Diagnosis and Fault-tolerant Control, Volume 2: From Fault Diagnosis to Fault-tolerant Control

277 Pages·2022·4.763 MB·English

by  Vicenç Puig, Silvio Simani

Diagnosis and Fault-tolerant Control 2
SCIENCES
Systems and Industrial Engineering,
Field Director – Jean-Paul Bourrières
Reliability, Diagnosis, Safety and Maintenance of Systems,
Subject Head – Jean-Marie Flaus
Diagnosis and
Fault-tolerant Control 2
From Fault Diagnosis to
Fault-tolerant Control
Coordinated by
Vicenç Puig
Silvio Simani
First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as
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© ISTE Ltd 2021
The rights of Vicenç Puig and Silvio Simani to be identified as the authors of this work have been
asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2021941902

British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78945-059-0
ERC code:
PE7 Systems and Communication Engineering
PE7_1 Control engineering
Contents
Chapter1.NonlinearMethodsforFaultDiagnosis . . . . . . . . . . . 1
SilvioSIMANIandPaoloCASTALDI
1.1.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2.Faultdiagnosistasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1.Residualgenerationtask . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2.Residualevaluationtask . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.Model-basedfaultdiagnosis . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.1.Parityspacerelations. . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.2.Observer-basedapproaches . . . . . . . . . . . . . . . . . . . . . . 12
1.3.3.Nonlinearfilteringmethods . . . . . . . . . . . . . . . . . . . . . . 14
1.3.4.Nonlineargeometricapproachstrategy . . . . . . . . . . . . . . . 17
1.4.Data-drivenfaultdiagnosis . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4.1.Onlineidentificationmethods . . . . . . . . . . . . . . . . . . . . . 21
1.4.2.Machinelearningapproachestofaultdiagnosis. . . . . . . . . . . 24
1.5.Model-basedanddata-drivenintegratedfaultdiagnosis . . . . . . . . . 34
1.6.Robustfaultdiagnosisproblem . . . . . . . . . . . . . . . . . . . . . . . 42
1.7.Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
1.8.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Chapter2.LinearParameterVaryingMethods . . . . . . . . . . . . . . 57
MickaelRODRIGUES,HabibHAMDIandDidierTHEILLIOL
2.1.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.2.Preliminaries: aclassicalapproach . . . . . . . . . . . . . . . . . . . . . 60
2.3.Problemstatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
2.4.Robustactivefault-tolerantcontroldesign . . . . . . . . . . . . . . . . . 65
2.4.1.Robustobserver-basedFTCdesign. . . . . . . . . . . . . . . . . . 65
2.4.2.Stabilityanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
vi DiagnosisandFault-tolerantControl2
2.5.Application: ananaerobicbioreactor . . . . . . . . . . . . . . . . . . . . 75
2.6.Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
2.7.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Chapter3.FuzzyandNeuralNetworkApproaches . . . . . . . . . . . 85
MarcinWITCZAK,MarcinPAZERA,NorbertKUKUROWSKIand
MarcinMRUGALSKI
3.1.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3.2.Fuzzymodeldesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.2.1.Takagi–Sugenosystems . . . . . . . . . . . . . . . . . . . . . . . . 87
3.2.2.GenerationofTSmodelsvianonlinearembedding. . . . . . . . . 88
3.3.Neuralmodeldesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.3.1.Recurrentneuralnetwork . . . . . . . . . . . . . . . . . . . . . . . 90
3.3.2.Identificationoftheneuralmodeluncertainty . . . . . . . . . . . . 93
3.4.Faultestimationanddiagnosis . . . . . . . . . . . . . . . . . . . . . . . 94
3.4.1.Actuatorfaultestimationusingneuralnetworks . . . . . . . . . . 94
3.4.2.Sensorandactuatorfaultestimationusingfuzzylogic . . . . . . . 97
3.5.Fault-tolerantcontrol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.5.1.Anoverviewofthefault-tolerantscheme . . . . . . . . . . . . . . 101
3.5.2.Robustfaultestimationandcontrol. . . . . . . . . . . . . . . . . . 103
3.5.3.Derivationofarobustinvariantset . . . . . . . . . . . . . . . . . . 106
3.5.4.EfficientpredictiveFTC . . . . . . . . . . . . . . . . . . . . . . . . 106
3.6.Illustrativeexamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
3.6.1.Sensorandactuatorfaultestimationexample . . . . . . . . . . . . 110
3.6.2.Fault-tolerantcontrolexample . . . . . . . . . . . . . . . . . . . . 113
3.7.Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
3.8.Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
3.9.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Chapter4.ModelPredictiveControlMethods . . . . . . . . . . . . . . 121
KrzysztofPATAN
4.1.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
4.2.IdeaofMPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
4.3.RobustnessofMPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
4.4.Neural-network-basedrobustMPC . . . . . . . . . . . . . . . . . . . . . 126
4.4.1.Neuralnetworkmodels . . . . . . . . . . . . . . . . . . . . . . . . 127
4.4.2.NonlinearMPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
4.4.3.ApproximateMPC . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
4.4.4.RobustnonlinearMPC. . . . . . . . . . . . . . . . . . . . . . . . . 132
4.4.5.RobustapproximateMPC . . . . . . . . . . . . . . . . . . . . . . . 132
4.5.Robustcontrolofapneumaticservo . . . . . . . . . . . . . . . . . . . . 134
4.5.1.Robustnonlinearneural-network-basedMPC . . . . . . . . . . . . 135
4.5.2.Robustapproximateneural-network-basedMPC . . . . . . . . . . 139
Contents vii
4.6.Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.7.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Chapter5.NonlinearModelingforFault-tolerantControl . . . . . . . 143
SilvioSIMANIandPaoloCASTALDI
5.1.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.1.1.Jointfaultdiagnosisandcontrol . . . . . . . . . . . . . . . . . . . 147
5.1.2.Nonlinearadaptivefaultestimators. . . . . . . . . . . . . . . . . . 149
5.1.3.Fuzzyfault-tolerantcontrol . . . . . . . . . . . . . . . . . . . . . . 161
5.1.4.Recursiveadaptivecontrol . . . . . . . . . . . . . . . . . . . . . . 164
5.1.5.Sustainablecontrol . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
5.2.Fault-tolerantcontrolstrategies . . . . . . . . . . . . . . . . . . . . . . . 175
5.2.1.Faulttoleranceandcompensation . . . . . . . . . . . . . . . . . . 177
5.3.Faultdiagnosisandtolerantcontrol. . . . . . . . . . . . . . . . . . . . . 180
5.3.1.Fault-tolerantcontroldesign . . . . . . . . . . . . . . . . . . . . . 183
5.4.Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
5.5.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Chapter6.VirtualSensorsandActuators . . . . . . . . . . . . . . . . . 193
DamianoROTONDOandVicençPUIG
6.1.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
6.2.Problemstatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
6.3.Virtualsensorsandvirtualactuators . . . . . . . . . . . . . . . . . . . . 198
6.4.LMI-baseddesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
6.5.Additionalconsiderations . . . . . . . . . . . . . . . . . . . . . . . . . . 205
6.6.Applicationexample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
6.6.1.Virtualactuator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
6.6.2.Virtualsensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
6.7.Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
6.8.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
Chapter7.Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
VicençPUIGandSilvioSIMANI
7.1.Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
7.2.Closingremarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
7.3.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Chapter8.OpenResearchIssues . . . . . . . . . . . . . . . . . . . . . . 241
VicençPUIGandSilvioSIMANI
8.1.Furtherworksandopenproblems. . . . . . . . . . . . . . . . . . . . . . 241
8.1.1.Sustainablecontroldesignobjectives . . . . . . . . . . . . . . . . 243
8.1.2.Sustainablecontrolconceptsandapproaches . . . . . . . . . . . . 247
viii DiagnosisandFault-tolerantControl2
8.1.3.Sustainablecontrolapproachesandworkingmethods . . . . . . . 249
8.1.4.Sustainablecontroldesignambition . . . . . . . . . . . . . . . . . 253
8.1.5.Sustainablecontrolinnovationpotentials . . . . . . . . . . . . . . 258
8.1.6.Sustainablecontrolexpectedimpacts . . . . . . . . . . . . . . . . 259
8.2.Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
8.3.References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262
ListofAuthors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
SummaryofVolume1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
1
Nonlinear Methods
for Fault Diagnosis
Silvio SIMANI1 andPaolo CASTALDI2
1DepartmentofEngineering,UniversityofFerrara,Emilia-Romagna,Italy
2DepartmentofElectrical,ElectronicandInformationEngineering,
UniversityofBologna,Italy
1.1. Introduction
The model-based approach to fault diagnosis in technical processes has been
receivingmoreandmoreattentionoverthelastfourdecades,inthecontextsofboth
researchandrealplantapplication.
Stemming from this activity, a large number of methods can be found in current
literature based on the use of mathematical models of the technical process under
diagnosisandonexploitingadvancedcontroltheory.
Model-based fault diagnosis methods usually use residuals that indicate changes
betweentheprocessandthemodel.Onegeneralassumptionisthattheresidualsare
changed significantly so that detection is possible. This means that the residual size
aftertheappearanceofafaultislargeandlongenoughtobedetectable.
This chapter provides an overview on different fault diagnosis strategies, with
particular attention to the fault detection and isolation (FDI) methods related to the
dynamicprocessesandapplicationexamplesconsideredinthisbook.
Forallofthemethodsconsidered,itisessentialthatthetechnicalprocesscanbe
described by a mathematical model. As there is almost never an exact agreement
DiagnosisandFault-tolerantControl2,
coordinatedbyVicençPUIGandSilvioSIMANI.©ISTELtd2021.
Diagnosis and Fault-tolerant Control 2: From Fault Diagnosis to Fault-tolerant Control,
First Edition. Vicenç Puig and Silvio Simani.
© ISTE Ltd 2021. Published by ISTE Ltd and John Wiley & Sons, Inc.
2 DiagnosisandFault-tolerantControl2
between the model used to represent the process and the plant, the model-reality
discrepancyisofprimaryinterest.
Hence, the most important issue in model-based fault detection concerns the
accuracy of the model describing the behavior of the monitored system. This issue
has become a central research theme over recent years, as modeling uncertainty has
risen from the impossibility of obtaining complete knowledge and understanding of
themonitoredprocess.
The main focus of this chapter is the mathematical description aspects of the
process whose faults are to be detected and isolated. The chapter also studies the
general structure of the controlled system, its possible fault locations and modes.
Residual generation is then identified as an essential problem in model-based FDI,
because, if it is not performed correctly, some fault information could be lost. The
generalframeworkfortheresidualgenerationisalsorecalled.
Residual generators based on different methods, such as input–output, state and
outputobservers,parityrelationsandparameterestimations,arejustspecialcasesin
this general framework. In the following, some commonly used residual generation
and evaluation techniques are discussed and their mathematical formulation is
presented.
Finally, the chapter presents and summarizes special features and problems
regardingthedifferentmethods.
1.2. Faultdiagnosistasks
According to the definitions available in the related literature, model-based FDI
can be defined as the detection, isolation and identification of faults in a system by
using methods that can extract features from measured signals and use a priori
information on the process available in terms of mathematical models. Faults are,
thus, detected by setting fixed or variable thresholds on residual signals generated
from the difference between actual measurements and their estimates obtained by
usingtheprocessmodel.
Anumberofresidualscanbedesigned,witheachhavingsensitivitytoindividual
faults occurring in different locations of the system. The analysis of each residual,
oncethethresholdisexceeded,thenleadstofaultisolation.
Figure 1.1 shows the general model-based FDI system. It comprises two main
stages of residual generation and residual evaluation. This structure was first
suggested in Chow and Willsky (1980) and now is widely accepted by the fault
diagnosiscommunity.