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改进小波算法在滚动轴承故障诊断的应用.docx

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改进小波算法在滚动轴承故障诊断的应用 Title:ImprovedApplicationofWaveletAlgorithmforRollingBearingFaultDiagnosis Abstract: Rollingbearingfaultdiagnosisplaysacriticalroleinensuringthereliableandsafeoperationofrotatingmachinery.Asanefficientsignalprocessingtool,thewaveletalgorithmhasbeenwidelyappliedinfaultdiagnosisduetoitsexcellenttime-frequencyanalysisabilities.Inthispaper,weproposeanimprovedapplicationofthewaveletalgorithmforrollingbearingfaultdiagnosis.Byintegratingadvancedtechniquessuchasdenoising,featureextraction,andclassification,theproposedapproachenhancestheaccuracyandreliabilityoffaultdetectionanddiagnosisinrollingbearings.Experimentalresultsshowthattheimprovedwaveletalgorithmeffectivelyidentifiesanddiagnosesdifferenttypesofrollingbearingfaults,contributingtocondition-basedmaintenancestrategiesandthepreventionofcatastrophicmachinefailures. 1.Introduction Rollingbearingsareessentialcomponentsofrotatingmachinery,includingmotors,gearboxes,andturbines.Bearingfaults,suchaspitting,spalling,andmisalignment,canleadtocatastrophicmachinefailuresifnotdetectedanddiagnosedinatimelymanner.Traditionalfaultdiagnosismethods,suchasvibrationanalysisandacousticemission,havesomelimitationsintermsofsignalanalysisandinterpretation.Thewaveletalgorithm,basedonthetransformtheory,hasemergedasapowerfultoolinfaultdiagnosisduetoitsabilitytosimultaneouslyanalyzetimeandfrequencyinformation. 2.BasicTheoryoftheWaveletAlgorithm Thewaveletalgorithmdecomposesasignalintodifferentscalesandfrequencybandsusingwaveletfunctions.Thetime-scalerepresentationprovidedbythewavelettransformallowsforaneffectiveanalysisoftransientandnon-stationarysignals.Thebasictheoryofthewaveletalgorithm,includingcontinuouswavelettransform(CWT)anddiscretewavelettransform(DWT),isintroducedinthissection. 3.SignalPreprocessingandDenoising Oneofthechallengesinrollingbearingfaultdiagnosisisthepresenceofnoiseinacquiredsignals,whichcanmaskthefaultsignature.Inthissection,variousdenoisingtechniquesarereviewed,includingwavelet-baseddenoising,threshold