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1、ONCONDITIONMONITORINGOFEXHAUSTVALVESINMARINEDIESELENGINESxzzxxxT.L.Fog,L.K.Hansen,J.Larsen,H.S.Hansen,L.B.Madsen,P.Srensen,xxE.R.Hansen,andP.S.Pedersenx:Research&Development,MANB&WDieselA/STeglholmsgade41,DK-2450,CopenhagenSV,Denmark.E-mail:tof,hsh,lbm,pcs,er,psp@manbw.dkWebsite:www.manbw.dkz:
2、THORCenterforNeuroinformatics,DepartmentofMathematicalModeling,Bldg.321TechnicalUniversityofDenmark,DK-2800Lyngby,DenmarkE-mail:lkhansen,jl@imm.dtu.dkWebsite:eivind.imm.dtu.dk/thorAbstract.Experimentalinvestigationsofthepossibilitiestonon-intrusivelycharacterizeexhaustvalveconditionsinlargemarin
3、edieselengines,werecarriedoutonafourcylinder,500mmbore2-strokemarinedieselengineatMANB&WDiesel'sResearchCenterinCopenhagen,Denmark.Theexperi-mentscomprisedthreedierentvalveconditions,wheretwowereconcernedwitharticiallyinducedvalveburn-throughsituations.Theprimarypotentialmonitoringmeasurements
4、werevibrationandstructure-bornestresswaves,alsoknownasAcousticEmission(AE).ResultshaveshownthattheAEsignalshasamajoradvantageoverotherinvolvedsensors,indicatingsensitivitytobothmechanicaland
uid-mechanicalcombustionrelatedactivity.RecordeddatahasbeenpreprocessedandfeaturesextractedusingPrincipal
5、ComponentAnalysis(PCA).Fromanumberofappliedheuristicsandstatis-tics,asearchfortheoptimalsub-spaceofprincipalcomponentstouse,havebeencarriedout.Thechosenfeature-spacehavebeenusedforclassicationofinvolvedexhaustvalveconditionsbyapplyingbothregularizedfeed-forwardneuralclassiersandlineardiscrimin
6、ators.ThecomplexityoftheneuralnetworkshavefurtherbeenoptimizedbyOptimalBrainDamage(OBD)pruning,leadingtoincreasedgeneralization.INTRODUCTIONTheexhaustvalveisanimportantpartofacombustionengine.Itsmainpurposeistosealduringthecompressionstroke,therebyprovidingmaximumcombustionpressureformaximumengi
7、neperformance.Further,duringtheexpansionstrokeatengine-builderspecictiming,toliftitsseatandtherebyprovidescavenge(fromscavengeairsystem)ofthepollutedairinthecylinderandfreshairintakeforthefollowingcompressionandcombustion.V