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基于改进VMD与GS_SVM的轴承故障诊断.docx

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基于改进VMD与GS_SVM的轴承故障诊断 Vibration-basedmachineconditionmonitoringandfaultdiagnosishavebecomeessentialtechniquesforensuringthesafeandreliableoperationofrotatingmachinery.Amongvariousvibrationanalysismethods,theVibrationModeDecomposition(VMD)algorithmandtheGaussianSupportVectorMachine(GS_SVM)classifierhaveshownpromisingresultsinthefieldofbearingfaultdiagnosis.ThispaperaimstopresentacomprehensivereviewandanimprovedapproachcombiningVMDandGS_SVMforbearingfaultdiagnosis. 1.Introduction: Thefailureofbearingsisamajorcauseofmachinebreakdownsandcanleadtocostlyrepairsandproductiondowntime.Therefore,earlydetectionanddiagnosisofbearingfaultsarecrucialforpreventingcatastrophicfailures.Vibrationanalysishasbeenwidelyemployedasanon-invasiveandeffectivemethodfordetectinganddiagnosingmachinefaults.Inrecentyears,theVMDalgorithmandtheGS_SVMclassifierhavegainedincreasingattentionduetotheirsuperiorperformanceinfaultdiagnosistasks. 2.VibrationModeDecomposition: VMDisanadaptivesignaldecompositionmethodthatseparatesamulti-componentsignalintoasetofintrinsicmodefunctions(IMFs)withdifferenttemporalandspectralcharacteristics.TheVMDalgorithmovercomesthelimitationsofconventionaldecompositionmethodsbyadaptivelyadjustingthecenterfrequenciesandbandwidthsofeachIMF.Thisallowsforaccurateseparationofdifferentfault-relatedcomponentsinbearingvibrationsignals. 3.GaussianSupportVectorMachine: GS_SVMisanextensionofthetraditionalSupportVectorMachine(SVM)classifierthatincorporatesaGaussiankernelfornon-linearclassificationtasks.SVMiswidelyusedforfaultdiagnosisduetoitsabilitytohandlehigh-dimensionalfeaturespacesanditsrobustnessagainstnoise.TheGaussiankernelfunctioninGS_SVMcapturesthenon-linearrelationshipsbetweeninputfeaturesandclasslabels,whichisadvantageousforcomplexfaultpatternsinbearingvibrationsignals. 4.ImprovedApproach: Tofurtherenhancetheperformanceofbearingfaultdiagnosis,animprovedapproachcombiningVMDandGS_SVMisproposed.Theprocessinvolvesthefollowingsteps: 4.1.DataAcquisitionandPreprocessing: Vibrationsign