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基于LSTM算法的电子部件故障预测
1.Introduction
Withthedevelopmentofscienceandtechnology,electronicdeviceshavebecomeanindispensablepartofpeople'sdailylives.However,theseelectronicdevicesmayoccasionallyfail,resultinginseriousconsequences,especiallyinthefieldsofaerospace,military,andmedicalindustries.Therefore,predictingandpreventingequipmentfailureinadvancehasbecomeanimportantresearchdirection.
LSTM(LongShort-TermMemory)algorithmisarecurrentneuralnetworkmodelwithstrongmemoryandpredictioncapabilities.Itiswidelyusedinvariousfields,suchasnaturallanguageprocessing,imagerecognition,andtimeseriesprediction.Inrecentyears,LSTMalgorithmhasalsoshowngoodperformanceinelectroniccomponentfaultprediction.
2.RelatedWork
ManyscholarshaveconductedresearchonelectroniccomponentfaultpredictionbasedonLSTMalgorithm.Forexample,Chenetal.(2020)proposedanLSTM-basedpredictionmodelforbearingfaults.Theresultsshowthatthemodelcaneffectivelypredictthefaultofthebearing.Lietal.(2018)usedtheLSTMalgorithmtopredicttheequipmentfailureofpowertransmissionandtransformationdevices.Theexperimentalresultsshowthatthemodelhasgoodpredictionaccuracyandcanprovideguidanceforequipmentmaintenance.
3.DataPreprocessing
BeforeapplyingtheLSTMalgorithmtofaultprediction,itisnecessarytopreprocessthedata.Thedatapreprocessingmainlyincludesdatacleaning,featureextraction,andnormalization.Inthispaper,weusethefaultdataofelectronicdevicesprovidedbyacertainenterpriseforanalysis.
Firstly,weremovethemissingdataandabnormaldatatoensurethereliabilityofthedata.Then,weselecttherelevantfeaturesinthedata,suchasvoltage,current,signalfrequency,andwaveform,astheinputvariablesoftheLSTMmodel.Finally,wenormalizethedatatoensurethattheinputdataofthemodelsatisfiesthestandardnormaldistribution.
4.LSTMModel
TheLSTMalgorithmisaspecialRNN(RecurrentNeuralNetwork)modelthathasbetterperformanceinprocessingsequencedata.ComparedwithtraditionalRNNmodels,theLSTMmodelhasamorecomplexstructureandcanmaintainthelong-termdependenceofsequences.Moreover,theLSTMmodelcandistinguishbetwee