Intelligent Fault Diagnosis and Accommodation Control

275 Pages·2020·9.074 MB·\275

by  Sunan Huang (Author), Kok Kiong Tan (Author), Poi Voon Er (Author), Tong Heng Lee (Author)

AI
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INTELLIGENT FAULT DIAGNOSIS E

AL
AND ACCOMMODATION CONTROL
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G
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Control systems include many components, such as transducers, sensors, E
M
N
actuators and mechanical parts. These components are required to be
operated under some specific conditions. However, due to prolonged MT
operations or harsh operating environment, the properties of these O F
devices may degrade to an unacceptable level, causing more regular fault DA
U
occurrences. It is therefore necessary to diagnose faults and provide the A
fault-accommodation control which compensates for the fault of the TL
T
component by substituting a configuration of redundant elements so I
O
that the system continues to operate satisfactorily. D
N
I
In Intelligent Fault Diagnosis and Accommodation Control, we present A
C
the results of several years of work in the area of fault diagnosis and G
O
fault-accommodation control. It aims at information estimate methods N
N AUTOMATION AND CONTROL ENGINEERING
when faults occur. The book uses the model built from the plant or O
T
process, to detect and isolate failures, in contrast to traditional hardware S
R
or statistical technologies dealing with failures. It presents model- I
OS
based learning and design technologies for fault detection, isolation
L
and identification as well as fault-tolerant control. These models are INTELLIGENT FAULT
also used to analyse the fault detectability and isolability conditions
and discuss the stability of the closed-loop system. It is intended to
DIAGNOSIS AND
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 H
ACCOMMODATION
u
faults; fault detection and isolation; fault identification, and fault a
n
reconfiguration (accommodation) control. It also covers application g

issues in the monitoring control of actuators, providing several • CONTROL
T
interesting case studies for more application-oriented readers. a
n

•

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r

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L Sunan Huang • Kok Kiong Tan
e
e
Poi Voon Er • Tong Heng Lee
COMPUTER SCIENCE & ENGINEERING
ISBN 978-0-367-20879-0
9 780367 208790
CRC Press titles are available as eBook editions in a range of digital formats
Intelligent Fault
Diagnosis and
Accommodation
Control
Automation and Control Engineering
Series Editors - Frank L. Lewis, Shuzhi Sam Ge, and Stjepan Bogdan

Synchronization and Control of Multiagent Systems
Dong Sun

System Modeling and Control with Resource-Oriented Petri Nets
MengChu Zhou, Naiqi Wu

Deterministic Learning Theory for Identification, Recognition, and Control
Cong Wang and David J. Hill

Optimal and Robust Scheduling for Networked Control Systems
Stefano Longo, Tingli Su, Guido Herrmann, and Phil Barber

Electric and Plug-in Hybrid Vehicle Networks
Optimization and Control
Emanuele Crisostomi, Robert Shorten, Sonja Stüdli, and Fabian Wirth

Adaptive and Fault-Tolerant Control of Underactuated Nonlinear Systems
Jiangshuai Huang, Yong-Duan Song

Discrete-Time Recurrent Neural Control
Analysis and Application
Edgar N. Sánchez

Control of Nonlinear Systems via PI, PD and PID
Stability and Performance
Yong-Duan Song

Multi-Agent Systems
Platoon Control and Non-Fragile Quantized Consensus
Xiang-Gui Guo, Jian-Liang Wang, Fang Liao, Rodney Swee Huat Teo

Classical Feedback Control with Nonlinear Multi-Loop Systems
With MATLAB® and Simulink®, Third Edition
Boris J. Lurie, Paul Enright

Motion Control of Functionally Related Systems
Tarik Uzunović and Asif Sabanović

Intelligent Fault Diagnosis and Accommodation Control
Sunan Huang, Kok Kiong Tan, Poi Voon Er, Tong Heng Lee

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Intelligent Fault
Diagnosis and
Accommodation
Control
Sunan Huang
Kok Kiong Tan
Poi Voon Er
Tong Heng Lee
Firsteditionpublished2020
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Contents
Preface ix
Acknowledgments xi
Authors xiii
1 Introduction 1
2 Fault Types and Modeling 7
2.1 Problem backgrounds . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Fault types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Fault modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3 Model-Based Fault Detection 19
3.1 Model-based approaches to fault detection . . . . . . . . . . 19
3.1.1 Parameter estimation approach . . . . . . . . . . . . . 20
3.1.2 Observer-based approach . . . . . . . . . . . . . . . . 22
3.1.2.1 Fault detection against actuator faults . . . . 23
3.1.2.2 Detectability issue . . . . . . . . . . . . . . . 25
3.1.2.3 Extension of fault detection to the more
general MIMO case . . . . . . . . . . . . . . 28
3.1.2.4 Fault detection against both actuator and
sensor faults . . . . . . . . . . . . . . . . . . 28
3.1.2.5 Detectability analysis . . . . . . . . . . . . . 31
3.1.2.6 Simulation example . . . . . . . . . . . . . . 33
3.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Model-Based Fault Isolation 37
4.1 Model-based approaches to fault isolation . . . . . . . . . . . 37
4.1.1 Directional residual scheme . . . . . . . . . . . . . . . 37
4.1.2 Dedicated observer scheme . . . . . . . . . . . . . . . 39
4.1.3 Generalized observer scheme . . . . . . . . . . . . . . 42
4.1.3.1 Thresholds of fault isolation . . . . . . . . . 45
4.1.3.2 Fault isolability analysis . . . . . . . . . . . 46
4.2 Relationship between fault detection and fault isolation . . . 48
4.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
v
vi Contents
5 Model-Based Fault Identification 51
5.1 Neural network-based fault identification . . . . . . . . . . . 51
5.1.1 Actuator fault identification with full-state
measurement . . . . . . . . . . . . . . . . . . . . . . . 52
5.1.2 Actuator fault identification with partial-state
measurements . . . . . . . . . . . . . . . . . . . . . . . 54
5.1.3 Sensor and actuator fault identification with partial-
state measurements . . . . . . . . . . . . . . . . . . . 67
5.1.4 Simulation example . . . . . . . . . . . . . . . . . . . 72
5.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6 Model-Based Fault Accommodation Control 77
6.1 Fault accommodation problem . . . . . . . . . . . . . . . . . 78
6.2 Accommodation control of full state feedback systems . . . . 78
6.2.1 Fault detection of full state feedback systems . . . . . 79
6.2.2 Model-based accommodation control of full state
feedback systems . . . . . . . . . . . . . . . . . . . . . 81
6.2.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . 90
6.3 Accommodation control of output feedback systems . . . . . 91
6.3.1 Fault detection of output feedback systems . . . . . . 94
6.3.2 Fault isolation of output feedback systems . . . . . . . 95
6.3.3 Fault identification of output feedback systems . . . . 97
6.3.4 Model-based accommodation control of output
feedback systems . . . . . . . . . . . . . . . . . . . . . 98
6.3.4.1 Control design without fault occurrence . . 98
6.3.4.2 Control design after fault detection . . . . . 101
6.3.4.3 Simulation . . . . . . . . . . . . . . . . . . . 102
6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7 Model-Based Fault Accommodation Control of Robotic
Systems 107
7.1 Problem statements . . . . . . . . . . . . . . . . . . . . . . . 108
7.2 Fault diagnosis scheme . . . . . . . . . . . . . . . . . . . . . 110
7.2.1 Fault detection . . . . . . . . . . . . . . . . . . . . . . 110
7.2.2 Fault isolation . . . . . . . . . . . . . . . . . . . . . . 111
7.3 Fault accommodation scheme . . . . . . . . . . . . . . . . . . 113
7.3.1 Normal controller before fault detection . . . . . . . . 114
7.3.2 Accommodation control of system failures (T >t T ) 115
1 0
≥
7.3.3 Accommodation control after fault isolation (t T ) . 119
1
≥
7.4 Simulation example . . . . . . . . . . . . . . . . . . . . . . . 122
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
8 Fault Diagnosis and Fault Accommodation Control for
Multi-Agent Systems 131
8.1 Consensus problem . . . . . . . . . . . . . . . . . . . . . . . 131
Contents vii
8.2 Graph theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
8.3 Model-based fault diagnosis of MASs . . . . . . . . . . . . . 133
8.4 Model-based passive fault accommodation control of
MASs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
8.5 Model-based active fault accommodation control of MASs . . 141
8.5.1 Control design before fault occurrence . . . . . . . . . 141
8.5.2 Control design after fault occurrence . . . . . . . . . . 141
8.6 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
8.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
9 Case Studies 149
9.1 Case Study 1: Fault simulator based on hardware-in-the-loop
technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
9.1.1 Induction motor model . . . . . . . . . . . . . . . . . 152
9.1.2 Fault cases of an induction motor . . . . . . . . . . . . 153
9.1.3 Design of hardware-in-the-loop simulator . . . . . . . 155
9.1.4 Experimental results . . . . . . . . . . . . . . . . . . . 157
9.1.5 Some comments . . . . . . . . . . . . . . . . . . . . . 159
9.2 Case Study 2: GPS spoofing detection based on unmanned
aerial vehicle model . . . . . . . . . . . . . . . . . . . . . . . 162
9.2.1 Related work . . . . . . . . . . . . . . . . . . . . . . . 163
9.2.2 Overview of the proposed control strategy . . . . . . . 164
9.2.3 UAV model . . . . . . . . . . . . . . . . . . . . . . . . 164
9.2.4 GPS spoofing . . . . . . . . . . . . . . . . . . . . . . . 167
9.2.5 GPS spoofing detection scheme . . . . . . . . . . . . . 175
9.2.6 Simulation study . . . . . . . . . . . . . . . . . . . . . 176
9.2.7 Some comments . . . . . . . . . . . . . . . . . . . . . 177
9.3 Case Study 3: Failure detection of an electrical machine . . . 179
9.3.1 Model of induction motor . . . . . . . . . . . . . . . . 180
9.3.2 Intelligent fault monitoring scheme . . . . . . . . . . . 182
9.3.3 Intelligent fault isolation scheme . . . . . . . . . . . . 184
9.3.4 Simulation test . . . . . . . . . . . . . . . . . . . . . . 188
9.3.5 Some comments . . . . . . . . . . . . . . . . . . . . . 190
9.4 Case Study 4: Fault-tolerance control of a linear drive . . . . 193
9.4.1 Linear drive system and control objective . . . . . . . 196
9.4.2 Softcomputing background . . . . . . . . . . . . . . . 197
9.4.3 Softcomputing based fault-tolerant control of linear
drives . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
9.4.3.1 Normal controller for healthy system . . . . 198
9.4.3.2 On-line monitoring. . . . . . . . . . . . . . . 200
9.4.3.3 Fault identification. . . . . . . . . . . . . . . 201
9.4.3.4 Fault-tolerant control . . . . . . . . . . . . . 202
9.4.4 Experimental results . . . . . . . . . . . . . . . . . . . 204
9.4.5 Some comments . . . . . . . . . . . . . . . . . . . . . 209
viii Contents
9.5 Case Study 5: Approach towards sensor placement, selection
and fusion for real-time condition monitoring of precision
machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
9.5.1 Proposed framework for condition monitoring . . . . . 216
9.5.1.1 Problem formulation. . . . . . . . . . . . . . 217
9.5.1.2 Preparation and calibration . . . . . . . . . . 217
9.5.1.3 Framework . . . . . . . . . . . . . . . . . . . 220
9.5.1.4 Scalability . . . . . . . . . . . . . . . . . . . 224
9.5.1.5 Low frequency monitor . . . . . . . . . . . . 224
9.5.2 Case study: results and discussion . . . . . . . . . . . 224
9.5.2.1 Data collection and calibration . . . . . . . . 226
9.5.2.2 Real-time condition monitoring. . . . . . . . 236
9.5.3 Some comments . . . . . . . . . . . . . . . . . . . . . 241
Bibliography 243
Index 259
Preface
Industrial systems have many components, such as electrical devices, sensors,
power amplifiers, actuators, and some mechanical parts. Due to long time
operation, the properties of some of these components may be degraded to a
dangerous level which may lead to a fault occurrence. Therefore, increasing
demands on the reliability and safety of systems require a controller that
should have a fault monitoring component which always checks on-line if a
faultoccurs.Afterafaultisdetected,afaultisolationschemeshouldbeusedto
findthefaultlocationandtype.Furthermore,whenweknowafaulttype,this
information can be incorporated into the controller to compensate the effect
ofthefaultinthesystems.Thus,faultdiagnosisandfault-tolerantcontrolare
importantissuesinallkindsofadvancedengineeringsystems.Duringthepast
two decades, considerable research efforts have been made to find systematic
approachestofaultdiagnosisandfault-tolerantcontrolindynamicalsystems.
It has been shown that the use of adequate process models can allow early
faultdiagnosisandaccommodationcontrolwithnormalmeasurablevariables.
Thisbookisaresultofseveralyearsofresearchintherealizationofmodel-
based fault diagnosis and accommodation control. The primary intent of this
bookseeks toreportnew technologies inthe areaof fault diagnosisand fault-
tolerant control, which will ultimately be applied in industries. It covers fault
analysisandstrategiesofdesignforvariousfault-tolerantcontrolapplications.
This book consists of nine chapters treating different topics. The content is
suitable for graduate students and engineers in precision engineering.
In what follows, the contents of the book will be briefly reviewed.
Chapter 1 introduces the fault concept first. Subsequently, fault diagnosis
methods are presented. Several methods are grouped briefly. After that, the
fault-tolerant control methods are discussed and they are grouped as two
classes: passive and active controls. Finally, several application areas of fault
diagnosis and fault-tolerant control are presented.
In Chapter 2, the backgrounds of fault occurrence are first discussed.
Next, fault types are grouped as several classes, including fault occurrence in
actuators, sensors and plant components. After that, a time-dependent fault
time profile is introduced. Finally, two fault modeling approaches are
presented, including mechanism modeling and black-box modeling.
In Chapter 3, the basic concept of model-based fault detection is first
discussed.Threefaultdetectionapproachesarepresented:theobserver-based
approach,theparityspaceapproach,andtheparameterestimationapproach.
ix