基于深度自编码神经网络的滚动轴承故障诊断方法研究

基于深度自编码神经网络的滚动轴承故障诊断方法研究摘要

随着现代工业领域的高速发展，机械装置的可靠性和运行效率已成为工业生产的关键问题。滚动轴承故障是导致机械设备失效的主要原因之一，因此轴承故障的预测和诊断技术日渐受到关注。本文提出了一种基于深度自编码神经网络的滚动轴承故障诊断方法，以实现对滚动轴承故障状态的实时诊断。

首先，本文介绍了智能故障诊断系统的基本结构和方法流程，并分析了滚动轴承故障诊断的基本原理和方法。接着，结合实际工程案例，本文选择了振动信号作为输入数据，使用小波变换对信号进行特征提取，构建了基于深度自编码神经网络的故障诊断模型。进一步，本文使用归一化和降维技术进行数据预处理以提高模型训练效果。最后，本文通过对实验结果的分析，验证了本文所提出的基于深度自编码神经网络的滚动轴承故障诊断方法的有效性和优越性。

关键词：滚动轴承；故障诊断；深度自编码神经网络；小波变换；特征提取

Abstract

Withtherapiddevelopmentofmodernindustrialfield,thereliabilityandoperationefficiencyofmachinerydeviceshavebecomekeyissuesofindustrialproduction.Rollingbearingfailureisoneofthemaincausesofmechanicalequipmentfailure,sothepredictionanddiagnosistechnologyofbearingfaultsisgraduallyreceivingattention.Inthispaper,arollingbearingfaultdiagnosismethodbasedondeepautoencoderneuralnetworkisproposedtoachievereal-timediagnosisofrollingbearingfaultstate.

Firstly,thebasicstructureandmethodflowofintelligentfaultdiagnosissystemwereintroduced,andthebasicprinciplesandmethodsofrollingbearingfaultdiagnosiswereanalyzed.Then,combinedwithpracticalengineeringcases,thevibrationsignalwasselectedastheinputdata,andwavelettransformwasusedforfeatureextractionofthesignaltoconstructthefaultdiagnosismodelbasedondeepautoencoderneuralnetwork.Furthermore,datapreprocessingusingnormalizationanddimensionalityreductiontechniqueswasperformedtoimprovethemodeltrainingefficiency.Finally,throughtheanalysisoftheexperimentalresults,theeffectivenessandsuperiorityoftherollingbearingfaultdiagnosismethodbasedondeepautoencoderneuralnetworkproposedinthispaperwereverified.

Keywords:rollingbearing;faultdiagnosis;deepautoencoderneuralnetwork;wavelettransform;featureextractionRollingbearingsarekeycomponentsinmanymechanicalsystems,andtheirhealthconditiondirectlyaffectstheoverallperformanceandreliabilityofthesystem.Faultdiagnosisofrollingbearingsisthereforeofgreatimportanceforensuringthesafeandefficientoperationofmechanicalsystems.Inrecentyears,manyresearchstudieshavebeenconductedtodevelopeffectiveandreliablemethodsforrollingbearingfaultdiagnosis.

Inthispaper,anewmethodforrollingbearingfaultdiagnosisbasedondeepautoencoderneuralnetworkwasproposed.Themethoduseswavelettransformforsignalpreprocessingandfeatureextraction,andadeepautoencoderneuralnetworkforfaultdiagnosis.Thedeepautoencoderneuralnetworkisatypeofartificialneuralnetworkthatconsistsofmultiplelayersofhiddenunits,andisabletolearncompactandhierarchicalrepresentationsofinputdata.

Theproposedmethodwasevaluatedusingreal-worlddatafromarollingbearingtestrig.Theexperimentalresultsdemonstratedthattheproposedmethodachievedhighaccuracyinrollingbearingfaultdiagnosis,andoutperformedseveralstate-of-the-artmethods.Thisindicatesthatthedeepautoencoderneuralnetworkisapowerfultoolforrollingbearingfaultdiagnosis,andhasthepotentialtobeappliedinvariousindustrialapplications.

Inaddition,severalpreprocessingtechniqueswereappliedtotherawdatatoimprovethetrainingefficiencyofthemodel.Normalizationwasusedtoscaletheinputdatatoacommonrange,anddimensionalityreductiontechniquessuchasprincipalcomponentanalysiswereusedtoreducethedimensionalityofthefeaturespace.Thesetechniqueshelpedtoreducethecomputationalcomplexityofthemodel,andimproveitsgeneralizationability.

Inconclusion,theproposedrollingbearingfaultdiagnosismethodbasedondeepautoencoderneuralnetworkisapromisingapproachforimprovingthereliabilityandefficiencyofmechanicalsystems.Themethodhasseveraladvantagesovertraditionalmethods,includinghighaccuracy,robustness,andscalability.FutureworkwillfocusonfurtherrefiningthemethodandapplyingittoothertypesofmechanicalsystemsFurthermore,theproposedmethodcanbeenhancedbycombiningitwithothermachinelearningtechniques,suchassupportvectormachinesordecisiontrees,tofurtherimprovetheaccuracyofthediagnosis.Additionally,themethodcanbeextendedtohandlemultiplefaultsanddetectearlysignsofwearandtearinmechanicalsystems.Thiscouldgreatlyincreasethereliabilityandlifespanofthesesystems,leadingtoimprovedperformanceandreducedmaintenancecosts.

Anotheravenueforfutureresearchistoinvestigatetheuseoftransferlearningforfaultdiagnosis.Transferlearningisatechniquewhereapre-trainedmachinelearningmodelisusedasastartingpointfortraininganewmodelforadifferenttask.Thisapproachcanbeparticularlyusefulinscenarioswherelimitedlabeleddataisavailablefortrainingthemodel.Byusingpre-trainedmodels,themodelcanlearntorecognizefeaturesthatarerelevanttothenewtaskmorequicklyandaccurately.

Overall,theproposedmethodhasthepotentialtorevolutionizethewaymechanicalsystemsarediagnosedandmaintained.Itoffersamoreefficientandaccurateapproachtofaultdiagnosis,whichcanleadtoimprovedsystemreliability,reducedmaintenancecosts,andincreaseduptime.Withfurtherresearchanddevelopment,thismethodcouldbeappliedtoawiderangeofmechanicalsystems,includingthoseusedinindustrial,transportation,andenergyapplicationsInadditiontothebenefitsoutlinedabove,theproposedmethodcouldalsocontributetomoresustainablepracticesinvariousindustries.Bydetectingfaultsandaddressingthembeforetheyescalateintomoreseriousissues,mechanicalsystemscanoperatemoreefficientlyandconsumelessenergy.Thisisparticularlyimportantinindustriesthatrelyheavilyonmechanicalsystems,suchasmanufacturing,transportation,andenergyproduction,whereenergyconsumptionhasasignificantimpactontheenvironment.

Moreover,theproposedmethodcouldalsoleadtoimprovementsinthedesignanddevelopmentofmechanicalsystems.Byanalyzingthedatacollectedduringthediagnosisprocess,engineerscangaininsightsintotheperformanceofthesystemandidentifyareasforimprovement.Thiscouldresultinmoreeffectiveandreliablemechanicalsystemsthatcanoperateathigherefficienciesandwithlowermaintenancerequirements.

Anotherpotentialapplicationoftheproposedmethodisinthefieldofpredictivemaintenance.Bycontinuouslymonitoringmechanicalsystemsandanalyzingthedatacollected,itmaybepossibletopredictwhenafaultislikelytooccurandtakepreventativeactionbeforeithappens.Thiscouldfurtherreducedowntimeandmaintenancecostswhileimprovingsystemreliability.

However,therearealsosomechallengesthatneedtobeaddressedinorderfortheproposedmethodtobewidelyadopted.Onepotentialchallengeisthecostofimplementingthenecessarysensorsanddataprocessingsystems.Additionally,thereisaneedforspecializedexpertisetointerpretthedataanddiagnosefaultsaccurately.Therefore,theremaybeaneedforinvestmentintrainingandeducationtodeveloptheseskillsandcapabilities.

Inconclusion,theproposedmethodhasthepotentialtotransformthewaymechanicalsystemsarediagnosed,ma