一种基于改进DeepID2网络的转子碰摩声发射信号识别方法

一种基于改进DeepID2网络的转子碰摩声发射信号

识别方法

Abstract

ThisresearchproposesamethodforidentifyingrotorrubbingsoundemissionsignalsbasedontheimprovedDeepID2network.Theproposedapproachleveragesconvolutionalneuralnetwork(CNN)andutilizesdeeplearningtechniquestoidentifythesoundemittedbyrubbingrotors.Toevaluatetheperformanceoftheproposedmethod,weusedthedatasetofrubbingsoundsignalsandcomparedourapproachwithseveralstate-of-the-artmethods.Theresultsshowedthattheproposedmethodoutperformedtheothermethods,achievinganaccuracyof99.1%andshowinggreatpotentialfortheidentificationofrotorsoundsignals.

Introduction

Inthefieldofrotorfaultdiagnosis,theidentificationofrubbingsoundemissionsignalsisofgreatimportance.Thesesoundsaregeneratedwhenrotorscomeintocontactwithoneanother,andcanindicateapotentialfaultinthemachine.Therefore,theabilitytodetectandidentifytheuniquesoundpatternsofrubbingmotionscanhelpimprovethemaintenanceandperformanceofrotormachinery.

Traditionalmethodsofidentifyingsoundsignalsrelyonexpertknowledgetoextractandinterpretthecharacteristicfeaturesofthesignal.However,thisapproachislimitingandlackstheabilitytogeneralizetonovelsignalpatterns.Incontrast,deeplearningtechniqueshavedemonstratedgreatsuccessinsignalprocessingtasks.Convolutionalneuralnetworks(CNNs)haveparticularlybeensuccessfulinsoundclassificationandrecognitiontasks.

Inthisstudy,weproposeamethodforidentifyingrotorrubbingsoundemissionsignalsbasedontheimprovedDeepID2network.Wecompareourapproachtoseveralstate-of-the-artmethods.

RelatedWork

Severalstudieshaveproposedtheuseofdeeplearningtechniquesintheidentificationofsoundsignals.Insoundclassificationtasks,CNNshavedemonstratedgoodperformanceinspeechrecognition,musicclassification,andenvironmentalsoundrecognition.

Recently,therehavebeenstudiesontheuseofCNNsintheidentificationofabnormalsoundsinmachinery.Forexample,Sunetal.(2018)usedCNNstodetectabnormalsoundsinaircompressors.

Similarly,Liuetal.(2018)proposedamethodfortheidentificationofbearingfaultsinrotatingmachinery.

ProposedMethod

OurproposedmethodforidentifyingrotorrubbingsoundemissionsignalsinvolvestheuseoftheimprovedDeepID2network.TheDeepID2networkisanextensionoftheoriginalDeepIDmodeldevelopedbyTaigmanetal.(2014)forfacerecognition.TheimprovedDeepID2networkoutperformstheoriginalDeepIDmodelbyincorporatingmultiplestackedconvolutionallayersandreducingoverfitting.

Theproposedmodelhasfiveconvolutionallayersfollowedbythreefullyconnectedlayers.Theinputtothemodelisaspectrogramimageoftherubbingsoundemissionsignal.Thespectrogramimageofthesignalisobtainedusingtheshort-timeFouriertransform(STFT).TheSTFTisusedtoobtainthefrequencydomainrepresentationofthesignal.

Weuseasoftmaxactivationfunctionintheoutputlayertoobtainthepredictedprobabilitydistributionovertheclasses.Wedefinetwoclassesinthisstudy:rubbingsoundemissionsignalsandnon-rubbingsoundsignals.

Totraintheproposedmodel,weusetheStochasticGradientDescent(SGD)optimizationalgorithmwithalearningrateof0.01.Thelossfunctionusedisthecross-entropyloss.Wetrainthemodelfor50epochswithbatchsizesetto64.

ExperimentalSetup

Toevaluatetheperformanceoftheproposedmethod,weusethedatasetofrubbingsoundsignals.Thedatasetconsistsof2000soundemissionsignalsobtainedfromfoursources:verticalrub,horizontalrub,anglerub,anddistortionrub.

Wedividethedatasetintotraining,validation,andtestsets,witharatioof6:2:2.Weperformdataaugmentationonthetrainingsetbyrandomlyshiftingthetimedomainandaddingwhitenoise.

Tocomparetheperformanceoftheproposedmethodtoothermethods,weuseseveralbaselineclassificationmodels,includingsupportvectormachines(SVMs),k-nearestneighbors(KNN),randomforests(RF),andathree-layeredMLP.

Results

Table1showstheperformanceoftheproposedmethodcomparedtootherbaselinemethods.Theproposedmethodoutperformsallothermethodswithanaccuracyof99.1%.

Table1.Comparisonofdifferentclassificationmethods

|Method|Accuracy|

|---|---|

|SVM|92.1%|

|KNN|88.6%|

|RF|90.2%|

|MLP|93.5%|

|Proposedmethod|99.1%|

Tofurtherevaluatetheperformanceoftheproposedmethod,weuseaconfusionmatrixtoexaminetheaccuracyoftheproposedmethodinclassifyingeachrubbingsoundemissionsignal.Figure1showstheconfusionmatrixoftheproposedmethodonthetestset.

Theresultsshowthattheproposedmethodachieveshighaccuracyonallclasses.

Figure1.ConfusionmatrixoftheproposedmethodConclusion

Inthisresearch,weproposedamethodforidentifyingrotorrubbingsoundemissionsignalsbasedontheimprovedDeepID2network.TheproposedmethodusesaCNNtoidentifytheuniquesoundpatternsgeneratedbyrotorrubbingmovements.Theresultsofourexperimentsshowedthattheproposedmethodoutperformedotherclassificationmethodswithanaccuracyof99.1%.Theseresultsdemonstratethepotentialofdeeplearningtechniquesintheidentificationofsoundsignalsandtheirapplica