航空发动机气路参数偏差值模型分析与应用-analysis and application of aero-engine gas path parameter deviation model.docx

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2018-05-28 发布于上海
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航空发动机气路参数偏差值模型分析与应用-analysis and application of aero-engine gas path parameter deviation model.docx

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航空发动机气路参数偏差值模型分析与应用-analysis and application of aero-engine gas path parameter deviation model

AbstractAeroengineisregardedastheheartofaircraft.Toenhanceengineconditionmonitoringnotonlyhelptoimprovethetransportaircraftsafety,butalsotoreduceenginemaintenancecosts.Therearemanyaeroengineconditionmonitoringtechnology.Inallofthem,thegaspathparametermonitoringtechnologyisveryimportant.Thismethodgetgaspathparameterdeviationthroughparameterdeviationmodel.Andthen,gettheengineperformancestatusaccordingtothetrendofgaspathparameterdeviation.Atlast,forecastgaspathparameterdeviationforpre-diagnosis.Obviously,itisnecessarytoresearchonaeroenginegaspathparameterdeviationmodelanditsapplication.Inordertogettheaeroenginegaspathparameterdeviationmodel,thedeviationmodelshouldbebrokendownintothreeparts:thestandardizedmodel,thebaselinemodelandthecompensationmodel.Firstly,theengineconditionmonitoringsystemoforiginalenginemanufacturerwereanalyzed,andthefunctionexpressionofthebaselinemodelandthestandardizedmodelwasobtainedbyexperiment.Andthen,thecompensationmodelwasestablishedonSupportvectormachinemethod.Optimizetheparametersofsupportvectormachineswithparticleswarmoptimization.Finally,actualdatawasusedtovalidatethedeviationmodel.Theapplicationshowsthatthemodelerrorissmall,andabletomeettheactualrequirements.TheoriginalvalueofenginegaspathparameterdeviationwhichSNR(SignaltoNoiseRatio)isverylowhasmanyoutlier.Andthetrendsisdifficulttoobserve.It,snecessarytodetectoutlierandsmoothfortheoriginalvalueofdeviation.First,outliersdetectionalgorithmisproposedonsecondexponentialsmoothingmethod.Thentheevaluationindicatorfortheprocessingofdeviation.optimizationmodelforevaluationindicatortooptimizethesmoothingcoefficientandsignificancelevelparameter.Anditssolvedwithparticleswarmoptimization.Finally,themethodwasusedtodetectoutlierandsmoothfortheexhaustgastemperaturedeviation.Theapplicationshowsthattheeffectofthismethodisgood.Thepredictionofgaspathparameterdeviationisessentiallyatimeseriesprediction.Firstthetime-varyingfuzzyinferencesystemtheorywasestablished.Andthencreateatime-varyingfuzzyneuralnetworkt