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1、StudyonFaultDiagnosisofGearboxBasedonNoiseReductionbyParticleFilterAbstractGearboxsystemiscommonlyusedinthetransmissionofrotatingmachineryequipment,whichperformancefineorbadisdirectlyrelatedtoqualityoftheentiredevice.Ithasgreatsignificancetomonitoritsstate,detectfaultstim
2、elyandclassifythefaults,whichalsocanpreventunnecessaryloss.Generally,samplingsignalismingledwithstrongbackgroundnoise,whichsomeusefulsignalmaybecoveredforthebadlyenvironmentofgearboxworking.Soitneedstocarryoutnoisereductionbeforefailureanalysis-Particlefilteringisanewmode
3、l-basedstateestimationtechnique.Studyingtheprincipleofparticlefilterin-depththenuseittothenoisereductionofgearvibrationaccelerationsignals.Providedthatthesignalmodelandnoisestatisticsisknowwhenuseparticlefiltertechnologytodenoise.Inthispaper,it'srealizedasfollows:firstly,
4、establishtimeseriesARmodelofthevibrationaccelerationsignal,thenthecoefficientsofthisARmodelastheparticlefiltercoefficientsofthestateequation;Usewavelettransformthresholdde-noisingmethodtoextractnoisesignalwhencomprehendtheprincipleofnoisereductionbywavelettheory.Thepartic
5、lefilterobservationequationwillbeusetheextractingnoisewhichassumedadditive.Basedontheabovetheoreticalanalysis,thegearboxvibrationaccelerationsignalsampledthroughexperimentisanalyzedandprocessed.Thefirststepisdenoisedbyparticlefilter;secondstepisclassifyingthefaultmodebyBP
6、neuralnetwork.Asanadaptivepatternrecognitiontechnology,neuralnetworkhasbeenwidelyappliedinthefieldoffaultpatternrecognitionandthetheoreticalresearchismature.Inthispaper,weusetwosetsofdatatodiagnosefaultsbyBPneuralnetwork,ofwhichoneisdenoisedbyparticlefilter,andtheotherisn
7、ot.ExtractingtheenergyspectrumscalesastheBPneuralnetworkinputvector.Thediagnosisresultsshowthatthedatadenoisedbyparticlefilterisbetterthanotherafternetworktraining.Itisalsoconfirmedthattheeffectofnoisereductionbyparticlefilterisgood.Keywords：gearbox,diagnosis,particlefilt
8、er,neuralnetwork目录1绪论11.1课题研究背景和意义11.1.1课题来源11.1.2选题意义11.2齿轮箱故障诊断的研究现状和发展趋势11.2.1齿轮箱故障诊断的研究现状112
