基于深度自編碼神經(jīng)網(wǎng)絡(luò)的滾動(dòng)軸承故障診斷方法研究

基于深度自編碼神經(jīng)網(wǎng)絡(luò)的滾動(dòng)軸承故障診斷方法研究摘要

隨著現(xiàn)代工業(yè)領(lǐng)域的高速發(fā)展，機(jī)械裝置的可靠性和運(yùn)行效率已成為工業(yè)生產(chǎn)的關(guān)鍵問(wèn)題。滾動(dòng)軸承故障是導(dǎo)致機(jī)械設(shè)備失效的主要原因之一，因此軸承故障的預(yù)測(cè)和診斷技術(shù)日漸受到關(guān)注。本文提出了一種基于深度自編碼神經(jīng)網(wǎng)絡(luò)的滾動(dòng)軸承故障診斷方法，以實(shí)現(xiàn)對(duì)滾動(dòng)軸承故障狀態(tài)的實(shí)時(shí)診斷。

首先，本文介紹了智能故障診斷系統(tǒng)的基本結(jié)構(gòu)和方法流程，并分析了滾動(dòng)軸承故障診斷的基本原理和方法。接著，結(jié)合實(shí)際工程案例，本文選擇了振動(dòng)信號(hào)作為輸入數(shù)據(jù)，使用小波變換對(duì)信號(hào)進(jìn)行特征提取，構(gòu)建了基于深度自編碼神經(jīng)網(wǎng)絡(luò)的故障診斷模型。進(jìn)一步，本文使用歸一化和降維技術(shù)進(jìn)行數(shù)據(jù)預(yù)處理以提高模型訓(xùn)練效果。最后，本文通過(guò)對(duì)實(shí)驗(yàn)結(jié)果的分析，驗(yàn)證了本文所提出的基于深度自編碼神經(jīng)網(wǎng)絡(luò)的滾動(dòng)軸承故障診斷方法的有效性和優(yōu)越性。

關(guān)鍵詞：滾動(dòng)軸承；故障診斷；深度自編碼神經(jīng)網(wǎng)絡(luò)；小波變換；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