Table Of Content152 x 229 mm, Paper, PG, Spine: 20.828 mm
FAULT DIAGNOSIS AND PROGNOSIS TECHNIQUES FF
OA
FOR COMPLEX ENGINEERING SYSTEMS RU
L
CT
EDITED BY HAMID REZA KARIMI O D
MI
A
Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems gives a systematically and almost
P
G
self-contained description of the many facets of envisaging, designing, implementing, or experimentally exploring L
N
emerging trends in fault diagnosis and failure prognosis in mechanical, electrical, hydraulic, and marine systems. E
O
X
S
The book is devoted to the development of mathematical methodologies for fault diagnosis and isolation, fault- E
I
tolerant control, and failure prognosis problems of engineering systems. It presents new techniques in reliability NS
modeling, reliability analysis, reliability design, fault and failure detection, signal processing, and fault-tolerant control G A
of engineering systems. INN
ED
It is specifically focusing on the development of mathematical methodologies for diagnosis and prognosis of faults E
P
or failures, providing a unified platform for understanding and applicability of advanced diagnosis and prognosis RR
I
methodologies for improving reliability purposes in both theory and practice, such as vehicles, manufacturing NO
systems, circuits, flights, and marine systems. GG
N
S
This book will be a valuable resource for different groups of readers—mechanical engineers working on vehicle YO
systems, electrical engineers working on rotary machinery systems, control engineers working on fault detection SS
systems, mathematicians and physician working on complex dynamics, and postgraduate students majoring in TI
S
mechatronics, control engineering, mechanical engineering, and applied mathematics. It can be also of significant E
MT
interest to the researchers within the mechatronics engineering society, including both academic and industrial parts.
E
S
C
Key Features H FAULT DIAGNOSIS AND PROGNOSIS
• Presents recent advances of theory, technological aspects, and applications of advanced diagnosis and N
prognosis methodologies in engineering applications. I
Q
TECHNIQUES FOR COMPLEX
• Provides a series of latest results in, including but not limited to, fault detection, isolation, fault-tolerant U
control, and failure prognosis of components. E
• Gives numerical and simulation results in each chapter to reflect the S ENGINEERING SYSTEMS
engineering practice, yet demonstrate the focus of the developed analysis and synthesis approaches.
About the Editor
Dr. Hamid Reza Karimi is a Professor of Applied Mechanics with the Department of Mechanical Engineering,
Politecnico di Milano, Milan, Italy. His current research interests include control systems and mechatronics with
applications to automotive systems, robotics, vibration systems, and wind energy. Prof. Karimi is currently the Editor-
K
in-Chief, Technical Editor, or Associate Editor for some international journals. He has been awarded as the 2016-2020
A
Web of Science Highly Cited Researcher in Engineering and also received the 2020 IEEE Transactions on Circuits and R
Systems Guillemin-Cauer Best Paper Award. IM
I
Technology and Engineering
ISBN 978-0-12-822473-1
EDITED BY
HAMID REZA KARIMI
9 780128 224731
Fault Diagnosis and Prognosis
Techniques for Complex Engineering
Systems
Fault Diagnosis and
Prognosis Techniques
for Complex
Engineering Systems
Edited by
Hamid Reza Karimi
DepartmentofMechanicalEngineering,PolitecnicodiMilano,Italy
AcademicPressisanimprintofElsevier
125LondonWall,LondonEC2Y5AS,UnitedKingdom
525BStreet,Suite1650,SanDiego,CA92101,UnitedStates
50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates
TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom
Copyright©2021ElsevierInc.Allrightsreserved.
Nopartofthispublicationmaybereproducedortransmittedinanyformorbyany
means,electronicormechanical,includingphotocopying,recording,oranyinformation
storageandretrievalsystem,withoutpermissioninwritingfromthepublisher.Details
onhowtoseekpermission,furtherinformationaboutthePublisher’spermissions
policiesandourarrangementswithorganizationssuchastheCopyrightClearance
CenterandtheCopyrightLicensingAgency,canbefoundatourwebsite:
www.elsevier.com/permissions.
Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyright
bythePublisher(otherthanasmaybenotedherein).
Notices
Knowledge and best practice in this field are constantly changing. As new research
and experience broaden our understanding, changes in research methods, professional
practices,ormedicaltreatmentmaybecomenecessary.
Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgein
evaluating and using any information, methods, compounds, or experiments described
herein. In using such information or methods they should be mindful of their own
safety and the safety of others, including parties for whom they have a professional
responsibility.
To the fullest extent of the law, neither the Publisher nor the authors, contributors, or
editors, assume any liability for any injury and/or damage to persons or property as a
matterofproductsliability,negligenceorotherwise,orfromanyuseoroperationofany
methods,products,instructions,orideascontainedinthematerialherein.
BritishLibraryCataloguing-in-PublicationData
AcataloguerecordforthisbookisavailablefromtheBritishLibrary
LibraryofCongressCataloging-in-PublicationData
AcatalogrecordforthisbookisavailablefromtheLibraryofCongress
ISBN:978-0-12-822473-1
For Information on all Academic Press publications visit our
websiteathttps://www.elsevier.com/books-and-journals
Publisher:MaraConner
AcquisitionsEditor:SonniniR.Yura
EditorialProjectManager:MeganHealy
ProductionProjectManager:KameshRamajogi
CoverDesigner:GregHarris
TypesetbyAptara,NewDelhi,India
Contents
Contributors ix
Preface xi
1 Quality-relatedfaultdetectionanddiagnosis:atechnical
reviewandsummary
GuangWangandHamidRezaKarimi
1.1 Introduction 1
1.2 Basicmethodology 6
1.3 Recentresearch 9
1.4 Simulation 27
AppendixA:Descriptionofthevariablesandfaults 43
References 47
2 Canonicalcorrelationanalysis–basedfaultdiagnosis
methodfordynamicprocesses
ZhiwenChenandKetianLiang
2.1 Introduction 51
2.2 Preliminaries 53
2.3 CCA-basedfaultdiagnosismethodfordynamic
processes 63
2.4 Experimentalresultsandanalysis 71
2.5 Conclusion 82
Acknowledgments 84
References 84
3 H∞ Faultestimationforlineardiscretetime-varyingsystems
withrandomuncertainties
YueyangLi
3.1 Introduction 89
3.2 RobustH∞faultdetectionforLDTVsystemswith
multiplicativenoise 91
3.3 RobustH∞faultdetectionforLDTVsystemswith
measurementpacketloss 102
3.4 Fixed-lagH∞faultestimatordesignforLDTVsystems
underanunreliablecommunicationlink 111
v
vi Contents
3.5 Conclusion 123
Acknowledgments 123
References 123
4 Faultdiagnosisandfailureprognosisofelectricaldrives
EliasG.Strangas
4.1 Introduction 127
4.2 Whatcanfailandhow 132
4.3 Diagnosismethodologyandtools 144
4.4 Faults,theirmanifestation,anddiagnosis 150
4.5 Failureprognosis,faultmitigation,andreliability 165
References 175
5 Intelligentfaultdiagnosisfordynamicsystemsviaextended
stateobserverandsoftcomputing
PaulP.Lin
5.1 Introduction 182
5.2 Extendedstateobserver 183
5.3 Casestudy:three-tankdynamicsystem 188
5.4 FaultdetectionbymeansofESO 192
5.5 Faultisolationandfaultidentification 194
5.6 Simultaneousfaultsofdifferenttypes 197
5.7 Isolationofsimultaneousprocessfaultsandactuatorfaults 200
5.8 Conclusionandfuturework 203
References 204
6 Faultdiagnosisandfailureprognosisinhydraulicsystems
JieLiu,YanheXu,KaiboZhouandMing-FengGe
6.1 Applicationstatusofsensordetectiontechnology 207
6.2 Cavitationresearch 217
6.3 Intelligentevaluationanddiagnosistechnology 229
6.4 Prognosticsresearch 244
References 253
7 Faultdetectionandfaultidentificationinmarinecurrent
turbines
TianzhenWang,ZhichaoLiandYilaiZheng
7.1 TheHT-baseddetectionmethod 264
7.2 Thewaveletthresholddenoising–baseddectectionmethod 269
7.3 Theidentificationmethodofbladeattachmentbasedon
thesparseautoencoderandsoftmaxregression 283
7.4 Theidentificationmethodofbladeattachmentbased
ondepthwiseseparableCNN 290
Contents vii
7.5 Conclusionandfutureworks 299
References 300
8 Quadrotoractuatorfaultdiagnosisandaccommodation
basedonnonlinearadaptivestateobserver
SichengZhou,KexinGuo,XiangYu,LeiGuoandYouminZhang
8.1 Introduction 305
8.2 Mathematicalmodelofaquadrotor 307
8.3 NASO-basedFTC 309
8.4 Validation 319
8.5 Conclusion 323
References 323
9 Defectdetectionandclassificationinweldingusingdeep
learninganddigitalradiography
M-Mahdi Naddaf-Sh, Sadra Naddaf-Sh, Hassan Zargaradeh, Sayyed M.
Zahiri,MaximDalton,GabrielElpersandAmirR.Kashani
9.1 Introduction 327
9.2 Literaturereview 333
9.3 Databasepreparation 336
9.4 Experimentalstudy 336
9.5 Experimentalimplementation 345
9.6 Conclusion 346
References 347
10 Real-timefaultdiagnosisusingdeepfusionoffeatures
extractedbyPeLSTMandCNN
FunaZhou,ZhiqiangZhangandDanminChen
10.1 Introduction 353
10.2 Basictheory 356
10.3 DeepfusionoffeatureextractedbyPeLSTMandCNN 357
10.4 Experimentaltesting 371
10.5 Conclusionandfuturework 395
Acknowledgment 398
References 398
Index 401
Contributors
DanminChen,SchoolofSoftware,HenanUniversity,China
ZhiwenChen,SchoolofAutomation,CentralSouthUniversity,China
Maxim Dalton, Artificial Intelligence Lab, Stanley Oil, and Gas, Stanley Black, and
Decker,UnitedStates
Gabriel Elpers, Artificial Intelligence Lab, Stanley Oil, and Gas, Stanley Black, and
Decker,UnitedStates
Ming-Feng Ge, School of Mechanical Engineering and Electronic Information, China
UniversityofGeosciences,China
KexinGuo,SchoolofAutomationScienceandElectricalEngineering,BeihangUniver-
sity,China
LeiGuo,SchoolofAutomationScienceandElectricalEngineering,BeihangUniversity;
Beijing Advanced Innovation Center for BigData-Based Precision Medicine, Bei-
hangUniversity,Beijing,China
HamidReza Karimi, Department of Mechanical Engineering, Politecnico di Milano,
Italy
AmirR.Kashani,ArtificialIntelligenceLab,StanleyOil,andGas,StanleyBlack,and
Decker,UnitedStates
YueyangLi,SchoolofElectricalEngineering,UniversityofJinan,China
ZhichaoLi,SchoolofLogisticEngineering,ShanghaiMaritimeUniversity,China
KetianLiang,SchoolofAutomation,CentralSouthUniversity,China
PaulP.Lin,FellowoftheAmericanSocietyofMechanicalEngineers(ASME);Professor
Emeritus,MechanicalEngineeringDepartment,ClevelandStateUniversity,United
States;VisitingScholar,KaohsiungUniversityofScienceandTechnology,Taiwan
M-Mahdi Naddaf-Sh, Electrical Engineering Department, Lamar University, United
States
SadraNaddaf-Sh,ElectricalEngineeringDepartment,LamarUniversity,UnitedStates
EliasG.Strangas,MichiganStateUniversity,UnitedStates
GuangWang,NorthChinaElectricPowerUniversity–BaodingCampus,China
TianzhenWang,SchoolofLogisticEngineering,ShanghaiMaritimeUniversity,China
XiangYu,SchoolofAutomationScienceandElectricalEngineering,BeihangUniver-
sity;Beijing Advanced Innovation Center for BigData-Based Precision Medicine,
BeihangUniversity,Beijing,China
ix
