Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques

By: Silvio Simani, Cesare Fantuzzi, Ron J. Patton

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Hardcover | 13 November 2002

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300 Pages

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24.77 x 16.51 x 2.54

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Safety in industrial process and production plants is a concern of rising importance, especially if people would be endangered by a catastrophic system failure. On the other hand, because the control devices which are now exploited to improve the overall performance of industrial processes include both sophisticated digital system design techniques and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of failure. As a direct consequence of this, control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions as early as possible. One of the most promising methods for solving this problem is the "analytical redundancy" approach, in which residual signals are obtained. The basic idea consists of using an accurate model of the system to mimic the real process behaviour. If a fault occurs, the residual signal, i.e., the difference between real system and model behaviours, can be used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification. The problem is treated in all its aspects covering: choice of model structure; parameter identification; residual generation; fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques will be of interest to researchers in control and fault identification. Industrial control engineers interested in applying the latest methods in fault diagnosis will benefit from the practical examples and case studies.

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Non-Fiction Engineering & Technology Technology in General Engineering in General Energy Technology & Engineering Electrical Engineering Mechanical Engineering & Materials Mechanical Engineering Production & Industrial Engineering Industrial Quality Control Mathematics Applied Mathematics

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Published: 21st October 2010

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Symbols and Abbreviations	p. xv
Introduction	p. 1
Nomenclature	p. 3
Fault Detection and Identification Methods based on Analytical Redundancy	p. 5
Model-based Fault Detection Methods	p. 7
Model Uncertainty and Fault Detection	p. 8
The Robustness Problem in Fault Detection	p. 9
System Identification for Robust FDI	p. 11
Fault Identification Methods	p. 12
Report on FDI Applications	p. 13
Outline of the Book	p. 16
Summary	p. 18
Model-based Fault Diagnosis Techniques	p. 19
Introduction	p. 19
Model-based FDI Techniques	p. 20
Modelling of Faulty Systems	p. 21
Residual Generator General Structure	p. 28
Residual Generation Techniques	p. 31
Residual Generation via Parameter Estimation	p. 32
Observer-based Approaches	p. 35
Fault Detection with Parity Equations	p. 40
Change Detection and Symptom Evaluation	p. 44
The Residual Generation Problem	p. 45
Fault Diagnosis Technique Integration	p. 51
Fuzzy Logic for Residual Generation	p. 51
Neural Networks in Fault Diagnosis	p. 53
Neuro-fuzzy Approaches to FDI	p. 54
Structure Identification of NF Models	p. 56
NF Residual Generation Scheme for FDI	p. 57
Summary	p. 59
System Identification for Fault Diagnosis	p. 61
Introduction	p. 61
Models for Linear Systems	p. 62
Parameter Estimation Methods	p. 64
System Identification in Noiseless Environment	p. 65
System Identification in Noisy Environment	p. 68
The Frisch Scheme in the MIMO Case	p. 73
Models for Non-linear Dynamic Systems	p. 75
Piecewise Affine Model	p. 75
Model Continuity and Domain Partitioning	p. 79
Local Affine Model Identification	p. 82
Multiple-Model Identification	p. 85
Fuzzy Modelling and Identification	p. 89
Fuzzy Multiple Inference Identification	p. 90
Takagi-Sugeno Multiple-Model Paradigm	p. 92
Fuzzy Clustering for Fuzzy Identification	p. 95
Product Space Clustering and Fuzzy Model Identification	p. 100
Non-linear Regression Problem and Black-Box Models	p. 103
Fuzzy Model Identification From Clusters	p. 107
Conclusion	p. 112
Residual Generation, Fault Diagnosis and Identification	p. 115
Introduction	p. 115
Output Observers for Robust Residual Generation	p. 116
Unknown Input Observer	p. 119
UIO Mathematical Description	p. 120
UIO Design Procedure	p. 122
FDI Schemes Based on UIO and Output Observers	p. 122
Sliding Mode Observers for FDI	p. 127
Sliding Mode Observers	p. 128
Kalman Filtering and FDI from Noisy Measurements	p. 130
Residual Robustness to Disturbances	p. 131
Disturbance Distribution Matrix Estimation	p. 132
Additive Non-linear Disturbance and Noise	p. 133
Model Complexity Reduction	p. 133
Parameter Uncertainty	p. 134
Distribution Matrix Low Rank Approximation	p. 135
Model Estimation with Bounded Uncertainty	p. 135
Disturbance Vector and Disturbance Matrix Estimation	p. 136
Distribution Matrix Optimisation	p. 139
Disturbance Distribution Matrix Identification	p. 139
Residual Generation via Parameter Estimation	p. 141
Residual Generation via Fuzzy Models	p. 142
FDI Using Neural Networks	p. 143
Neural Network Basics	p. 145
Fault Diagnosis of an Industrial Plant at Different Operating Points Using Neural Networks	p. 147
Operating Point Detection and Fault Diagnosis	p. 147
FDI Method Development	p. 149
Neuro-fuzzy in FDI	p. 150
Methods of Neuro-fuzzy Integration	p. 151
Neuro-fuzzy Networks	p. 152
Residual Generation Using Neuro-fuzzy Models	p. 154
Neuro-fuzzy-based Residual Evaluation	p. 155
Summary	p. 156
Fault Diagnosis Application Studies	p. 157
Introduction	p. 157
Physical Background and Modelling Aspects of an Industrial Gas Turbine	p. 158
Gas Turbine Model Description	p. 158
Identification and FDI of a Single Shaft Industrial Gas Turbinel68
System Identification	p. 169
FDI Using Dynamic Observers	p. 176
FDI Using Kalman Filters	p. 183
Fuzzy System Identification and FDI	p. 189
Sensor Fault Identification Using Neural Networks	p. 191
Multiple Working Conditions FDI Using Neural Networks	p. 196
FDI Method Development	p. 196
Multiple Operating Point Simulation Results	p. 197
Identification and FDI of Double Shaft Industrial Gas Turbine	p. 199
Process Description	p. 199
System Identification	p. 201
FDI Using Unknown Input Observers	p. 203
FDI Using Kalman Filters	p. 208
Disturbance Decoupled Observers for Sensor FDI	p. 209
Fuzzy Models for Fault Diagnosis	p. 210
Modelling and FDI of a Turbine Prototype	p. 214
System Modelling and Identification	p. 215
Turbine FDI Using Output Observers	p. 220
Case 1: Compressor Failure (Component Fault)	p. 221
Case 2: Fault Diagnosis of the Output Sensor	p. 223
Case 3: Turbine Damage (Turbine Component Fault)	p. 227
Case 4: Actuator Fault (Controller Malfunctioning)	p. 228
FDI in Noisy Environment Using Kalman Filters	p. 233
Fault Isolation	p. 235
Minimal Detectable Faults	p. 239
FDI with Eigenstructure Assignment	p. 242
Robust Fault Diagnosis of the Industrial Process	p. 243
Robust Residual Generation Problem	p. 247
Summary	p. 249
Concluding Remarks	p. 251
Suggestions for Future Work	p. 253
Frequency Domain Residual Generation	p. 253
Adaptive Residual Generators	p. 255
Integration of Identification, FDI and Control	p. 256
Fault Identification	p. 256
Fault Diagnosis of Non-Linear Dynamic Systems	p. 258
References	p. 261
Index	p. 279
Table of Contents provided by Publisher. All Rights Reserved.

Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques

At a Glance

Hardcover

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