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E-grāmata: Intelligent Fault Diagnosis and Accommodation Control

, (National University of Singapore), , (National University of Singapore)
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Intelligent Fault Diagnosis and Accommodation ControlPalielināt
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Control systems include many components, such as transducers, sensors, actuators and mechanical parts. These components are required to be operated under some specific conditions. However, due to prolonged operations or harsh operating environment, the properties of these devices may degrade to an unacceptable level, causing more regular fault occurrences. It is therefore necessary to diagnose faults and provide the fault-accommodation control which compensates for the fault of the component by substituting a configuration of redundant elements so that the system continues to operate satisfactorily.

In this book, we present a result of several years of work in the area of fault diagnosis and fault-accommodation control. It aims at information estimate methods when faults occur. The book uses the model built from the plant or process, to detect and isolate failures, in contrast to traditional hardware or statistical technologies dealing with failures. It presents model-based learning and design technologies for fault detection, isolation and identification as well as fault-tolerant control. These models are also used to analyse the fault detectability and isolability conditions and discuss the stability of the closed-loop system. It is intended to report new technologies in the area of fault diagnosis, covering fault analysis and control strategies of design for various applications. The book addresses four main schemes: modelling of actuator or sensor faults; fault detection and isolation; fault identification, and fault reconfiguration (accommodation) control. It also covers application issues in the monitoring control of actuators, providing several interesting case studies for more application-oriented readers.

Preface ix
Acknowledgments xi
Authors xiii
1 Introduction
1(6)
2 Fault Types and Modeling
7(12)
2.1 Problem backgrounds
7(1)
2.2 Fault types
8(3)
2.3 Fault modeling
11(7)
2.4 Conclusions
18(1)
3 Model-Based Fault Detection
19(18)
3.1 Model-based approaches to fault detection
19(15)
3.1.1 Parameter estimation approach
20(2)
3.1.2 Observer-based approach
22(1)
3.1.2.1 Fault detection against actuator faults
23(2)
3.1.2.2 Detectability issue
25(3)
3.1.2.3 Extension of fault detection to the more general MIMO case
28(1)
3.1.2.4 Fault detection against both actuator and sensor faults
28(3)
3.1.2.5 Detectability analysis
31(2)
3.1.2.6 Simulation example
33(1)
3.2 Conclusions
34(3)
4 Model-Based Fault Isolation
37(14)
4.1 Model-based approaches to fault isolation
37(11)
4.1.1 Directional residual scheme
37(2)
4.1.2 Dedicated observer scheme
39(3)
4.1.3 Generalized observer scheme
42(3)
4.1.3.1 Thresholds of fault isolation
45(1)
4.1.3.2 Fault isolability analysis
46(2)
4.2 Relationship between fault detection and fault isolation
48(1)
4.3 Conclusions
49(2)
5 Model-Based Fault Identification
51(26)
5.1 Neural network-based fault identification
51(23)
5.1.1 Actuator fault identification with full-state measurement
52(2)
5.1.2 Actuator fault identification with partial-state measurements
54(13)
5.1.3 Sensor and actuator fault identification with partial-state measurements
67(5)
5.1.4 Simulation example
72(2)
5.2 Conclusions
74(3)
6 Model-Based Fault Accommodation Control
77(30)
6.1 Fault accommodation problem
78(1)
6.2 Accommodation control of full state feedback systems
78(13)
6.2.1 Fault detection of full state feedback systems
79(2)
6.2.2 Model-based accommodation control of full state feedback systems
81(9)
6.2.3 Simulation
90(1)
6.3 Accommodation control of output feedback systems
91(13)
6.3.1 Fault detection of output feedback systems
94(1)
6.3.2 Fault isolation of output feedback systems
95(2)
6.3.3 Fault identification of output feedback systems
97(1)
6.3.4 Model-based accommodation control of output feedback systems
98(1)
6.3.4.1 Control design without fault occurrence
98(3)
6.3.4.2 Control design after fault detection
101(1)
6.3.4.3 Simulation
102(2)
6.4 Conclusions
104(3)
7 Model-Based Fault Accommodation Control of Robotic Systems
107(24)
7.1 Problem statements
108(2)
7.2 Fault diagnosis scheme
110(3)
7.2.1 Fault detection
110(1)
7.2.2 Fault isolation
111(2)
7.3 Fault accommodation scheme
113(9)
7.3.1 Normal controller before fault detection
114(1)
7.3.2 Accommodation control of system failures (T1 < t ≤ T0)
115(4)
7.3.3 Accommodation control after fault isolation (t 7le; T1)
119(3)
7.4 Simulation example
122(2)
7.5 Conclusions
124(7)
8 Fault Diagnosis and Fault Accommodation Control for Multi-Agent Systems
131(18)
8.1 Consensus problem
131(1)
8.2 Graph theory
132(1)
8.3 Model-based fault diagnosis of MASs
133(2)
8.4 Model-based passive fault accommodation control of MASs
135(6)
8.5 Model-based active fault accommodation control of MASs
141(1)
8.5.1 Control design before fault occurrence
141(1)
8.5.2 Control design after fault occurrence
141(1)
8.6 Simulation
142(3)
8.7 Conclusions
145(4)
9 Case Studies
149(94)
9.1 Case Study 1: Fault simulator based on hardware-in-the-loop technique
150(12)
9.1.1 Induction motor model
152(1)
9.1.2 Fault cases of an induction motor
153(2)
9.1.3 Design of hardware-in-the-loop simulator
155(2)
9.1.4 Experimental results
157(2)
9.1.5 Some comments
159(3)
9.2 Case Study 2: GPS spoofing detection based on unmanned aerial vehicle model
162(17)
9.2.1 Related work
163(1)
9.2.2 Overview of the proposed control strategy
164(1)
9.2.3 UAV model
164(3)
9.2.4 GPS spoofing
167(8)
9.2.5 GPS spoofing detection scheme
175(1)
9.2.6 Simulation study
176(1)
9.2.7 Some comments
177(2)
9.3 Case Study 3: Failure detection of an electrical machine
179(14)
9.3.1 Model of induction motor
180(2)
9.3.2 Intelligent fault monitoring scheme
182(2)
9.3.3 Intelligent fault isolation scheme
184(4)
9.3.4 Simulation test
188(2)
9.3.5 Some comments
190(3)
9.4 Case Study 4: Fault-tolerance control of a linear drive
193(16)
9.4.1 Linear drive system and control objective
196(1)
9.4.2 Softcomputing background
197(1)
9.4.3 Softcomputing based fault-tolerant control of linear drives
198(1)
9.4.3.1 Normal controller for healthy system
198(2)
9.4.3.2 On-line monitoring
200(1)
9.4.3.3 Fault identification
201(1)
9.4.3.4 Fault-tolerant control
202(2)
9.4.4 Experimental results
204(5)
9.4.5 Some comments
209(1)
9.5 Case Study 5: Approach towards sensor placement, selection and fusion for real-time condition monitoring of precision machines
209(34)
9.5.1 Proposed framework for condition monitoring
216(1)
9.5.1.1 Problem formulation
217(1)
9.5.1.2 Preparation and calibration
217(3)
9.5.1.3 Framework
220(4)
9.5.1.4 Scalability
224(1)
9.5.1.5 Low frequency monitor
224(1)
9.5.2 Case study: results and discussion
224(2)
9.5.2.1 Data collection and calibration
226(10)
9.5.2.2 Real-time condition monitoring
236(5)
9.5.3 Some comments
241(2)
Bibliography 243(16)
Index 259
Huang Sunan is a Senior Research Scientist in Temasek Laboratories, National University of Singapore. His research interests include fault diagnosis and accommodation, adaptive control, neural network control, and unmanned systems.

Tan Kok Kiong is currently a Professor with the Department of Electrical and Computer Engineering, National University of Singapore. His current research interests are in the areas of advanced control and auto-tuning, precision instrumentation and control, and general industrial automation.

Er Poi Voon is a Senior Research Scientist in Temasek Laboratories, National University of Singapore. His research interests include error compensation of fault diagnosis and accommodation, adaptive control, neural network control and unmanned systems.

Lee Tong Heng is a Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS); and also a Professor in the NUS Graduate School, NUS NGS. Dr. Lee's research interests are in the areas of adaptive systems, knowledge-based control, intelligent mechatronics and computational intelligence. He currently holds Associate Editor appointments in the IEEE Transactions in Systems, Man and Cybernetics; Control Engineering Practice (an IFAC journal); and the International Journal of Systems Science (Taylor and Francis, London). In addition, he is the Deputy Editor-in-Chief of IFAC Mechatronics journal.
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