提取磨削主轴型转子轴承系统在加速度期间振动信号的特征研究外文文献翻译中英文翻译.docx

提取磨削主轴型转子轴承系统在加速度期间振动信号的特征研究外文文献翻译中英文翻译.docx

《提取磨削主轴型转子轴承系统在加速度期间振动信号的特征研究外文文献翻译中英文翻译.docx》由会员分享，可在线阅读，更多相关《提取磨削主轴型转子轴承系统在加速度期间振动信号的特征研究外文文献翻译中英文翻译.docx（9页珍藏版）》请在冰豆网上搜索。

提取磨削主轴型转子轴承系统在加速度期间振动信号的特征研究外文文献翻译中英文翻译

附录Ⅱ

Proceedingsofthe7thICFDM2006

InternationalConferenceonFrontiersofDesignandManufacturing

June19-22,2006,Guangzhou,China

Pages255-260

ASTUDYONVIBRATIONSIGNAL-BASEDFEATUREEXTRACTION

FORGRINDINGSPINDLE-TYPEDROTOR-BEARINGSYSTEM

DURINGACCELERATION

Jong-KweonPark,Bong-SukKim,Soo-HunLeeandJun-YeobSong

IntelligenceandPrecisionMachineryResearchDivision,KIMM,305343,Rep.ofKorea

SchoolofMechanicalEngineering,AjouUniversity,443749,Rep.ofKorea

Abstract:

Thegoalofsystemmonitoringistominimizeeconomicloss,toincreasereliability,tomaximizeproductivity,andtomaintainproductqualityinmanufacturing.Sincevibrationsignalssufficientlycontaintheabundant

runninginformationoftherealsystemandthehiddenfaultsymptoms,thefeatureextractionthroughthosesignalsiswidelyappliedforperformanceevaluationfaultdiagnosticsofrotatingmachineries.

Thispapershowsfeatureextractionfromvibrationsignalsgatheredinthegrindingspindle-typedrotor-bearingsystemduringaccelerationinordertomonitoranabnormalconditionofcurrentsystemlikeshaftcrackbyusingvariouskindsofsignalprocessingmethodssuchastheFastFourierTransform,Short-TimeFourierTransform,Wigner-VilleDistribution,andDiscreteWaveletTransform.Aswell,theresultoffeatureextractioninshaftcrackconditionwascomparedwiththatinnormalcondition.

Keywords:

Featureextraction,Grindingspindle-typedrotor-bearingsystem,Non-stationarysignalprocessingmethod,Acceleratingprocess,Wavelettransform

1.Introduction

Theconditionmonitoringorfaultdiagnosisinrotatingmachineriesandmachiningprocessisacrucialrequirementinordertomaintainreliability,safety,andproductqualityandtopreventfailuresordamages.Comparedwithothermachiningmethods,high-performancegrindingprocessisoneofthemostcomplicatedandimportantcuttingprocessesasfinalmachiningstage;consequently,themonitoringofgrindingprocessandmachineismuchmorenecessaryinordertosupervisetheprocessandmachineandalsodetectabnormalities.Amongvariouskindsofapproaches,vibrationsignalanalysismethodforfeatureextractionandnondestructivedamageidentificationhasbeenwidelyutilizedduetocapabilitytocarrytheabundantdynamicinformationandtoindicatedetailedmotionofmechanicalsystemsandtodescribesimultaneouslywhenafaultoccursorwhatisitsfrequency.However,sincemostofthevibrationsignalssampledonmechanicalsystemsarenon-stationaryortransientsignalswhichsufficientlycontainadditionalinformationorabnormalsymptom,whichcannotberevealedfromstationarysignal,itisthekeyhowtoaccuratelydrawdominantfeaturecomponentsfromvibrationsignalsbecausenon-stationarysignalismorecomplexthanstationarysignal.Uptodate,forfeatureextractionofrotatingmachinery,manykindsofresearchresultshavemainlybeenfocusedonthestationarysignalprocess;ontheotherhand,littleresearchhasbeenaccomplishedforthenon-stationarysignalprocesssuchasspeed-upprocess;especially,thereisalmostnofeatureextractionusingvibrationsignalofspeed-upconditioninthefieldofgrindingprocess.

.Thispaperwasaboutastudytoextractthedominantfeaturesfromvibrationsignalsacquiredinalaboratorygrindingspindle-typedrotor-bearingsystemduringaccelerationbyusingseveralsignalprocessingmethodssuchasTimeDomainAnalysis（TDA）,Frequency

DomainAnalysis（FDA）,andtheTime-FrequencyAnalysisMethod（TFAM）.Modaltesting,whichdetectsdynamiccharacteristicsofthesystemlikenaturalfrequency,wasperformedforthepurposeofdeterminingoperatingrangeforaccelerationintestsetup.Vibrationdatafromthebearinghousingpassingthroughthedistinctiveresonancefrequenciesandfrequencybandinspeed-upprocessweregatheredthroughtheexperimentswithnormalandcrackshaftcondition.Togetprominentsignalsofabnormalityfromacquiredtimedataasafundamentalstagefordiagnosisormonitoringtechnology,theFastFourierTransform（FFT）,Short-TimeFourierTransform（STFT）,Wigner-Villedistribution（WVD）,andWaveletTransform（WT）usingcommercialsoftwarewerecarriedoutandcomparedwitheachresult

2.TheoreticalBackground

2.1.ReviewofSignalAnalysisMethods

Therearetwomajortypesofsignalinthefirstnaturaldivisioncategory:

thestationarysignalandnon-stationarysignal.Stationarysignalsareconstantintheirstatisticalparametersovertime.Moreover,stationarysignalsarefurtherdividedintodeterministicandrandomsignals.

Randomsignalsareunpredictableintheirfrequencycontentandtheiramplitudelevel,buttheystillhaverelativelyuniformstatisticalcharacteristicsovertime.

Ontheotherhand,non-stationarysignalsaredividedintoc