Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes

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Springer Science & Business Media, Jun 24, 2008 - Technology & Engineering - 206 pages
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An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is re?ected in plenty of papers on fault diagnosis in many control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon model basedfault diagnosis has been accumulated through scienti?c literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available.
 

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Contents

Introduction
1
11 Organization of the Book
3
Modelling Issue in Fault Diagnosis
7
21 Problem of Fault Detection and Fault Diagnosis
8
22 ModelsUsedinFaultDiagnosis
11
221 Parameter Estimation
12
223 Observers
13
224 Neural Networks
14
516 System Identification Based on Real Process Data
92
517 Convergence of Network States
93
52 Stability Analysis Layers Networks with Two Hidden
96
521 Second Method of Lyapunov
97
522 First Method of Lyapunov
106
53 Stability Analysis Cascade Networks
110
54 Summary
111
Optimum Experimental Design for Locally Recurrent Networks
113

225 Fuzzy Logic
15
23 Neural Networks in Fault Diagnosis
16
232 Radial Basis Function Network
18
233 Kohonen Network
20
234 Model Based Approaches
21
235 Knowledge Based Approaches
23
24 Evaluation of the FDI System
24
25 Summary
26
Locally Recurrent Neural Networks
28
31 Neural Networks with External Dynamics
30
32 Fully Recurrent Networks
31
33 Partially Recurrent Networks
32
34 StateSpace Networks
34
35 Locally Recurrent Networks
36
351 Model with the IIR Filter
40
352 Analysis of Equilibrium Points
43
353 Controllability and Observability
47
354 Dynamic Neural Network
49
36 Training of the Network
52
362 Adaptive Random Search
53
363 Simultaneous Perturbation Stochastic Approximation
55
364 Comparison of Training Algorithms
57
37 Summary
62
Approximation Abilities of Locally Recurrent Networks
65
41 Modelling Properties of the Dynamic Neuron
66
411 StateSpace Representation of the Network
67
43 Approximation Abilities
68
44 Process Modelling
72
45 Summary
74
Stability and Stabilization of Locally Recurrent Networks
76
51 Stability Analysis Networks with One Hidden Layer
78
511 Gradient Projection
82
513 Strong Convergence
86
514 Numerical Complexity
88
515 Pole Placement
90
61 Optimal Sequence Selection Problem in Question
114
612 Sequence Quality Measure
115
613 Experimental Design
116
62 Characterization of Optimal Solutions
117
63 Selection of Training Sequences
118
64 Illustrative Example
119
642 Results
120
65 Summary
121
Decision Making in Fault Detection
123
71 Simple Thresholding
124
72 Density Estimation
126
722 Density Estimation
127
723 Threshold Calculating A Single Neuron
130
724 Threshold Calculating A TwoLayer Network
131
73 Robust Fault Diagnosis
132
731 Adaptive Thresholds
133
732 Fuzzy Threshold Adaptation
135
74 Summary
140
Industrial Applications
141
811 Instrumentation Faults
143
813 Experiments
146
814 Final Remarks
160
82 Fluid Catalytic Cracking Fault Detection
161
821 Process Modelling
163
822 Faulty Scenarios
164
824 Robust Fault Diagnosis
168
825 Final Remarks
172
831 AMIRA DR300 Laboratory System
173
832 Motor Modelling
176
834 Robust Fault Diagnosis
181
835 Final Remarks
182
Concluding Remarks and Further Research Directions
187
References
190
Index
203
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