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E-raamat: Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and Tools

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Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms and ToolsSuurem pilt
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This book gives readers a framework of model-based FDI techniques, helping them to become familiar with the basic ideas and schemes in a systematic way. Examples and benchmarks provide a means of practising the ideas and judging the methods described.

Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: • new material on fault isolation and identification and alarm management; • extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; • addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and • enhanced discussion of residual evaluation which now deals with stochastic processes. Model-based Fault Diagnosis Techniques will interest academic researchers working in fault identification and diagnosis and as a text it is suitable for graduate students in a formal university-based course or as a self-study aid for practising engineers working with automatic control or mechatronic systems from backgrounds as diverse as chemical process and power engineering.
Part I Introduction, Basic Concepts and Preliminaries
1 Introduction
3(10)
1.1 Basic Concepts of Fault Diagnosis Technique
4(4)
1.2 Historical Development and Some Relevant Issues
8(2)
1.3 Notes and References
10(3)
2 Basic Ideas, Major Issues and Tools in the Observer-Based FDI Framework
13(8)
2.1 On the Observer-Based Residual Generator Framework
13(1)
2.2 Unknown Input Decoupling and Fault Isolation Issues
14(1)
2.3 Robustness Issues in the Observer-Based FDI Framework
15(1)
2.4 On the Parity Space FDI Framework
16(1)
2.5 Residual Evaluation and Threshold Computation
17(1)
2.6 FDI System Synthesis and Design
18(1)
2.7 Notes and References
18(3)
3 Modelling of Technical Systems
21(30)
3.1 Description of Nominal System Behavior
22(1)
3.2 Coprime Factorization Technique
23(2)
3.3 Representations of Systems with Disturbances
25(1)
3.4 Representations of System Models with Model Uncertainties
25(2)
3.5 Modelling of Faults
27(2)
3.6 Modelling of Faults in Closed-Loop Feedback Control Systems
29(2)
3.7 Case Study and Application Examples
31(18)
3.7.1 Speed Control of a DC Motor
31(3)
3.7.2 Inverted Pendulum Control System
34(4)
3.7.3 Three-Tank System
38(3)
3.7.4 Vehicle Lateral Dynamic System
41(5)
3.7.5 Continuous Stirred Tank Heater
46(3)
3.8 Notes and References
49(2)
4 Fault Detectability, Isolability and Identifiability
51(20)
4.1 Fault Detectability
51(5)
4.2 Excitations and Detection of Multiplicative Faults
56(1)
4.3 Fault Isolability
57(8)
4.3.1 Concept of System Fault Isolability
57(1)
4.3.2 Fault Isolability Conditions
58(7)
4.4 Fault Identifiability
65(2)
4.5 Notes and References
67(4)
Part II Residual Generation
5 Basic Residual Generation Methods
71(46)
5.1 Analytical Redundancy
72(3)
5.2 Residuals and Parameterization of Residual Generators
75(3)
5.3 Issues Related to Residual Generator Design and Implementation
78(1)
5.4 Fault Detection Filter
79(2)
5.5 Diagnostic Observer Scheme
81(17)
5.5.1 Construction of Diagnostic Observer-Based Residual Generators
81(1)
5.5.2 Characterization of Solutions
82(9)
5.5.3 A Numerical Approach
91(5)
5.5.4 An Algebraic Approach
96(2)
5.6 Parity Space Approach
98(5)
5.6.1 Construction of Parity Relation Based Residual Generators
98(3)
5.6.2 Characterization of Parity Space
101(1)
5.6.3 Examples
102(1)
5.7 Interconnections, Comparison and Some Remarks
103(12)
5.7.1 Parity Space Approach and Diagnostic Observer
104(4)
5.7.2 Diagnostic Observer and Residual Generator of General Form
108(3)
5.7.3 Applications of the Interconnections and Some Remarks
111(2)
5.7.4 Examples
113(2)
5.8 Notes and References
115(2)
6 Perfect Unknown Input Decoupling
117(46)
6.1 Problem Formulation
117(2)
6.2 Existence Conditions of PUIDP
119(7)
6.2.1 A General Existence Condition
119(1)
6.2.2 A Check Condition via Rosenbrock System Matrix
120(2)
6.2.3 An Algebraic Check Condition
122(4)
6.3 A Frequency Domain Approach
126(2)
6.4 UIFDF Design
128(13)
6.4.1 The Eigenstructure Assignment Approach
129(4)
6.4.2 Geometric Approach
133(8)
6.5 UIDO Design
141(11)
6.5.1 An Algebraic Approach
141(1)
6.5.2 Unknown Input Observer Approach
142(4)
6.5.3 A Matrix Pencil Approach to the UIDO Design
146(4)
6.5.4 A Numerical Approach to the UIDO Design
150(2)
6.6 Unknown Input Parity Space Approach
152(1)
6.7 An Alternative Scheme---Null Matrix Approach
153(1)
6.8 Discussion
154(1)
6.9 Minimum Order Residual Generator
154(6)
6.9.1 Minimum Order Residual Generator Design by Geometric Approach
155(2)
6.9.2 An Alternative Solution
157(3)
6.10 Notes and References
160(3)
7 Residual Generation with Enhanced Robustness Against Unknown Inputs
163(86)
7.1 Mathematical and Control Theoretical Preliminaries
164(13)
7.1.1 Signal Norms
165(2)
7.1.2 System Norms
167(2)
7.1.3 Computation of H2 and H∞ Norms
169(2)
7.1.4 Singular Value Decomposition (SVD)
171(1)
7.1.5 Co-Inner--Outer Factorization
171(3)
7.1.6 Model Matching Problem
174(1)
7.1.7 Essentials of the LMI Technique
175(2)
7.2 Kalman Filter Based Residual Generation
177(3)
7.3 Robustness, Fault Sensitivity and Performance Indices
180(4)
7.3.1 Robustness and Sensitivity
181(1)
7.3.2 Performance Indices: Robustness vs. Sensitivity
182(1)
7.3.3 Relations Between the Performance Indices
182(2)
7.4 Optimal Selection of Parity Matrices and Vectors
184(12)
7.4.1 Sf, + / Rd as Performance Index
184(4)
7.4.2 Sf, - / Rd as Performance Index
188(2)
7.4.3 Js - R as Performance Index
190(2)
7.4.4 Optimization Performance and System Order
192(1)
7.4.5 Summary and Some Remarks
193(3)
7.5 H∞ Optimal Fault Identification Scheme
196(2)
7.6 H2/H2 Design of Residual Generators
198(3)
7.7 Relationship Between H2/H2 Design and Optimal Selection of Parity Vectors
201(7)
7.8 LMI Aided Design of FDF
208(22)
7.8.1 H2 to H2 Trade-off Design of FDF
208(5)
7.8.2 On the H- Index
213(8)
7.8.3 H2 to H- Trade-off Design of FDF
221(2)
7.8.4 H∞ to H- Trade-off Design of FDF
223(2)
7.8.5 H∞ to H- Trade-off Design of FDF in a Finite Frequency Range
225(1)
7.8.6 An Alternative H∞ to H- Trade-off Design of FDF
226(3)
7.8.7 A Brief Summary and Discussion
229(1)
7.9 The Unified Solution
230(8)
7.9.1 Hi/H∞ Index and Problem Formulation
230(1)
7.9.2 Hi/H∞ Optimal Design of FDF: The Standard Form
231(3)
7.9.3 Discrete-Time Version of the Unified Solution
234(1)
7.9.4 A Generalized Interpretation
235(3)
7.10 The General Form of the Unified Solution
238(6)
7.10.1 Extended CIOF
239(2)
7.10.2 Generalization of the Unified Solution
241(3)
7.11 Notes and References
244(5)
8 Residual Generation with Enhanced Robustness Against Model Uncertainties
249(36)
8.1 Preliminaries
250(2)
8.1.1 LMI Aided Computation for System Bounds
250(1)
8.1.2 Stability of Stochastically Uncertain Systems
251(1)
8.2 Transforming Model Uncertainties into Unknown Inputs
252(2)
8.3 Reference Model Based Strategies
254(7)
8.3.1 The Basic Idea
254(1)
8.3.2 A Reference Model Based Solution for Systems with Norm-Bounded Uncertainties
254(7)
8.4 Residual Generation for Systems with Polytopic Uncertainties
261(6)
8.4.1 The Reference Model Scheme Based Scheme
262(4)
8.4.2 H- to H∞ Design Formulation
266(1)
8.5 Residual Generation for Stochastically Uncertain Systems
267(13)
8.5.1 System Dynamics and Statistical Properties
268(1)
8.5.2 Basic Idea and Problem Formulation
269(1)
8.5.3 An LMI Solution
270(7)
8.5.4 An Alternative Approach
277(3)
8.6 Notes and References
280(5)
Part III Residual Evaluation and Threshold Computation
9 Norm-Based Residual Evaluation and Threshold Computation
285(30)
9.1 Preliminaries
286(2)
9.2 Basic Concepts
288(1)
9.3 Some Standard Evaluation Functions
289(2)
9.4 Basic Ideas of Threshold Setting and Problem Formulation
291(5)
9.4.1 Dynamics of the Residual Generator
292(1)
9.4.2 Definitions of Thresholds and Problem Formulation
293(3)
9.5 Computation of Jth, RMS,2
296(6)
9.5.1 Computation of Jth, RMS,2 for the Systems with the Norm-Bounded Uncertainty
296(4)
9.5.2 Computation of Jth, RMS,2 for the Systems with the Polytopic Uncertainty
300(2)
9.6 Computation of Jth, peak, peak
302(4)
9.6.1 Computation of Jth, peak, peak for the Systems with the Norm-Bounded Uncertainty
302(3)
9.6.2 Computation of Jth, peak, peak for the Systems with the Polytopic Uncertainty
305(1)
9.7 Computation of Jth, peak,2
306(4)
9.7.1 Computation of Jth, peak,2 for the Systems with the Norm-Bounded Uncertainty
306(3)
9.7.2 Computation of Jth, peak,2 for the Systems with the Polytopic Uncertainty
309(1)
9.8 Threshold Generator
310(2)
9.9 Notes and References
312(3)
10 Statistical Methods Based Residual Evaluation and Threshold Setting
315(24)
10.1 Introduction
315(1)
10.2 Elementary Statistical Methods
315(10)
10.2.1 Basic Hypothesis Test
315(3)
10.2.2 Likelihood Ratio and Generalized Likelihood Ratio
318(2)
10.2.3 Vector-Valued GLR
320(2)
10.2.4 Detection of Change in Variance
322(1)
10.2.5 Aspects of On-Line Realization
323(2)
10.3 Criteria for Threshold Computation
325(3)
10.3.1 The Neyman-Pearson Criterion
325(1)
10.3.2 Maximum a Posteriori Probability (MAP) Criterion
326(1)
10.3.3 Bayes' Criterion
327(1)
10.3.4 Some Remarks
328(1)
10.4 Application of GLR Testing Methods
328(9)
10.4.1 Kalman Filter Based Fault Detection
329(6)
10.4.2 Parity Space Based Fault Detection
335(2)
10.5 Notes and References
337(2)
11 Integration of Norm-Based and Statistical Methods
339(30)
11.1 Residual Evaluation in Stochastic Systems with Deterministic Disturbances
339(7)
11.1.1 Residual Generation
340(1)
11.1.2 Problem Formulation
341(1)
11.1.3 GLR Solutions
342(3)
11.1.4 An Example
345(1)
11.2 Residual Evaluation Scheme for Stochastically Uncertain Systems
346(11)
11.2.1 Problem Formulation
347(1)
11.2.2 Solution and Design Algorithms
348(9)
11.3 Probabilistic Robustness Technique Aided Threshold Computation
357(9)
11.3.1 Problem Formulation
357(2)
11.3.2 Outline of the Basic Idea
359(1)
11.3.3 LMIs Used for the Solutions
360(1)
11.3.4 Problem Solutions in the Probabilistic Framework
361(2)
11.3.5 An Application Example
363(2)
11.3.6 Concluding Remarks
365(1)
11.4 Notes and References
366(3)
Part IV Fault Detection, Isolation and Identification Schemes
12 Integrated Design of Fault Detection Systems
369(36)
12.1 FAR and FDR
370(3)
12.2 Maximization of Fault Detectability by a Given FAR
373(13)
12.2.1 Problem Formulation
373(1)
12.2.2 Essential Form of the Solution
374(2)
12.2.3 A General Solution
376(3)
12.2.4 Interconnections and Comparison
379(4)
12.2.5 Examples
383(3)
12.3 Minimizing False Alarm Number by a Given FDR
386(12)
12.3.1 Problem Formulation
387(1)
12.3.2 Essential Form of the Solution
388(2)
12.3.3 The State Space Form
390(2)
12.3.4 The Extended Form
392(1)
12.3.5 Interpretation of the Solutions and Discussion
393(4)
12.3.6 An Example
397(1)
12.4 On the Application to Stochastic Systems
398(4)
12.4.1 Application to Maximizing FDR by a Given FAR
399(1)
12.4.2 Application to Minimizing FAR by a Given FDR
400(1)
12.4.3 Equivalence Between the Kalman Filter Scheme and the Unified Solution
400(2)
12.5 Notes and References
402(3)
13 Fault Isolation Schemes
405(36)
13.1 Essentials
406(6)
13.1.1 Existence Conditions for a Perfect Fault Isolation
406(2)
13.1.2 PFIs and Unknown Input Decoupling
408(3)
13.1.3 PFIs with Unknown Input Decoupling (PFIUID)
411(1)
13.2 Fault Isolation Filter Design
412(15)
13.2.1 A Design Approach Based on the Duality to Decoupling Control
413(3)
13.2.2 The Geometric Approach
416(2)
13.2.3 A Generalized Design Approach
418(9)
13.3 An Algebraic Approach to Fault Isolation
427(4)
13.4 Fault Isolation Using a Bank of Residual Generators
431(8)
13.4.1 The Dedicated Observer Scheme (DOS)
432(4)
13.4.2 The Generalized Observer Scheme (GOS)
436(3)
13.5 Notes and References
439(2)
14 Fault Identification Schemes
441(30)
14.1 Fault Identification Filter Schemes and Perfect Fault Identification
442(7)
14.1.1 Fault Detection Filters and Existence Conditions
442(4)
14.1.2 FIF Design with Measurement Derivatives
446(3)
14.2 On the Optimal FIF Design
449(7)
14.2.1 Problem Formulation and Solution Study
449(2)
14.2.2 Study on the Role of the Weighting Matrix
451(5)
14.3 Approaches to the Design of FIF
456(5)
14.3.1 A General Fault Identification Scheme
457(1)
14.3.2 An Alternative Scheme
457(1)
14.3.3 Identification of the Size of a Fault
458(2)
14.3.4 Fault Identification in a Finite Frequency Range
460(1)
14.4 Fault Identification Using an Augmented Observer
461(2)
14.5 An Algebraic Fault Identification Scheme
463(1)
14.6 Adaptive Observer-Based Fault Identification
464(4)
14.6.1 Problem Formulation
464(1)
14.6.2 The Adaptive Observer Scheme
465(3)
14.7 Notes and References
468(3)
15 Fault Diagnosis in Feedback Control Systems and Fault-Tolerant Architecture
471(20)
15.1 Plant and Control Loop Models, Controller and Observer Parameterizations
472(6)
15.1.1 Plant and Control Loop Models
472(1)
15.1.2 Parameterization of Stabilizing Controllers, Observers, and an Alternative Formulation of Controller Design
473(2)
15.1.3 Observer and Residual Generator Based Realizations of Youla Parameterization
475(1)
15.1.4 Residual Generation Based Formulation of Controller Design Problem
476(2)
15.2 Residual Extraction in the Standard Feedback Control Loop and a Fault Detection Scheme
478(3)
15.2.1 Signals at the Access Points in the Control Loop
478(1)
15.2.2 A Fault Detection Scheme Based on Extraction of Residual Signals
479(2)
15.3 2-DOF Control Structures and Residual Access
481(4)
15.3.1 The Standard 2-DOF Control Structures
481(2)
15.3.2 An Alternative 2-DOF Control Structure with Residual Access
483(2)
15.4 On Residual Access in the IMC and Residual Generator Based Control Structures
485(3)
15.4.1 An Extended IMC Structure with an Integrated Residual Access
485(2)
15.4.2 A Residual Generator Based Feedback Control Loop
487(1)
15.5 Notes and References
488(3)
References 491(8)
Index 499
Steven X. Ding began investigating issues concerned with model-based fault detection and identification in 1986 during his PhD. Following a three-year stay in industry, in which he gained experience with the application of FDI techniques in real technical processes, he returned to academia and during the last 17 years has become a university professor and institute head. He has been involved with numerous national and international research grants and industrial projects in the development of advanced FDI methods and their application in different sectors of industry. He teaches fault diagnosis and fault-tolerant systems to Masters students and advanced FDI methods to PhD students at the University of Duisburg-Essen. He has also guest-lectured and taught courses on these subjects at other universities and research institutes. Professor Ding has published more than 80 journal and 130 conference papers associated with the field of FDI.

 
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