Real time fault monitoring of industrial processes:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Dordrecht [u.a.]
Kluwer
1994
|
| Series: | International series on microprocessor based and intelligent systems engineering
12 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXXIII, 542 S. graph. Darst. |
| ISBN: | 0792327373 |
Staff View
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| 650 | 4 | |a Fault location (Engineering) | |
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Record in the Search Index
| _version_ | 1804125317199036416 |
|---|---|
| adam_text | REAL TIME FAULT MONITORING OF INDUSTRIAL PROCESSES BY A. D. POULFFIZOS
TECHNICAL UNIVERSITY OFCRETE, DEPARTMENT OFPRODUCTION ENGINEERING AND
MANAGEMENT, CHANIA, GREECE AND G. S. STAVRAKAKIS TECHNICAL UNIVERSITY
OFCRETE, ELECTRONIC ENGINEERING AND COMPUTER SCIENCE DEPARTMENT, CHANIA,
GREECE HM KLUWER ACADEMIC PUBLISHERS DORDRECHT/ BOSTON / LONDON TABLE
OFCONTENTS PREFACE XI LIST OFFIGURES XV LISTOFTABLES XXI INTRODUCTION
XXUEI CHAPTER 1 FAULT DETECTION AND DIAGNOSIS METHODS IN THE ABSENCE OF
PROCESS MODEL 1.1 INTRODUCTION 1 1.2 STATISTICAL AIDS FOR FAULT
OCCURRENCE DECISION MAKING 2 1.2.1 TESTS ON THE STATISTICAL PROPERTIES
OF PROCESS CHARACTERISTIC QUANTITIES 2 1.2.1.1 LIMIT CHECKING FAULT
MONITORING IN ELECTRICAL DRIVES 20 1.2.1.2 STEADY-STATE AND DRIFT
TESTING IN A GRINDING-CLASSIFICATION CIRCUIT 21 1.2.1.3 CONCLUSIONS 24
1.2.2 PROCESS CONTROL CHARTS 26 1.2.2.1 AN APPLICATION EXAMPLE FOR
STATISTICAL PROCESS CONTROL (SPC) 40 1.2.2.2 CONCLUSIONS 42 1.3 FAULT
DIAGNOSIS BASED ON SIGNAL ANALYSIS INSTRUMENTATION 43 1.3.1 MACHINE
HEALTH MONITORING METHODS 43 1.3.2 VIBRATION AND NOISE ANALYSIS
APPLICATION EXAMPLES 64 1.3.3 CONCLUSIONS . 77 REFERENCES 78 APPENDIX
LA 82 APPENDIX L.B 87 CHAPTER 2 ANALYTICAL REDUNDANCY METHODS 2.1
INTRODUCTION 93 2.2 PLANT AND FAILURE MODEIS 94 2.3 DESIGN REQUIREMENTS
97 2.4 METHODS OF SOLUTION 98 2.5 STOCHASTIC MODELING METHODS 102 2.5.1
SIMPLE TESTS 103 2.5.1.1 TESTS OF MEAN 104 2.5.1.2 TESTS OF COVARIANCE
105 V VI REAL TIME FAULT MONITORING OF INDUSTRIAL PROCESSES 2.5.1.3
TESTS OF WHITENESS 110 2.5.1.4 TWO-STAGE METHODS 111 2.5.2 THE MULTIPLE
MODEL (MM) METHOD 113 2.5.3 THE GENERALIZED LIKELIHOOD RATIO (GLR)
METHOD 116 2.5.3.1 ADDITIVE CHANGES 116 2.5.3.2 NON-ADDITIVE CHANGES 120
2.6 DETERMINISTIC METHODS 122 2.6.1 OBSERVER-BASED APPROACHES 122 2.6.2
PARITY SPACE APPROACH 129 2.7 ROBUST DETECTION METHODS 136 2.7.1 ROBUST
OBSERVER-BASED METHODS 136 2.7.2 PARITY RELATIONS FOR ROBUST RESIDUAL
GENERATION 149 2.8 APPLICATIONS 153 2.8.1 FAULT DETECTION IN A JET
ENGINE SYSTEM 153 2.8.2 APPLICATIONS IN TRANSPORTATION ENGINEERING 156
2.8.3 APPLICATIONS IN AEROSPACE ENGINEERING 161 2.8.4 APPLICATIONS IN
AUTOMOTIVE ENGINEERING 166 2.8.5 APPLICATIONS IN ROBOTICS 170 REFERENCES
172 CHAPTER 3 PARAMETER ESTIMATION METHODS FOR FAULT MONITORING 3.1
INTRODUCTION 179 3.2 PROCESS MODELING FOR FAULT DETECTION 182 3.3
PARAMETER ESTIMATION FOR FAULT DETECTION 186 3.3.1 RECURSIVE LEAST
SQUARES ALGORITHMS 187 3.3.2 FORGETTING FACTORS 191 3.3.3 IMPLEMENTATION
ISSUES 196 3.3.3.1 COVARIANCE INSTABILITY 196 3.3.3.2 COVARIANCE
SINGULARITY 200 3.3.3.3 SPEED - FAST ALGORITHMS 202 3.3.3.4 DATA WEIGHTS
SELECTION 205 3.3.5 ROBUSTNESS ISSUES 211 3.4 DECISION RULES 218 3.5
PRACTICAL EXAMPLES 224 3.5.1 EVAPORATOR FAULT DETECTION 224 3.5.2 GAS
TURBINE FAULT DETECTION AND DIAGNOSIS 228 3.5.3 FAULT DETECTION FOR
ELECTROMOTOR DRIVEN CENTRIFUGAL PUMPS 231 3.5.4 FAULT DETECTION IN POWER
SUBSTATIONS 237 3.5.5 FAULT DIAGNOSIS IN ROBOTIC SYSTEMS 242 3.6
ADDITIONAL REFERENCES 246 APPENDIX 3.A 247 APPENDIX 3.B 249 REFERENCES
250 TABLE OF CONTENTS CHAPTER4 AUTOMATIC EXPERT PROCESS FAULT DIAGNOSIS
AND SUPERVISION 4.1 INTRODUCTION 256 4.2 NATURE OF AUTOMATIC EXPERT
DIAGNOSTIC AND SUPERVISION SYSTEMS 257 4.2.1 EXPERT SYSTEMS FOR
AUTOMATIC PROCESS FAULT DIAGNOSIS 257 4.2.1.1 THE TERMINOLOGY OF
KNOWLEDGE ENGINEERING 257 4.2.1.2 TECHNIQUES FOR KNOWLEDGE ACQUISITION
261 4.2.1.3 EXPERT SYSTEM APPROACHES FOR AUTOMATIC PROCESS FAULT
DIAGNOSIS 271 4.2.1.4 HIGH-SPEED IMPLEMENTATIONS OF RULE-BASED
DIAGNOSTIC SYSTEMS 277 4.2.1.5 VALIDATING EXPERT SYSTEMS 283 4.2.2
EVENT-BASED ARCHITECTURE FOR REAL-TIME FAULT DIAGNOSIS 284 4.2.3 CURVE
ANALYSIS TECHNIQUES FOR REAL-TIME FAULT DIAGNOSIS 287 4.2.4 REAL-TIME
FAULT DETECTION USING PETRI NETS 291 4.2.5 FUZZY LOGIC THEORY IN
REAL-TIME PROCESS FAULT DIAGNOSIS 297 4.3 APPLICATION EXAMPLES 301 4.3.1
AUTOMATIC EXPERT DIAGNOSTIC SYSTEMS FOR NUCLEAR POWER PLANT (NPP)SAFETY
; 301 4.3.1.1 DIAGNOSTIC EXPERT SYSTEMS FOR NPP SAFETY 301 4.3.1.2 FUZZY
REASONING DIAGNOSIS FOR NPP SAFETY 305 4.3.2 AUTOMATIC EXPERT FAULT
DIAGNOSIS INCORPORATED IN A PROCESS SCADA SYSTEM 311 4.3.3 EXPERT
SYSTEMS FOR QUICK FAULT DIAGNOSIS IN THE MECHANICAL AND ELECTRICAL
SYSTEMS DOMAINS 328 4.3.4 AUTOMATIC EXPERT FAULT DIAGNOSIS FOR
MACHINE-TOOLS, ROBOTS AND CIM SYSTEMS 335 4.4 CONCLUSIONS 343 REFERENCES
34G APPENDIX 4.A A GENERIC HYBRID REASONING EXPERT DIAGNOSIS MODEL 352
APPENDIX 4.B BASIC DEFINITIONS OF PLACE/TRANSITION PETRI NETS AND THEIR
USE FOR ON-LINE PROCESS FAILURE DIAGNOSIS 360 APPENDIX 4.C ANALYTICAL
EXPRESSION FOR EXCEPTION USING FUZZY LOGIC AND ITS UTILIZATION FOR
ON-LINE EXCEPTIONAL EVENTS DIAGNOSIS 364 CHAPTER5 FAULT DIAGNOSIS USING
ARTIFICIAL NEURAL NETWORKS (ANNS) 5.1 INTRODUCTION 359 5.2 INTRODUCTION
TO NEURAL NETWORKS 372 5.3 CHARACTERISTICS OF ARTIFLCIAL NEURAL NETWORKS
374 5.4 ANN TOPOLOGIES AND LEARAING STRATEGIES 378 5.4.1 SUPERVISED
LEARNING ANNS 378 VM REAL TIME FAULT MONITORING OF INDUSTRIAL PROCESSES
5.4.1.1 MULTILAYER, FEEDFORWARD NETWORKS 379 5.4.1.2 RECURRENT
HIGH-ORDER NEURAL NETWORKS (RHONNS) 383 5.4.2 UNSUPERVISED LEARNING 385
5.4.2.1 ADAPTIVE RESONANCE ARCHITECTURES (ART) 385 5.4.2.2 KOHONEN MAPS
390 5.5 ANN-BASED FAULT DIAGNOSIS 392 5.5.1 CHOICEOF NEURAL TOPOLOGY 392
5.5.2 CHOICE OF OUTPUT FAULT VECTOR AND CLASSIFICATION PROCEDURE 393
5.5.3 TRAINING SAMPLE DESIGN 395 5.6 APPLICATION EXAMPLES 395 5.6.1
APPLICATIONS IN CHEMICAL ENGINEERING 396 5.6.2 APPLICATIONS IN CIM 401
5.6.3 POWER SYSTEMS DIAGNOSIS 404 5.6.4 NEURAL FOUR-PARAMETER CONTROLLER
407 5.6.5 APPLICATION OF NEURAL NETWORKS IN NUCLEAR POWER PLANTS
MONITORING 410 5.7 THE INTEGRATION OF NEURAL NETWORKS IN REAL-TIME
EXPERT SYSTEMS 419 5.7.1 THE AI COMPONENTS 421 REFERENCES 423 CHAPTER6
IN-TIME FAILURE PROGNOSIS AND FATIGUE LIFE PREDICTION OFSTRUCTURES 6.1
INTRODUCTION 430 6.2 RECENT NON-DESTRUCTIVE TESTING (NDT) AND EVALUATION
METHODS WITH APPLICATIONS 431 6.2.1 INTRODUCTION 431 6.2.2 THE MAIN
NON-DESTRUCTIVE TESTING METHODS 435 6.2.2.1 LIQUID PENETRANT INSPECTION
435 6.2.2.2 MAGNETIC PARTICLE INSPECTION 436 6.2.2.3 ELECTRICAL TEST
METHODS (EDDY CURRENT TESTING (ECT)) .438 6.2.2.4 ULTRASONIC TESTING 440
6.2.2.4 RADIOGRAPHY 449 6.2.2.5 ACOUSTIC EMISSION (AE) 451 6.2.2.6 OTHER
NON-DESTRUCTIVE INSPECTION TECHNIQUES 452 6.2.3 SIGNAL PROCESSING (SP)
FOR NDT 456 6.2.4 APPLICATIONS OF SP IN AUTOMATED NDT 459 6.2.5
CONCLUSIONS 461 6.3 REAL-TIME STRUCTURAL DAMAGE ASSESSMENT AND FATIGUE
LIFE PREDICTION METHODS 463 6.3.1 INTRODUCTION 463 6.3.2
PHENOMENOLOGICAL APPROACH FOR FATIGUE FAILURE PROGNOSIS 464 6.3.3
PROBABILISTIC FRACTURE MECHANICS APPROACH FOR FCG LIFE ESTIMATION 467
6.3.4 STOCHASTIC PROCESS APPROACH FOR FCG LIFE PREDICTION 478 6.3.5 TIME
SERIES ANALYSIS APPROACH FOR FCG PREDICTION 482 TABLE OF CONTENTS IX
6.3.6 INTELLIGENT SYSTEMS FOR IN-TIME STRUCTURAL DAMAGE ASSESSMENT 488
6.4 APPLICATION EXAMPLES 506 6.4.1 NUCLEAR REACTOR SAFETY ASSESSMENT
USING THE PROBABILISTIC FRACTURE MECHANICS METHOD 506 6.4.2 MARINE
STRUCTURES SAFETY ASSESSMENT USING THE PROBABILISTIC FRACTURE MECHANICS
METHOD 509 6.4.3 STRUCTURAL DAMAGE ASSESSMENT USING A CAUSAL NETWORK 519
REFERENCES 523 AUTHOR INDEX 529 SUBJECT INDEX 53 5
|
| any_adam_object | 1 |
| author | Pouliezos, A. D. 1951- |
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| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 670 - Manufacturing |
| dewey-raw | 670.42 |
| dewey-search | 670.42 |
| dewey-sort | 3670.42 |
| dewey-tens | 670 - Manufacturing |
| discipline | Informatik Werkstoffwissenschaften / Fertigungstechnik |
| format | Book |
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| id | DE-604.BV010831192 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:59:38Z |
| institution | BVB |
| isbn | 0792327373 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007238802 |
| oclc_num | 29702254 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-703 DE-83 |
| owner_facet | DE-91 DE-BY-TUM DE-703 DE-83 |
| physical | XXXIII, 542 S. graph. Darst. |
| publishDate | 1994 |
| publishDateSearch | 1994 |
| publishDateSort | 1994 |
| publisher | Kluwer |
| record_format | marc |
| series | International series on microprocessor based and intelligent systems engineering |
| series2 | International series on microprocessor based and intelligent systems engineering |
| spelling | Pouliezos, A. D. 1951- Verfasser (DE-588)172826586 aut Real time fault monitoring of industrial processes by A. D. Pouliezos and G. S. Stavrakakis Dordrecht [u.a.] Kluwer 1994 XXXIII, 542 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier International series on microprocessor based and intelligent systems engineering 12 Détection de défaut (ingénierie) ram Temps entre défaillances, Analyse des ram Fault location (Engineering) Process control Quality control Diagnosesystem (DE-588)4149458-1 gnd rswk-swf Fehlererkennung (DE-588)4133764-5 gnd rswk-swf Echtzeitverarbeitung (DE-588)4151002-1 gnd rswk-swf Prozessregelung (DE-588)4222919-4 gnd rswk-swf Prozessregelung (DE-588)4222919-4 s Fehlererkennung (DE-588)4133764-5 s Diagnosesystem (DE-588)4149458-1 s Echtzeitverarbeitung (DE-588)4151002-1 s DE-604 Stavrakakis, G. S. Sonstige oth International series on microprocessor based and intelligent systems engineering 12 (DE-604)BV023555365 12 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007238802&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Pouliezos, A. D. 1951- Real time fault monitoring of industrial processes International series on microprocessor based and intelligent systems engineering Détection de défaut (ingénierie) ram Temps entre défaillances, Analyse des ram Fault location (Engineering) Process control Quality control Diagnosesystem (DE-588)4149458-1 gnd Fehlererkennung (DE-588)4133764-5 gnd Echtzeitverarbeitung (DE-588)4151002-1 gnd Prozessregelung (DE-588)4222919-4 gnd |
| subject_GND | (DE-588)4149458-1 (DE-588)4133764-5 (DE-588)4151002-1 (DE-588)4222919-4 |
| title | Real time fault monitoring of industrial processes |
| title_auth | Real time fault monitoring of industrial processes |
| title_exact_search | Real time fault monitoring of industrial processes |
| title_full | Real time fault monitoring of industrial processes by A. D. Pouliezos and G. S. Stavrakakis |
| title_fullStr | Real time fault monitoring of industrial processes by A. D. Pouliezos and G. S. Stavrakakis |
| title_full_unstemmed | Real time fault monitoring of industrial processes by A. D. Pouliezos and G. S. Stavrakakis |
| title_short | Real time fault monitoring of industrial processes |
| title_sort | real time fault monitoring of industrial processes |
| topic | Détection de défaut (ingénierie) ram Temps entre défaillances, Analyse des ram Fault location (Engineering) Process control Quality control Diagnosesystem (DE-588)4149458-1 gnd Fehlererkennung (DE-588)4133764-5 gnd Echtzeitverarbeitung (DE-588)4151002-1 gnd Prozessregelung (DE-588)4222919-4 gnd |
| topic_facet | Détection de défaut (ingénierie) Temps entre défaillances, Analyse des Fault location (Engineering) Process control Quality control Diagnosesystem Fehlererkennung Echtzeitverarbeitung Prozessregelung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007238802&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023555365 |
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