基于加权合成少数类过采样技术的故障诊断.docx

基于加权合成少数类过采样技术的故障诊断
摘要
故障诊断是工业领域中的重要问题之一，而在故障诊断中，少数类样本往往难以被正确分类，因为其数量较少，且常常被误分类为多数类。因此，本文提出了加权合成少数类过采样技术来解决这个问题。这种方法能够有效地增加少数类样本的数量，并提高分类的准确率。同时，我们将该方法与其他常见的过采样技术进行了比较，结果表明加权合成少数类过采样技术具有更好的性能。
关键词：故障诊断，过采样技术，少数类，加权合成
Abstract
Faultdiagnosisisoneoftheimportantproblemsintheindustrialfield.Infaultdiagnosis,minoritysamplesareoftendifficulttobecorrectlyclassifiedbecauseoftheirsmallnumberandtheyareoftenmisclassifiedasmajoritysamples.Therefore,thispaperproposesaweightedsyntheticminorityover-samplingtechniquetosolvethisproblem.Thismethodcaneffectivelyincreasethenumberofminoritysamplesandimprovetheaccuracyofclassification.Atthesametime,wecomparedthismethodwithothercommonover-samplingtechniques.Theresultsshowthattheweightedsyntheticminorityover-samplingtechniquehasbetterperformance.
Keyword:faultdiagnosis,over-samplingtechnique,minorityclass,weightedsynthesis
1.Introduction
Faultdiagnosisisanimportanttaskinindustrialsystems.Itisessentialforensuringthereliabilityofthesystemandimprovingitsefficiency.Theaccuracyoffaultdiagnosislargelydependsonthequalityoftheinputdata.However,inmanycases,thedataisimbalanced,makingitdifficulttoachieveaccuratefaultdiagnosis.Specifically,thenumberofminorityclasssamplesisoftensignificantlysmallerthanthenumberofmajorityclasssamples,leadingtoabiasedlearningprocess.Toovercomethisproblem,itisnecessarytodevelopeffectivemethodstohandletheimbalanceddata.
Over-samplingisacommontechniqueforhandlingimbalanceddata.Itinvolvesgeneratingsyntheticsamplesbyreplicatingtheminorityclassorconstructingnewsamplesbasedontheexistingminorityclasssamples.Thesetechniquescanhelpbalancethenumberofsamplesbetweenthemajorityandminorityclasses,andimprovetheaccuracyofthemodel.
However,traditionalover-samplingtechniquessuchasrandomoversamplingandSMOTE(SyntheticMinorityOver-SamplingTechnique)mayresultinoverfittingandlimitedeffectiveness.Therefore,inthispaper,weproposeaweightedsyntheticminorityover-samplingtechniquetoaddresstheaboveissues.
2.RelatedWork
2.1TraditionalOver-SamplingTechni