预览加载中,请您耐心等待几秒...
1/3
2/3
3/3

在线预览结束,喜欢就下载吧,查找使用更方便

如果您无法下载资料,请参考说明:

1、部分资料下载需要金币,请确保您的账户上有足够的金币

2、已购买过的文档,再次下载不重复扣费

3、资料包下载后请先用软件解压,在使用对应软件打开

基于共振稀疏分解的滚动轴承故障诊断方法研究的中期报告 Abstract Inmechanicalequipment,therollingbearingisoneofthemostcommonlyusedcomponents,anditsfailurewillcauseseriousconsequences.Therefore,itisimportanttostudyareliableandeffectivefaultdiagnosismethodforrollingbearings.Basedontheresonantsparsedecompositionmethod,thisstudyproposesarollingbearingfaultdiagnosismethodforrotatingmachinery.Throughtheoreticalanalysisandsimulationexperiments,itisfoundthatthismethodcaneffectivelydiagnoserollingbearingfaults. Introduction Rollingbearingsarewidelyusedinvarioustypesofmachinery,suchaswindturbines,motors,andgearboxes.Thefailureofrollingbearingswillcauseseriousconsequences,includingincreasedmaintenancecosts,reducedequipmentefficiency,andevensafetyhazards.Therefore,itisessentialtostudyeffectiveandreliablefaultdiagnosismethodsforrollingbearings. Duetothecomplexityofrollingbearingsystems,itischallengingtoaccuratelydiagnosefaults.Withthedevelopmentofsignalprocessingtechnologies,severalmethodshavebeenproposedforrollingbearingfaultdiagnosis.However,mostofthesemethodsrelyonaprioriknowledgeofthefaultcharacteristicsandrequireextensivedatapreprocessing.Therefore,itisnecessarytodevelopafaultdiagnosismethodthatdoesnotdependonpriorknowledgeandcaneffectivelyextractfaultfeatures. Resonantsparsedecompositionisanewmethodinsignalprocessingthatcanextractthesparsecomponentsofsignalsandeffectivelysuppressnoise.Basedonthismethod,thispaperproposesarollingbearingfaultdiagnosismethodthatdoesnotrelyonpriorknowledge. Methodology Thebasicprincipleoftheresonantsparsedecompositionmethodistodecomposeasignalintotwoparts:asparsecomponentandanon-sparsecomponent.Thesparsecomponentiscomposedofafewsignificanttime-frequencyatoms,whilethenon-sparsecomponentrepresentstheremainingpartofthesignal.Thesparsecomponentcaneffectivelyextractthefeatureinformationofthesignal,whilethenon-sparsecomponentmainlyrepresentstheinterferenceandnoiseinthesignal. Toapplytheresonantsparsedecompositionmethodtorollingbearingfaultdiagnosis,wefirstcollectvibrationsignals