摘要
Afaultdiagnosismodelisproposedbasedonfuzzysupportvectormachine(FSVM)combinedwithfuzzyclustering(FC).Consideringtherelationshipbetweenthesamplepointandnon-selfclass,FCalgorithmisappliedtogeneratefuzzymemberships.Inthealgorithm,sampleweightsbasedonadistributiondensityfunctionofdatapointandgeneticalgorithm(GA)areintroducedtoenhancetheperformanceofFC.Thenamulti-classFSVMwithradialbasisfunctionkernelisestablishedaccordingtodirectedacyclicgraphalgorithm,thepenaltyfactorandkernelparameterofwhichareoptimizedbyGA.Finally,themodelisexecutedformulti-classfaultdiagnosisofrollingelementbearings.Theresultsshowthatthepresentedmodelachieveshighperformancesbothinidentifyingfaulttypesandfaultdegrees.TheperformancecomparisonsofthepresentedmodelwithSVManddistance-basedFSVMfornoisycasedemonstratethecapacityofdealingwithnoiseandgeneralization.
出版日期
2013年03月13日(中国期刊网平台首次上网日期,不代表论文的发表时间)