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基于Autoencoder-BLSTM的涡扇发动机剩余寿命预测 Title:RemainingLifePredictionofTurbofanEnginesusingAutoencoder-BLSTM Abstract: Thepredictionofremaininglifeforcriticalcomponentsofturbofanenginesisofvitalimportanceforensuringtheproperfunctioningandsafetyofaircraftoperations.Traditionalprognosticmethodsoftenrelyonmanualfeatureengineeringandhavelimitedaccuracy.Inrecentyears,deeplearningapproacheshaveshownpromisingresultsinvariouspredictivemaintenanceapplications.Thispaperproposesanovelapproachbasedonthecombinationofanautoencoderandabidirectionallongshort-termmemory(BLSTM)networkforthetaskofpredictingtheremaininglifeofturbofanengines.Theproposedmethodaimstoimprovetheaccuracyofremaininglifepredictionwhilereducingthedependencyonmanualfeatureengineering. 1.Introduction 1.1Background Turbofanenginesarecrucialcomponentsofaircraftpropulsionsystemsandplayavitalroleinensuringthesafetyandefficiencyofflightoperations.Theaccuratepredictionoftheremaininglifeoftheseenginesisofparamountimportancetoavoidunforeseenfailuresandoptimizemaintenanceschedules.Traditionalapproachesforremaininglifepredictionoftenrelyonstatisticalandengineeringmodelsthatusemanuallyengineeredfeatures.However,thesemethodsoftensufferfromlimitedaccuracyandefficiency.Hence,thereisaneedformoreadvancedandeffectivetechniquesforremaininglifeprediction. 1.2Objective ThemainobjectiveofthisresearchistodeveloparobustandaccuratepredictionmodelfortheremaininglifeofturbofanenginesusingacombinationofautoencoderandBLSTMnetwork.Theproposedapproachaimstoeliminatethedependenceonmanualfeatureengineeringandimprovetheaccuracyofremaininglifeprediction. 2.Methodology 2.1DataCollectionandPreprocessing Inthisstudy,adatasetconsistingofsensorreadingsandcorrespondingremaininglifelabelsofturbofanenginesiscollected.Thecollecteddataispreprocessedbyremovingnoise,normalizingthesensorreadings,andsplittingitintotrainingandtestingsets. 2.2Autoencoder-BLSTMArchitecture Theproposedapproachinvolvestwomaincomponents:anautoencoderandaBLSTMnetwork.Theautoencoderisusedtole