奇异谱分解和最大相关峭度解卷积在轴承故障声学诊断中的应用

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关键词：复合故障；滚动轴承；奇异谱分解；最大相关峭度解卷积 中图分类号： TH165+.3 ；TH133.3 文献标志码：A DOI：10.16385/j.cnki.issn.1004-4523.202508013

Abstract：Theefectiveseparationofaultfeaturecomponentsisthecoreofrolingbearingcompositefaultdiagnosis.Underthe backgroundofstrongnoiseandmutualinterferenceandcouplingbetweenvarious faults，theacousticcompositefaultdiagnosisof rolingbearingisverychalenging.Inthispaper，acompositefaultacousticdiagnosis methodbasedonoptimizedsingularspectrum decomposition（SSD）and parameter adaptive maximum corelated kurtosis deconvolution（MCKD） is proposed.The envelope kurtosis isusedasanindicatortoassistSSDtoquicklydeterminetheoptialdecompositionlevel，soas toovercometheuertaintyoftheartificialempiricaldeterminationofthedecompostionlevelanddecomposethesignalintoultiplesingularspectralomponents.Combiningtheratiooffaultcharacteristicfrequencyenergyamplitudeasanindex，thetwosingularspectralcomponents containing themain faultcharacteristic informationareadaptivelyselected.The parameteradaptive MCKDisusedtofilterth se lectedoptimalcomponentandenhancethesignalfeature，andthefaultfeature frequencyis extractedbyenvelope spectrumanalysis torealizefaultdiagnosis.Theeffectivenessoftheproposedmethodisverifiedbythesimulationandexperimentalacousticsigalsof roling bearings.The research provides a new means for the composite fault diagnosis of rotating machinery.

Keywords：compound fault；rolling bearing；singular spectrumdecomposition;maximumcorelationkurtosis deconvolution

滚动轴承作为现代动力传动系统最重要的部件之一，已广泛应用于航空航天、交通运输、海洋工程等领域。（剩余14519字）