Deep Learning-Based Machinery Fault Diagnostics

292 Pages·2022·25.255 MB·English

by  Hongtian Chen, Kai Zhong, Guangtao Ran, Chao Cheng

Deep Learning-
Based Machinery
Fault Diagnostics
Edited by
Hongtian Chen, Kai Zhong, Guangtao Ran and Chao Cheng
Printed Edition of the Special Issue Published in Machines
www.mdpi.com/journal/machines
Deep Learning-Based Machinery
Fault Diagnostics

Deep Learning-Based Machinery
Fault Diagnostics
Editors
HongtianChen
KaiZhong
GuangtaoRan
ChaoCheng
MDPI•Basel•Beijing•Wuhan•Barcelona•Belgrade•Manchester•Tokyo•Cluj•Tianjin
Editors
HongtianChen KaiZhong GuangtaoRan
DepartmentofChemical InstitutesofPhysicalScience DepartmentofControl
andMaterialsEngineering, andInformationTechnology, ScienceandEngineering,
UniversityofAlberta, AnhuiUniversity, HarbinInstituteofTechnology,
Edmonton,ABT6G1H9,Canada Anhui230601,China Harbin150001,China
ChaoCheng
SchoolofComputerScience
andEngineering,Changchun
UniversityofTechnology,
Changchun130012,China
EditorialOffice
MDPI
St.Alban-Anlage66
4052Basel,Switzerland
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Contents
AbouttheEditors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
HongtianChen,KaiZhong,GuangtaoRanandChaoCheng
DeepLearning-BasedMachineryFaultDiagnostics
Reprintedfrom:Machines2022,10,690,doi:10.3390/machines10080690 . . . . . . . . . . . . . . . 1
QunhongTian,TaoWang,BingLiuandGuangtaoRan
Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle
withOceanCurrents
Reprintedfrom:Machines2022,10,582,doi:10.3390/machines10070582 . . . . . . . . . . . . . . . 5
HongZheng,KeyuanZhu,ChaoChengandZhaowangFu
FaultDetectionforHigh-SpeedTrainsUsingCCAandJust-in-TimeLearning
Reprintedfrom:Machines2022,10,526,doi:10.3390/machines10070526 . . . . . . . . . . . . . . . 23
LuQian,QingPan,YaqiongLvandXingweiZhao
FaultDetectionofBearingbyResnetClassifierwithModel-BasedDataAugmentation
Reprintedfrom:Machines2022,10,521,doi:10.3390/machines10070521 . . . . . . . . . . . . . . . 37
XiangyuPeng,YalinWang,LinGuanandYongfeiXue
A Local Density-Based Abnormal Case Removal Method for Industrial Operational
OptimizationundertheCBRFramework
Reprintedfrom:Machines2022,10,471,doi:10.3390/machines10060471 . . . . . . . . . . . . . . . 53
ShuyueGuan,DarongHuang,ShenghuiGuo,LingZhaoandHongtianChen
AnImprovedFaultDiagnosisApproachUsingLSSVMforComplexIndustrialSystems
Reprintedfrom:Machines2022,10,443,doi:10.3390/machines10060443 . . . . . . . . . . . . . . . 67
RongqiangZhaoandXiongHu
An Adaptive Fusion Convolutional Denoising Network and Its Application to the Fault
DiagnosisofShoreBridgeLiftGearbox
Reprintedfrom:Machines2022,10,424,doi:10.3390/machines10060424 . . . . . . . . . . . . . . . 89
ZhongdaLu,ChundaZhang,FengxiaXu,ZifeiWangandLijingWang
FaultDetectionforIntervalType-2T-SFuzzyNetworkedSystemsviaEvent-TriggeredControl
Reprintedfrom:Machines2022,10,347,doi:10.3390/machines10050347 . . . . . . . . . . . . . . . 105
ZhigangLi,ZhijieZhou,JieWang,WeiHeandXiangyiZhou
Health Assessment of Complex System Based on Evidential Reasoning Rule with
TransformationMatrix
Reprintedfrom:Machines2022,10,250,doi:10.3390/machines10040250 . . . . . . . . . . . . . . . 123
LijingWang,ChundaZhang,JuanZhuandFengxiaXu
FaultDiagnosisofMotorVibrationSignalsbyFusionofSpatiotemporalFeatures
Reprintedfrom:Machines2022,10,246,doi:10.3390/machines10040246 . . . . . . . . . . . . . . . 151
JiaruiCui,PeiningWang,XiangquanLi,RuoyuHuang,QingLi,BinCaoandHuiLu
Multipoint Feeding Strategy of Aluminum Reduction Cell Based on Distributed Subspace
PredictiveControl
Reprintedfrom:Machines2022,10,220,doi:10.3390/machines10030220 . . . . . . . . . . . . . . . 169
XiaoyuCheng,ShanshanLiu,WeiHe,PengZhang,BingXu,YawenXieandJiayuanSong
AModelforFlywheelFaultDiagnosisBasedonFuzzyFaultTreeAnalysisandBeliefRuleBase
Reprintedfrom:Machines2022,10,73,doi:10.3390/machines10020073 . . . . . . . . . . . . . . . 185
v
PuYang,ChenwanWen,HuilinGengandPengLiu
IntelligentFaultDiagnosisMethodforBladeDamageofQuad-RotorUAVBasedonStacked
PruningSparseDenoisingAutoencoderandConvolutionalNeuralNetwork
Reprintedfrom:Machines2021,9,360,doi:10.3390/machines9120360 . . . . . . . . . . . . . . . . 209
ShubinWang,YukunTian,XiaogangDeng,QianleiCao,LeiWangandPengxiangSun
Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical
VariateAnalysisMethod
Reprintedfrom:Machines2021,9,272,doi:10.3390/machines9110272 . . . . . . . . . . . . . . . . 229
ChenXuandYawenMao
Auxiliary Model-Based Multi-Innovation Fractional Stochastic Gradient Algorithm
forHammersteinOutput-ErrorSystems
Reprintedfrom:Machines2021,9,247,doi:10.3390/machines9110247 . . . . . . . . . . . . . . . . 245
NingChen,FuhaiHu,JiayaoChen,ZhiwenChen,WeihuaGuiandXuLi
AProcessMonitoringMethodBasedonDynamicAutoregressiveLatentVariableModelandIts
ApplicationintheSinteringProcessofTernaryCathodeMaterials
Reprintedfrom:Machines2021,9,229,doi:10.3390/machines9100229 . . . . . . . . . . . . . . . . 261
vi
About the Editors
HongtianChen
HongtianChen(Member,IEEE)receivedtheB.S.andM.S.degreesinSchoolofElectricaland
Automation Engineering from Nanjing Normal University, China, in 2012 and 2015, respectively;
andhereceivedthePh.D.degreeinCollegeofAutomationEngineeringfromNanjingUniversityof
AeronauticsandAstronautics,China,in2019.HehadeverbeenaVisitingScholarattheInstitutefor
AutomaticControlandComplexSystems,UniversityofDuisburg-Essen,Germany,in2018.Nowhe
isaPost-DoctoralFellowwiththeDepartmentofChemicalandMaterialsEngineering,Universityof
Alberta,Canada.Hisresearchinterestsincludeprocessmonitoringandfaultdiagnosis,datamining
and analytics, machinelearning, and quantum computation; and their applications in high-speed
trains,newenergysystems,andindustrialprocesses.
Dr. ChenwasarecipientoftheGrandPrizeofInnovationAwardofMinistryofIndustryand
InformationTechnologyofthePeople’sRepublicofChinain2019,theExcellentPh.D.ThesisAward
ofJiangsuProvincein2020,andtheExcellentDoctoralDissertationAwardfromChineseAssociation
of Automation (CAA) in 2020. He currently serves as Associate Editors and Guest Editors of a
number of scholarly journals such as IEEE Transactions on Instrumentation and Measurement,
IEEETransactionsonNeuralNetworksandLearningSystemsandIEEETransactionsonArtificial
Intelligence.
KaiZhong
KaiZhong(Professor)receivedthePh.D.fromDalianUniversityofTechnologyin2020.Nowhe
isanassociateprofessorwiththeInstitutesofPhysicalScienceandInformationTechnology,Anhui
University. Hismainresearchinterestsincludeprocessmonitoring,faultdiagnosisanddeepneural
networks.
GuangtaoRan
GuangtaoRanreceivedtheB.E.andM.E.degreesfromQiqiharUniversity,Qiqihar,China,in
2016 and 2019, respectively. He is currently pursuing the Ph.D. degree with the Department of
ControlScienceandEngineering,HarbinInstituteofTechnology,Harbin,China. Nowheisalsoa
jointtrainingstudentwiththeDepartmentofElectricalandComputerEngineering, Universityof
Alberta,Edmonton,ABT6G1H9,Canada.
Hisresearchinterestsincludefuzzycontrol,reinforcementlearning,networkedcontrolsystems,
multi-agentsystems,androbustcontrol.
ChaoCheng
Chao Cheng (Professor) received the M.Eng. and Ph.D. degrees from Jilin University,
Changchun,China,in2011and2014,respectively. HeiscurrentlyaprofessorwiththeChangchun
University of Technology, Changchun. He has been a Post-Doctoral Fellow in process control
engineeringwiththeDepartmentofAutomation,TsinghuaUniversity,Beijing,China,since2018.He
hasalsobeenaPost-DoctoralFellowwiththeNationalEngineeringLaboratory,CRRCChangchun
Railway Vehicles Co., Ltd., China, since 2018. His research interest includes dynamic system
fault diagnosis and predictive maintenance, wireless sensor network, artificial intelligence, and
data-drivenmethod.
vii

machines
Editorial
Deep Learning-Based Machinery Fault Diagnostics
HongtianChen1,*,KaiZhong2,GuangtaoRan1,3andChaoCheng4
1 DepartmentofChemicalandMaterialsEngineering,UniversityofAlberta,Edmonton,ABT6G1H9,Canada
2 InstitutesofPhysicalScienceandInformationTechnology,AnhuiUniversity,Hefei230601,China
3 DepartmentofControlScienceandEngineering,HarbinInstituteofTechnology,Harbin150001,China
4 SchoolofComputerScienceandEngineering,ChangchunUniversityofTechnology,
Changchun130012,China
* Correspondence:[email protected]
Inrecentyears,deeplearninghasshownitsuniquepotentialandadvantagesinfeature
extractionandpatternrecognition. Theapplicationofdeeplearningtofaultdiagnosis
ofcomplexmachinerysystemshasbegunitsinitialexplorationstage.ThisSpecialIssue
providesaninternationalforumforprofessionals,academics,andresearcherstopresentthe
latestdevelopmentsfromtheoreticalstudiesandcomputationalalgorithmdevelopmentto
applicationsofadvanceddeeplearning-basedmachinerysystemfaultdiagnosismethods.
Thecontentsofthesestudiesarebrieflydescribedasfollows.
In[1],apossibilisticfuzzyC-means(PFCM)algorithmwasproposedtorealizethe
faultclassification.Basedontheresultsoffaultdiagnostics,afuzzycontrolstrategywas
usedtosolvethefaulttolerantcontrolforAUV.Consideringtheuncertaintyofocean
currents,amin-maxrobustoptimizationstrategywascarriedouttooptimizethefuzzycon-
troller,whichwassolvedbyacooperativeparticleswarmoptimization(CPSO)algorithm.
Simulationandunderwaterexperimentswereusedtoverifytheaccuracyandfeasibilityof
theproposedmethodinfaultdiagnosticsandfault-tolerantcontrol.
In[2],theauthorsproposedafaultdetection(FD)model,namedasCCA-JITLby
usingcanonicalcorrelationanalysis(CCA)andjust-in-timelearning(JITL)toprocess
scalarsignalsofhigh-speedtraingears.Afterdatapre-processingandnormalization,CCA
Citation:Chen,H.;Zhong,K.;Ran,
transformedcovariancematricesofhigh-dimensionhistoricaldataintolow-dimension
G.;Cheng,C.DeepLearning-Based
subspaceandmaximizedcorrelationsbetweenthemostimportantlatentdimensions.Then,
MachineryFaultDiagnostics.
JITLcomponentsformulatedthelocalFDmodelbyutilizingthesubsetsoftestingsamples
Machines2022,10,690. https://
withlargerEuclideandistancestotrainingdata.AcasestudydemonstratedthataCCA-
doi.org/10.3390/machines10080690
JITLFDmodelsignificantlyoutperformedtraditionalCCAmodels.Theproposedapproach
Received:8August2022 canalsobeintegratedwithotherdimensionreductionFDmodels,suchastheprincipal
Accepted:8August2022 componentanalysisandpartialleastsquaresmodels.
Published:13August2022
In [3], the authors designed a Resnet-based classifier with the model-based data
Publisher’sNote:MDPIstaysneutral augmentationskill,whichwasappliedforbearingfaultdetection.Inparticular,adynamic
withregardtojurisdictionalclaimsin modelwasfirstestablishedtodescribethebearingsystembyadjustingmodelparameters,
publishedmapsandinstitutionalaffil- suchasspeed,load,faultsize,andthedifferentfaulttypes.Largeamountsofdataunder
iations. variousoperationconditionscanthenbegenerated.Thetrainingdatasetwasconstructed
throughthesimulateddata,whichwasthenappliedtotraintheResnetclassifier.Moreover,
inordertoreducethegapbetweenthesimulationdataandtherealdata,theenvelopsignals
wereusedinsteadoftheoriginalsignalsinthetrainingprocess.Finally,theeffectiveness
Copyright: © 2022 by the authors.
oftheproposedmethodwasdemonstratedbytherealbearingdata. Itwasremarkable
Licensee MDPI, Basel, Switzerland.
thattheapplicationoftheproposedmethodcanbefurtherextendedtoothermechatronic
Thisarticleisanopenaccessarticle
systemswithadeterministicdynamicmodel.
distributed under the terms and
In[4],alocaldensity-basedabnormalcaseremovalmethodwasproposedtoremove
conditionsoftheCreativeCommons
theabnormalcasessoastopreventperformancedeteriorationinindustrialoperational
Attribution(CCBY)license(https://
optimization.Morespecifically,thereasonswhyclassiccase-basedreasoning(CBR)would
creativecommons.org/licenses/by/
retrieveabnormalcaseswereanalyzedfromtheperspectiveofcaseretrieval.Then,alocal
4.0/).
Machines2022,10,690.https://doi.org/10.3390/machines10080690 1 https://www.mdpi.com/journal/machines