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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 ThisisareprintofarticlesfromtheSpecialIssuepublishedonlineintheopenaccessjournalMachines (ISSN2075-1702) (availableat:https://www.mdpi.com/journal/machines/specialissues/dlfaul). Forcitationpurposes,citeeacharticleindependentlyasindicatedonthearticlepageonlineandas indicatedbelow: LastName,A.A.;LastName,B.B.;LastName,C.C.ArticleTitle. JournalNameYear,VolumeNumber, PageRange. ISBN978-3-0365-5173-9(Hbk) ISBN978-3-0365-5174-6(PDF) ©2022bytheauthors. ArticlesinthisbookareOpenAccessanddistributedundertheCreative Commons Attribution (CC BY) license, which allows users to download, copy and build upon publishedarticles,aslongastheauthorandpublisherareproperlycredited,whichensuresmaximum disseminationandawiderimpactofourpublications. ThebookasawholeisdistributedbyMDPIunderthetermsandconditionsoftheCreativeCommons licenseCCBY-NC-ND. 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