUPERVISION OF THE DRILLING PROCESS;228
14.1;1 Introduction;228
14.2;2 Theoretical modeling of the drilling process;228
14.3;3 Continuous time parameter estimation of the drilling process;230
14.4;4 Tool wear monitoring by parameter estimation;230
14.5;5. Fault diagnosis;233
14.6;Conclusions;233
14.7;Acknowledgements;233
14.8;References;233
15;CHAPTER 27. MONITORING AND DIAGNOSIS OF A GRINDING PROCESS;234
15.1;1. INTRODUCTION;234
15.2;2. MILL MODELLING;234
15.3;3 SIMULATION MODEL;235
15.4;4. PROCESS PARAMETER ESTIMATION;236
15.5;5. ROBUSTNESS ISSUES IN PARAMETER ESTIMATION;237
15.6;6. REAL TIME IMPLEMENTATION AND RESULTS;237
15.7;7. CONCLUDING REMARKS;238
15.8;REFERENCES;238
16;CHAPTER 28. COMPONENT, ACTUATOR AND INSTRUMENT MARGINAL SIZE FAULTS ISOLATION VIA STATE ESTIMATION;240
16.1;INTRODUCTION;240
16.2;THE METHOD;241
16.3;APPLICATION;242
16.4;CONCLUSION;245
16.5;REFERENCE;245
17;CHAPTER 29. OPTIMAL SELECTION OF UNKNOWN INPUT DISTRIBUTION MATRIX IN THE DESIGN OF ROBUST OBSERVERS FORFAULT DIAGNOSIS;246
17.1;INTRODUCTION;246
17.2;PROBLEM STATEMENT;247
17.3;BRIEF DETAILS OF ROBUST FAULT DETECTION BY USING EIGENSTRUCTURE ASSIGNMENT;247
17.4;OPTIMIZATION OF UNKNOWN INPUT DISTRIBUTION MATRIX;248
17.5;AN EXAMPLE;249
17.6;CONCLUDING DISCUSSION;251
17.7;ACKNOWLEDGMENT;251
17.8;REFERENCES;251
18;CHAPTER 30. ROBUST OBSERVERS FOR AUTOMATIC TRACK CONTROL AND INSTRUMENT FAULT DETECTION OF A CITY BUS;252
18.1;INTRODUCTION;252
18.2;DESCRIPTION OF THE TRACK-GUIDED CITY BUS;252
18.3;DESIGN OF ROBUST DRC-OBSERVERS;253
18.4;DESIGN OF AN IFD-OBSERVER;255
18.5;CONCLUSIONS;256
18.6;REFERENCES;256
19;CHAPTER 31. A NONLINEAR OBSERVER FOR SENSOR FAULT DETECTION IN AN AIRPLANE;258
19.1;INTRODUCTION;258
19.2;THE NONLINEAR OBSERVER MODEL;259
19.3;RESULTS OF THE OBSERVER;261
19.4;THE POLYNOMIAL CLASSIFIER;262
19.5;SUMMARY AND CONCLUSION;263
19.6;REFERENCES;263
20;CHAPTER 32. FAILURE DETECTION AND IDENTIFICATION OF STRUCTURAL CONTROL SYSTEMS;264
20.1;INTRODUCTION;264
20.2;CONTROL OF FLEXIBLE STRUCTURES;264
20.3;FAILURE DETECTION METHOD;266
20.4;ILLUSTRATIVE EXAMPLE;267
20.5;CONCLUSIONS;268
20.6;REFERENCES;268
21;CHAPTER 33. DISTINCTION OF ACTUATOR FAILURES FROM PLANT DYNAMIC CHANGES;270
21.1;INTRODUCTION;270
21.2;REFERENCES;273
21.3;CONCLUSION;275
21.4;APPENDIX;275
22;CHAPTER 34. ROBUST FAULT DETECTION;276
22.1;BACKGROUND;276
22.2;DESIGN APPROACH;277
22.3;APPLICATION;279
22.4;CONCLUSION;280
22.5;References;281
23;CHAPTER 35. DESIGN OF ROBUST OBSERVERS FOR FAULT ISOLATION;282
23.1;1 Introduction;282
23.2;2 Problem Formulation;283
23.3;3 Preliminaries;283
23.4;4 Fault Detection and Isolation Observer;284
23.5;5 Illustrative Example;285
23.6;6 Conclusions;286
23.7;Acknowledgement;286
23.8;References;286
24;CHAPTER 36. FREQUENCY DOMAIN APPROACH AND THRESHOLD SELECTOR FOR ROBUST MODEL-BASED FAULT DETECTION AND ISOLATION;288
24.1;INTRODUCTION;288
24.2;PROBLEM FORMULATION;288
24.3;NOTATION AND PRELIMINARIES;289
24.4;RESIDUAL GENERATION;289
24.5;RESIDUAL EVALUATION;291
24.6;CONCLUSIONS;292
24.7;REFERENCES;292
25;CHAPTER 37. ROBUST COMPONENT FAULT DETECTION AND ISOLATION IN NONLINEAR DYNAMIC SYSTEMS USING NONLINEAR UNKNOWN INPUT OBSERVERS;294
25.1;MODELLING OF COMPONENT FAULTS AND UNKNOWN INPUTS;294
25.2;ROBUST COMPONENT FAULT DETECTION OBSERVER;295
25.3;DESIGN OF RCFDO WITH LINEAR ERROR DYNAMICS;296
25.4;APPLICATION TO A SYNCHRONOUS MACHINE;297
25.5;CONCLUSIONS;298
25.6;REFERENCES;298
25.7;APPENDIX;298
26;CHAPTER 38. ON FAILURE DETECTION AND IDENTIFICATION: AN OPTIMUMROBUST MIN-MAX APPROACH;300
26.1;INTRODUCTION;300
26.2;PROBLEM FORMULATION;300
26.3;THE BASIC PROBLEM;301
26.4;FAILURE ISOLATION;302
26.5;THE BATCH PROCESSING ALGORITHM;302
26.6;THE RECURSIVE ALGORITHMS;302
26.7;SIMULATION RESULTS;303
26.8;CONCLUSION;304
26.9;APPENDIX;304
26.10;REFERENCES;304
27;CHAPTER 39. INSTRUMENT FAULT DETECTION AND IDENTIFICATION BASED ON ANALYTICAL REDUNDANCY;306
27.1;INTRODUCTION;306
27.2;IDENTIFICATION OF SENSOR FAULTS;307
27.3;IDENTIFICATION OF PROCESS PARAMETERS;307
27.4;RESULTS;310
27.5;CONCLUSIONS;311
27.6;REFERENCES;311
27.7;ACKNOWLEDGEMENT;311
28;CHAPTER 40. EFFICIENCY CALCULATION ANDSENSOR FAULT DETECTION IN A LARGE SCALE PLANT;312
28.1;NITRIC ACID PRODUCTION PROCESS;312
28.2;A SIMPLE MATHEMATICAL PROCESSMODEL;313
28.3;DETECTION OF SENSOR ERROR;313
28.4;HARDWARE REDUNDANCY;314
28.5;SOFTWARE REDUNDANCY;314
28.6;EXAMPLE;316
28.7;SUMMARY;316
28.8;NOTATION;316
28.9;REFERENCES;316
29;CHAPTER 41. MODEL BASED DIAGNOSIS OF GAS TURBINES INCLUDING SENSOR FAULT DETECTION;318
29.1;INTRODUCTION;318
29.2;MODELING;318
29.3;DIAGNOSIS AND SENSOR FAULT DETECTION;319
29.4;APPLICATIONS;320
29.5;REFERENCES;323
30;CHAPTER 42. REAL-TIME DIAGNOSIS AND RECOVERY IN HIERARCHICAL FMS CONTROL;324
30.1;INTRODUCTION;324
30.2;STRUCTURE FOR REAL-TIME CONTROL AND MONITORING OF F.M.S.;324
30.3;GLOBAL OPERATIONOF A MODULE;325
30.4;STRUCTURE OF A MODULE;325
30.5;HUMAN OPERATOR IN THE MONITORING LOOP;325
30.6;FAILURE DETECTION;326
30.7;THE DIAGNOSIS SYSTEM;326
30.8;THE RECOVERY FUNCTION;327
30.9;CONCLUSION;329
30.10;REFERENCES;329
31;CHAPTER 43. MONITORING BEHAVIORAL EVOLUTION FOR ON-LINE FAULT DETECTION;330
31.1;INTRODUCTION;330
31.2;THE STEEL FURNACE PROCESS;331
31.3;THE BEHAVIORAL MODEL;331
31.4;EVOLUTION GRAPHS AND TRAJECTORY ENCODING;333
31.5;MONITORING WITH EVOLUTION GRAPHS;334
31.6;ILLUSTRATION OF EVOLUTION GRAPH MANAGER;335
31.7;IMPLEMENTATION AND RESEARCH ISSUES;336
31.8;REFERENCES;336
32;CHAPTER 44. PETRI NET BASED SYSTEM FOR MONITORING, DIAGNOSIS AND THERAPY OF FAILURES IN COMPLEX MANUFACTURING SYSTEMS;338
32.1;1. Introduction;338
32.2;2. Petri nets;338
32.3;3. The Diagnosis System;339
32.4;4. Integrated Automation Environment;342
32.5;5. Application;343
32.6;6. Conclusion;343
32.7;REFERENCES;343
32.8;Acknoledgements;343
33;CHAPTER 45. SECOND GENERATION DIAGNOSTIC EXPERT SYSTEMS: REQUIREMENTS, ARCHITECTURES AND PROSPECTS;344
33.1;INTRODUCTION;344
33.2;LIMITATIONS OF FIRST-GENERATION E.S.;344
33.3;REQUIREMENTS AND ARCHITECTURESOF SECOND-GENERATION E.S.;345
33.4;THE E.S. APPROACH TO SYSTEMDEVELOPMENT;347
33.5;A QUALITATIVE REASONINGDIAGNOSTIC EXPERT SYSTEM;347
33.6;PROSPECTS OF SECOND-GENERATIONSYSTEMS;348
33.7;REFERENCES;349
34;CHAPTER 46. INTEGRATION: THE KEY TO SECOND GENERATION APPLICATIONS;350
34.1;RULEBASED APPLICATIONS;350
34.2;IMPLICATIONS FOR SECOND GENERATION SYSTEMS;350
34.3;SUMMARY;352
34.4;REFERENCES;352
35;CHAPTER 47. A METHOD FOR LOGIC-BASED FAULT DIAGNOSIS;354
35.1;INTRODUCTION;354
35.2;1. THE DIAGNOSIS PROBLEM;354
35.3;2 . THE PREDICATE LOGIC DESCRIPTION OF THE PROCESS;355
35.4;3. STATEMENT OF THE DIAGNOSIS PROBLEM IN PREDICATE LOGIC;356
35.5;4. SURVEY OF THE WAY OF SOLUTION;356
35.6;5. THE CAUSAL STRUCTURE OF DYNAMICAL SYSTEMS;356
35.7;6. TRANSFORMATION OF THE PROCESS MODEL;357
35.8;7 . THE DIAGNOSIS SYSTEM;358
35.9;8 . EXAMPLE;358
35.10;REFERENCES;359
36;CHAPTER 48. MULTI-LEVEL SIGNAL ABSTRACTION FOR DIAGNOSING ANALOGUE CIRCUITS;360
36.1;INTRODUCTION;360
36.2;SEGMENTATION;361
36.3;SECOND-LEVEL SYMBOL GENERATION;362
36.4;GENERAL CONSIDERATIONS ON SIGNALS AND SYMBOLS ON THE HIGHEST ABSTRACTION LEVEL;362
36.5;GENERAL SYSTEM ARCHITECTURE;363
36.6;EXAMPLE : SIGNAL-ABSTRACTION FOR PROBE CALIBRATION;363
36.7;CONCLUSION;364
36.8;ACKNOWLEDGEMENTS;364
36.9;REFERENCES;364
37;CHAPTER 49. MODEL BASED INTELLIGENT PROCESS MONITORING AND REAL-TIME DIAGNOSIS;366
37.1;INTRODUCTION;366
37.2;CONVENTIONAL APPROACHES;366
37.3;DESIGN CONSIDERATIONS;366
37.4;CO-GENERATION PLANT;367
37.5;MODELS IN IPCS;367
37.6;DIAGNOSIS ALGORITHM;369
37.7;PROGRAM AND EXECUTION ENVIRONMENT;371
37.8;AN APPLICATION TO A CO-GENERATION PLANT;371
37.9;CONCLUSION;371
37.10;REFERENCES;371
38;CHAPTER 50. FAULT DIAGNOSIS IN DIAPASON, A CONTINUOUS PROCESS CONTROL AID SYSTEM;372
38.1;ABSTRACT;372
38.2;KEYWORDS;372
38.3;INTRODUCTION;372
38.4;THE DIAGNOSIS SYSTEM;372
38.5;KNOWLEDGE REQUIREMENTS;374
38.6;KNOWLEDGE STRUCTURE;374
38.7;INFERENCE MECHANISM;375
38.8;APPLICATION;376
38.9;CONCLUSION;376
38.10;ACKNOWLEDGEMENTS;376
38.11;REFERENCES;377
39;CHAPTER 51. DIAGNOSIS OF FLEXIBLE MANUFACTURING SYSTEMS (FMS) -ASPECTS OF ON-LINE ERRO RDETECTION AND KNOWLEDGE PROCESSING;378
39.1;Introduction;378
39.2;OFF-LINE DIAGNOSTIC SYSTEM;378
39.3;ON-LINE DIAGNOSIS;379
39.4;PROCESS-LINKAGE -ERROR DETECTION AND MESSAGE HANDLING;380
39.5;CHANGE-OVER BETWEEN ON- AND OFF LINE DIAGNOSIS;381
39.6;EXAMPLE OF A DIAGNOSIS;381
39.7;EVALUATION;382
39.8;CONCLUSION;382
39.9;ACKNOWLEDGEMENT;383
39.10;REFERENCES;383
40;CHAPTER 52. KNOWLEDGE BASED INCIPIENT FAULT DIAGNOSIS OF INDUSTRIAL ROBOTS;386
40.1;INTRODUCTION;386
40.2;FAULT DETECTION VIA MODEL BASED SYMPTOM GENERATION;387
40.3;REALIZATION AND IMPLEMENTATION;389
40.4;EXPERIMENTAL RESULTS;390
40.5;CONCLUSIONS;392
40.6;REFERENCES;392
41;CHAPTER 53. A SYSTEM FOR DIAGNOSING FAULTS IN HYDRAULIC CIRCUITS BASED ON QUALITATIVE MODELS OF COMPONENT BEHAVIOUR;394
41.1;INTRODUCTION;394
41.2;BACKGROUND TO QUALITATIVE REASONING;394
41.3;EVALUATION OF THE QUALITATIVE REASONING APPROACH;395
41.4;DEVELOPMENT OF QUALITATIVE COMPONENT MODELS;395
41.5;SOFTWARE ARCHITECTURE;396
41.6;APPLICATION TO SINGLE FLOWPATH TYPE CIRCUITS;397
41.7;APPLICATION TO HYDROSTATIC TRANSMISSIONS;397
41.8;CONCLUSIONS;398
41.9;REFERENCES;399
42;CHAPTER 54. KNOWLEDGE-BASED NOISE ANALYSIS: A PROMISING TOOL FOR EARLY FAILURE DETECTION IN NUCLEAR POWER PLANTS
;400
42.1;INTRODUCTION;400
42.2;SYSTEM PHILOSOPHY AND ARCHITECTURE;401
42.3;OFF-LINE SIGNAL PROCESSING SYSTEM;402
42.4;OFF-LINE FAILURE DETECTION SYSTEM;403
42.5;DIAGNOSTIC INFERENCE SYSTEM;404
42.6;THE SIGNAL PROCESSING SUPERVISOR;405
42.7;OPERATIONAL MODES OF THE SYSTEM;405
42.8;APPLICATION;406
42.9;CONCLUSIONS;406
42.10;REFERENCES;407
43;CHAPTER 55. AN EXPERT SYSTEM FOR DIAGNOSIS OF TURBO-MACHINERY FAULTS;408
43.1;Introduction;408
43.2;Expert System;408
43.3;TURBEX;408
43.4;EXAMPLES;409
43.5;Conclusion;411
43.6;References;411
44;CHAPTER 56. HIERARCHICAL ON-LINE DIAGNOSIS SYSTEM FOR POWER PLANTS;414
44.1;INTRODUCTION;414
44.2;CONDITION MONITORING AND FAULT DIAGNOSIS IN POWER PLANTS;414
44.3;DIAGNOSIS ALGORITHMS;415
44.4;MODEL IDENTIFICATION;416
44.5;SYSTEM IMPLEMENTATION;416
44.6;USER INTERFACE;417
44.7;FAULT DIAGNOSIS ON EXPERT SYSTEM LEVEL;417
44.8;CURRENT STATE OF THE IMPLEMENTATION;418
44.9;CONCLUSIONS;418
44.10;REFERENCES;419
45;CHAPTER 57. KNOWLEDGE BASED REAL TIME FAULT DIAGNOSIS WITH EFTAS;420
45.1;INTRODUCTION;420
45.2;KNOWLEDGE REPRESENTATION WITH FAULT TREES;420
45.3;INFERENCE RULES;421
45.4;EVALUATION OF PROBABILITIES;422
45.5;CALCULATION OF RELEVANCE NUMBERS;423
45.6;FAULT DIAGNOSIS WITH EFTAS;423
45.7;APPLICATION;424
45.8;CONCLUSIONS;425
45.9;REFERENCES;425
46;CHAPTER 58. COMPARISON OF NEURAL AND CLASSICAL DECISION ALGORITHMS;426
46.1;Problem Definition;426
46.2;Feature Determination;426
46.3;Numercal Decision Algorithms;427
46.4;Neural Networks as Pattern Classifiers;430
46.5;Applications: Machines with Rotating Parts;431
46.6;Conclusions;432
46.7;References;432
47;CHAPTER 59. MULTILAYERED PERCEPTRONS AS CLASSIFIERS FOR AUTOMATIC INSPECTION;434
47.1;INTRODUCTION;434
47.2;MULTILAYERED PERCEPTRONS AND RCE-NETS;435
47.3;INSPECTION SYSTEM ENVIRONMENT;436
47.4;SIMULATIONS AND RESULTS;437
47.5;REFERENCES;439
48;CHAPTER 60. APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN PROCESS FAULT DIAGNOSIS;440
48.1;INTRODUCTION;440
48.2;NEURAL NETWORKS IN FAULT DIAGNOSIS;441
48.3;EXAMPLE PROCESS;441
48.4;MULTI-LAYER PERCEPTRON;442
48.5;COUNTERPROPAGATION NETWORK;443
48.6;THE NEAREST-NEIGHBOR RULE;444
48.7;CONCLUSION;445
48.8;REFERENCES;445
49;CHAPTER 61. AN HYBRID CONNECTIONIST MODEL FOR A DECISION SYSTEM WITH A REJECT OPTION;446
49.1;INTRODUCTION;446
49.2;DEVELOPMENT;447
49.3;APPLICATION;448
49.4;CONCLUSION;449
49.5;REFERENCES;449
50;CHAPTER 62. QUALITATIVE EVENT ANALYSIS FOR FAULT DIAGNOSIS;450
50.1;ABSTRACT;450
50.2;KEY WORDS;450
50.3;INTRODUCTION;450
50.4;2. STATE OF THE ART;450
50.5;3 BASIC CONCEPTS OF THE MODELLING;451
50.6;4. FAULT DETECTION;451
50.7;5. CAUSALITY ANALYSIS;453
50.8;6. APPLICATION;454
50.9;DISCUSSION;455
50.10;BIBLIOGRAPHY;455
51;CHAPTER 63. HIERARCHICAL FAULT DIAGNOSIS SYSTEM UTILIZING THE SIGNED DIRECTED GRAPH AND THE EXTENDED KALMAN FILTER;456
51.1;INTRODUCTION;456
51.2;HIERARCHICAL FAULT DIAGNOSIS SYSTEM;456
51.3;APPLICATION;458
51.4;CONCLUSION;460
51.5;REFERENCES;460
52;CHAPTER 64. RELIABILITY ASSURING MAINTENANCE;462
52.1;INTRODUCTION;462
52.2;STRUCTURE OF TOTAL MANAGEMENT SYSTEM OF PLANT MAINTENANCE;463
52.3;LOGICS OF GENERATING INDIVIDUAL EQUIPMENT DATABASE;464
52.4;LOGICS OF INDIVIDUAL ELEMENT CONTROL;466
52.5;DISCUSSION;467
53;CHAPTER 65. FRAMEWORK FOR COMPUTER ASSISTED LIFE-CYCLE MAINTENANCE SYSTEM;468
53.1;INTRODUCTION;468
53.2;EXISTING FRAMEWORK FOR MAINTENANCE;468
53.3;LIFE-CYCLE MAINTENANCE BASED ON DETERIORATION PREDICTION;469
53.4;ARCHITECTURE OF THE COMPUTER ASSISTED LIFE-CYCLE MAINTENANCE SYSTEM;470
53.5;TECHNOLOGIES NEEDED FOR DEVELOPMENTOF CALMS;471
53.6;CONCLUSION;472
53.7;REFERENCES;473
54;CHAPTER 66. MODEL EQUATION AND FAULT DETECTION OF ELECTRIC MOTORS;474
54.1;ABSTRACT;474
54.2;KEYWORDS;474
54.3;INTRODUCTION;474
54.4;MODELING OF ALOW POWER D.C. MOTOR;475
54.5;FAULT DETECTION;476
54.6;CONCLUSIONS;476
54.7;REFERENCES;477
55;CHAPTER 67. CHARACTERISTIC CURVE DETERMINATION AND FAULT DETECTION OF INDUSTRIAL LYPRODUCED SERIES-WOUND MOTORS;478
55.1;INTRODUCTION;478
55.2;STATE OF THE ART;478
55.3;NEW APPROACH;479
55.4;CONCLUSIONS;482
55.5;RESULTS;482
55.6;REFERENCES;482
56;CHAPTER 68. MONITORING OF A STATIC CONVERTER FED MACHINE USING AVERAGE REFERENCE MODELS;484
56.1;1 - INTRODUCTION;484
56.2;2 - DESCRIPTION OF THE APPLICATION;484
56.3;3 - CONVERTER AND DC POWER SUPPLY DEFECTS;484
56.4;4 - MODELING;484
56.5;5 - DESCRIPTION OF THE SIMULATION;485
56.6;6-DESIGN METHOD;485
56.7;7 - RESIDUAL GENERATION;486
56.8;8 - FAULT DIAGNOSTIC LOGIC;487
56.9;9 - CONCLUSION;488
56.10;References;488
57;CHAPTER 69. FAULT DETECTION AND DIAGNOSIS INPROPULSION SYSTEMS; A REAL TIME IDENTIFICATION APPROACH;490
57.1;INTRODUCTION;490
57.2;MODEL OF THE FAULTY PROCESS;491
57.3;DIAGNOSTIC MODEL;492
57.4;CONCLUSION;493
57.5;REFERENCES;493
58;CHAPTER 70. ROBUST DETECTION FILTER DESIGN FOR JET ENGINE CONTROL SYSTEM;496
58.1;1. INTRODUCTION;496
58.2;2. SOME RESULTS OF DETECTION FILTER;496
58.3;3. COMBINATION METHOD FOR DESIGNING A ROBUST DETECTION FILTER;497
58.4;4. DESIGN EXAMPLE;498
58.5;5. CONCLUSION;500
58.6;REFERENCES;500
59;CHAPTER 71. APPLICATION OF ROBUST FAULT DETECTION METHODS TO F404 GAS TURBINE ENGINES;502
59.1;1 Introduction;502
59.2;2 Failure Detection;502
59.3;3 Analysis of Data;504
59.4;4 Conclusion;505
59.5;REFERENCES;505
60;CHAPTER 72. REAL-TIME FAULT DIAGNOSIS FOR PROPULSION SYSTEMS;508
60.1;INTRODUCTION;508
60.2;SENSOR FAULT DETECTION;508
60.3;INTELLIGENT FAULT DIAGNOSIS;510
60.4;CONCLUSIONS;511
60.5;REFERENCES;511
61;CHAPTER 73. ON-BOARD DIAGNOSTICS OF VEHICLE EMISSION SYSTEM COMPONENTS: REVIEW OF UPCOMING GOVERNMENT REGULATION;514
61.1;INTRODUCTION;514
61.2;CATALYTIC CONVERTER RFFICIENCY;514
61.3;MISFIRE DETECTION;515
61.4;ENGINE CONTROL SYSTEM COMPONENTS;515
61.5;IDEAL ENGINE ON-BOARD FAILURE DETECTION SYSTEM;516
61.6;FAILURE DETECTION LITERATURE REVIEW;517
61.7;SUMMARY;517
61.8;REFERENCES;517
62;CHAPTER 74. MODEL-BASED ON-BOARD FAULT DETECTION AND DIAGNOSIS FOR AUTOMOTIVE ENGINES;520
62.1;INTRODUCTION;520
62.2;THE FAULT DETECTION AND ISOLATION METHODOLOGY;520
62.3;THE CAR ENGINE PROBLEM;522
62.4;PARITY EQUATION STRUCTURES;523
62.5;OXYGEN-SENSOR RELATED RESIDUALS;523
62.6;IDENTIFICATION OF THE PARITY EQUATIONS;524
62.7;VALIDATION OF THE PARITY EQUATIONS;524
62.8;REFERENCES;524
63;CHAPTER 75. A ROBUST FAILURE DETECTION AND ISOLATION METHOD FOR AUTOMOTIVE POWER TRAIN SENSORS;526
63.1;1. Introduction;526
63.2;2. Four State Power train Model;526
63.3;3. Failure Detection Filters;527
63.4;4. Residuals and Fault Detection Thresholds;527
63.5;5. Hardware Configuration;530
63.6;6. Experimental Results;531
63.7;7. Conclusion;531
63.8;8. Acknowledgements;531
63.9;References;532
64;CHAPTER 76. A HIERARCHICAL MODEL-BASED FAULT DIAGNOSIS SYSTEM FOR A PEAT POWER PLANT;534
64.1;INTRODUCTION;534
64.2;MODEL BASED-DIAGNOSIS;535
64.3;HIERARCHICAL, MODULAR SYSTEM;536
64.4;A PEAT-FIRED POWER PLANT;536
64.5;REAL-TIME APPLICATION;538
64.6;CONCLUSION;539
64.7;REFERENCES;539
65;CHAPTER 77. QUALITATIVE SIMULATION FOR SUPERVISION OF A NUCLEAR REPROCESSING PLANT;540
65.1;ABSTRACT;540
65.2;KEYWORDS;540
65.3;1. INTRODUCTION;540
65.4;2. MODELING;540
65.5;3. SIMULATION;541
65.6;4. HELP PROPOSED TO OPERATORS;542
65.7;5. DISCUSSION;543
65.8;REFERENCES;544
66;CHAPTER 78. ENVELOPE CURVE ANALYSIS OF MACHINES WITH ROLLING-ELEMENT BEARINGS;546
66.1;INTRODUCTION;546
66.2;IMPACT-LIKE EXCITATIONS WITHIN THE ACOUSTIC EMISSION AND VIBRATION SIGNALS;546
66.3;ENVELOPE CURVE ANALYSIS;548
66.4;CONCLUSIONS;550
66.5;REFERENCES;550
67;CHAPTER 79. SUPERVISION SYSTEM DESIGN FOR A PETROLEUM PRODUCTION APPLICATION;552
67.1;I-INTRODUCTION;552
67.2;II - APPLICATION;553
67.3;Ill- DESIGN OF A SUPERVISION USING STRUCTURAL ANALYSIS;554
67.4;IV- RESULTS AND CONCLUSION;556
67.5;REFERENCES;556
68;CHAPTER 80. FAULT CLASSIFICATION WITH THE AID OF ARTIFICIAL NEURAL NETWORKS;558
68.1;INTRODUCTION;558
68.2;PREVIOUS INVESTIGATIONS USING ANN FOR FAULT DETECTION;558
68.3;FEATURES OF ARTIFICIAL NEURAL NETWORKS USE FUL FOR FAULT DETECTION AND DIAGNOSIS;559
68.4;EXAMPLE APPLICATION OF ANN: DIAGNOSIS OF FAULTS IN A HEAT EXCHANGER;559
68.5;INTERNAL REPRESENTATION IN ANN USED FOR FAULT DIAGNOSIS;559
68.6;EXAMPLE APPLICATION OF ANN: DECTION OF TOOL WEAR;560
68.7;CONCLUSIONS;561
68.8;REFERENCES;561
69;CHAPTER 81. DIAGNOSING NOISY PROCESS DATA USING NEURAL NETWORKS;564
69.1;INTRODUCTION;564
69.2;DESCRIPTION OF CASE STUDY;564
69.3;NETWORK ARCHITECTURE AND TRAINING STRATEGY;564
69.4;RESULTS;566
69.5;CONCLUSIONS;567
69.6;REFERENCES;567
70;CHAPTER 82. FAULT DIAGNOSIS OF THE CHEMICAL PROCESS UTILIZING SIGNED DIRECTED GRAPH;570
70.1;INTRODUCTION;570
70.2;MODEL OF THE SYSTEM AND REPRESENTATION OF FAILURE;570
70.3;IMPROVED ALGORITHM USING THE CONCEPT OF AMPLIFYING BRANCH;571
70.4;FAULT DIAGNOSIS ALGORITHM;572
70.5;ESTIMATION OF THE ACCURACY OF THE DIAGNOSTIC RESULT;573
70.6;CONCLUSION;573
70.7;EXPERIMENTS ON TANK-PIPELINE SYSTEM;574
70.8;PREFERENCES;575
71;CHAPTER 83. PROCESS- AND SIGNAL-MODEL BASEDFAULT DETECTION OF THE GRINDINGPROCESS;576
71.1;INTRODUCTION;576
71.2;STRATEGY OF MODEL BASED FAULT DIAGNOSIS;576
71.3;THEORETICAL PROCESS AND SIGNAL MODELS;577
71.4;LEAST SQUARES PARAMETER ESTIMATION;579
71.5;IDENTIFICATION OF SIGNAL- AND PROCESS MODEL;580
71.6;EXPERIMENTAL RESULTS;580
71.7;CONCLUSIONS.;581
71.8;REFERENCES.;581
72;CHAPTER 84. SENSOR SYSTEM FOR MONITORING OF MULTISPINDLE DRILLING;582
72.1;PARTICULAR REQUIREMENTS FOR MONITORING MULTISPINDLE DRILLING;582
72.2;SENSOR FOR MULTISPINDLE DRILLING;583
72.3;INTELLIGENT SENSOR SYSTEM;587
72.4;REFERENCES;587
73;CHAPTER 85. CONDITION MONITORING MACHINE TOOL DRIVES VIA HEALTH INDICES;588
73.1;INTRODUCTION;588
73.2;SYSTEM DESCRIPTION;588
73.3;SELECTION OF MONITORING SIGNAL;589
73.4;SIGNAL PROCESSING AND DATA ACQUISITION;589
73.5;INITIAL TESTING;589
73.6;HEALTH MEASUREMENT;589
73.7;APPLICATIONS;590
73.8;EXPERIMENTAL RESULTS;591
73.9;DISCUSSION;591
73.10;CONCLUSION;592
73.11;REFERENCES;592
74;CHAPTER 86. MODELBASED SUPERVISION AND DIAGNOSTIC OF THE STATE OF THE TURNING TOOL;594
74.1;INTRODUCTION;594
74.2;CHIP FORMING PROCESS;594
74.3;DYNAMIC PROCESS MODEL;595
74.4;DIAGNOSIS AND FORECASTING OF TOOL WEAR;596
74.5;CONCLUSION;597
74.6;REFERENCES;597
75;CHAPTER 87. MONITORING OF GRIPPING FORCE IN LATHE CHUCKS;598
75.1;INTRODUCTION;598
75.2;DYNAMIC GRIPPING FORCE MEASUREMENT SYSTEM;599
75.3;FRICTION MONITORING SYSTEM FOR LATHE CHUCKS;601
75.4;CONCLUSION;603
75.5;REFERENCES;603
76;CHAPTER 88. ZERO FAULT - FAULT DETECTION -FAULT TOLERANCE;604
76.1;INTRODUCTION;604
76.2;REFERENCES;607
77;CHAPTER 89. FAULT DETECTION AND DIAGNOSIS APPLIED TO A PILOT PROCESS;608
77.1;I HIERARCHICAL STAGES OF A DETECTION DIAGNOSIS PROCEDURE.;608
77.2;II DETECTION LEVEL;608
77.3;III DIAGNOSIS LEVEL;610
77.4;IV CONCLUSIONS;613
77.5;REFERENCES;613
78;CHAPTER 90. ON-LINE FAULT-DETECTION AND DIAGNOSTICS ON PAPER MACHINES;614
78.1;1 Special features of the papermaking process and needs for fault-detection and diagnostics;614
78.2;2 State of the art;615
78.3;3 WEDGE system;615
78.4;4 FLEXPRO analysis;617
78.5;5 Future development;618
78.6;REFERENCES;619
79;CHAPTER 91. ANALYSIS OF THE OFFSHORE DRILLING PROCESS FOR SAFETY ASPECTS;620
79.1;INTRODUCTION;620
79.2;THE DRILLING PROCESS;620
79.3;MAIN SAFETY HAZARD: KICKS AND BLOWOUT;621
79.4;THE ANALYSIS HIERARCHY;621
79.5;THE STATUS ANALYSIS;621
79.6;THE OPERATIONAL PROCEDURE MONITORING;622
79.7;THE NORMALITY ANALYSIS;622
79.8;FAULT AND EVENT DETECTION;623
79.9;DIAGNOSIS MODULE;623
79.10;SOFTWARE IMPLEMENTATION;623
79.11;RESULTS;623
79.12;CONCLUSIONS;623
79.13;REFERENCES;624
80;CHAPTER 92. MULTIPLE MODEL FILTERING FOR THE DETECTION OF TRANS FORMER RATIO CHANGES IN ELECTRIC POWER SYSTEMS;626
80.1;1. Introduction;626
80.2;2. The Physical Model of Transformer;626
80.3;3. Detection and Estimation Problem;627
80.4;4. Review of Previous Results;627
80.5;5. Multiple Model Filtering Algorithm;628
80.6;6. Experimental Results;629
80.7;7. Discussion;630
80.8;8. Conclusions;630
80.9;9. References;630
81;CHAPTER 93. EDUCATION IN SAFETY MANAGEMENT OF TECHNICAL SYSTEMS;632
81.1;RESULT;632
82;CHAPTER 94. MODERN MAINTENANCE;636
83;CHAPTER 95. SIGNAL AND/OR MODEL BASEDDIAGNOSIS;638
84;AUTHOR INDEX;640
85;KEYWORD INDEX;642