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柴油机飞轮机械加工工艺规程及工装设备设计,柴油机,飞轮,机械,加工,工艺,规程,工装,设备,装备,设计

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中北大学分校2007届本科毕业设计说明书第1页共2页110柴油机飞轮壳机械加工工艺规程及工艺装备设计摘要飞轮壳式发动机上一个非常典型的零件，它一方面挂靠在柴油机缸体的后端面用于储能飞轮的防护罩；另一方面，它也是柴油机离合器壳的连接支撑部件。其外观和内在质量及影响着柴油的整体的美观程度，也影响着柴油机的整体性能。110柴油机飞轮壳的主要结构特点是形状复杂，壁薄且不均匀，加工的部位多，加工难度大，各个加工面和加工孔均要求较高的精度。本文首先根据零件的特点对零件进行了分析，为制定工艺路线作了基础。其次，确定了零件的生产纲领、类型和毛坯的制造形式。依据第一章的分析拟定了两条工艺路线，对其进行了比较分析，综合其优缺点按照先基准后其它，先粗后精，先主后次的原则确定了加工方案，并对工序进行了详细设计。110柴油机飞轮壳的夹具根据该零件的特点设计，该零件属于薄壁类零件，在裝夹过程中极易变形，因而影响精度甚至破坏零件。两套工装采用了恰当的定位和夹紧，既保证了零件的精度又确保了零件的完好加工，同时减少辅助工时，提高了劳动生产率。本文对飞轮壳进行了详细的工艺分析及研究，对本产品的工艺及其工装的设计探索出了一条切实可行的新路。关键词飞轮壳工艺方案夹具设计AbstractFlywheelhousingontheengineisaverytypicalparts,ontheonehand,itanchoredcylinderdieselengineaftertheendofflywheelenergystorageforprotectiveenclosures;theotherhand,ItisalsotheconnectingsupportcomponentsofthehousingofDieselclutch.Theexternalandinternalqualityofflywheelhousingeitherimpactontheoveralldieselbeautifuloraffecttheoverallperformanceofdieselengines.themainstructureoftheflywheelhousingof110dieselengineisthecomplexshape,thinwallanduneven,processingsite,difficultprocess,varioussurfaceprocessingandprocessingKonghavehigheraccuracy.Inthispaper,thedevelopmentofrouteswerebasedonthecharacteristicsofcomponentsoftheanalysiscomponents.Secondly,theidentificationofthepartsoftheproductionprogram,thetypeandmanufacturerofroughform.Basedonthefirstchapteroftheanalysisoftworoutes,itscomparativeanalysis,Accordingtoitscomprehensiveadvantagesanddisadvantagesofotherfirstbenchmark,thefirstaftercruderefined,afterthefirst中北大学分校2007届本科毕业设计说明书第2页共2页meetingofthemainprinciplesofthemachiningprogram,aswellasproceduresforthedetaileddesign.thefixturepartsoftheflywheelhousingof110dieselengineaccordingtoofthedesigncharacteristicsofthethin-walledpartsisparts.Clampingintheprocessvulnerabletodeformation,thusaffectingtheaccuracyorevendamagedparts.Equipmentusedtwosetsoftheappropriatepositioningandclamping,ensuringtheaccuracyofthecomponentsalsoensuretheintegrityofthepartsprocessing,Auxiliarywhilereducingworkinghours,improvedlaborproductivity.Inthispaper,theflywheelhousingwasmadeadetailedtechnicalanalysisandresearch.thetechnologyandequipmentofadesignwasexploredanewpracticablepathforthisproduct.Keyword:Flywheelhousing、TechnologyProgram、machining,designsuits中北大学分校2007届本科毕业设计说明书第1页共21页AnIdentificationModelofHealthStatesofMachineWearBasedonOiLAnalysisFUjun-qing,lihan-xiong,suanxin-hua1.SchoolofAutomobile&MechanicalEngineringChangshaUnivcrsityofSciene&TechnologyChangsha410076,P.R.China2.SchoolofMechanicalandElectricalEngineeringCcntralSouthUniversityChangsha410083,P.R.ChinaAbstractThispaperpresentsisamodelingprocedureforderivingasing1evaluemeasurebasedonaresgressionandamethodfordeterminingastatisticalthrehoIdvalueasidentificationcriterionofnomalorabnomalstatesofmachinewearArea1。numerica1exampleisexamfinedbythemethodandidentificationcriterionpresented.TheresultindicatethatthejudgmentsbythepresentedmethodsareBasicallyconsistentwiththerea1facts,andthereforethemethodandidentificationcriterionarecaluableforjudgingtheno;malstateofmachinewearbasedonoilanalysis.Keywords:oilanalysisrcgrcssionmodel,singlevalue,measure,andthresholdvalueIntroductionOilanalysishasbeenusedworldwideasamethodforreducingmaintenancecost,improvingrcliabilityandproductivityinvariousindustries1.Currently,mostoilanalyzersusethemethodsofatomicemfissionspectrometryopticalorelectronicmicroscopyandferrogmphyetctoconducttheoilanalysis.Theaimofoilanalysisistoevaluatetheconditionofthelubricationortheequipmentfromthe1ubricantoilsamplceofamachine.,andrecommendmaintenanceactionstotheequipmentopemtingactivity.Withoutdisassemblingthemachinetheoilsamplesofamachinecanbeacquiredaccordingtocertaregulations,andthroughanalysisoftheoilsampletheoilandmachineconditioncanbeevaluatedOriginalequipmentmanufacturers(OEM),lubricantsuppliersandoilanalysislabomtoriesprovidespecificguidelinesforvicarmetalconcentrationsintheoil.Theselimitsprovidegoodgencralguide1inesforinterpretingoilanalysisdata.Buttherearemanyelementsinoilanalysisdate,itisverydifficulttodirectlyjudgethewearstateaccordingtotheoilanalysisdata.Forengineeringapplication,asinglevalueindexormeasureisneededforidentifyingthe中北大学分校2007届本科毕业设计说明书第2页共21页statesofthemonitoredoilsamplesV.Macianetal(8)derived。generalexpressionoftherateofvicarfromengineoilanalysisdateanddefinedtheenginewearrate(Zer)asanindexZ.Theindexvalue(Z)wasusedtoevaluatethewearrateofanenginebeingnormalorabnormalbyreferencetoanormalwearrate(EMwr).Infactindexandreferenceindexshouldberandomvariablesofoilsamplesandnotexactvalues,thereforeastatisticalmodeloftheindexesisneededChunhuaZhaoetaldevelopedamodelbymeansofastepwisepluralisticregressionwhichdeletessomeinsignificantelementsorlinearlyrelateelementsinoilanalysisoriginaldate,andtransferstheoilanalysisdataintoasinglevalue.Thesinglevaluewasusedasajudgmentofthewearstate,buttherewasnothresholdorcriticalvalueusedasanidentificationcriterion.Thusforthevaluesofthesamplesfarfromthenormalstatevalue1orabnormalstatevalue2,itwasnotclearwhethertheybelongedtothenormalorabnormalstate.ThispaperfirstimprovesthemodelingprocedureinReference(9).fordorivingasinglevaluemeasure，basedonaregressionmodeandthenpresentsamethodtodetermineastatisticalthresholdvalueasanidentificationcriterionofanormalorabnormalstate,Arealnumericalexampleisexaminedbythemethodandanidentificationcriterionarepresented.Theresultsindicatethatthejudgmentsbythepresentedmethodsarebasicallyconsistentwiththetruefacts,andthereforethemethodandidentificationcritcrionisvaluableforjudgingthenormalora5normalstateofmachinewearfromoilsamples.2Modelingprocedure2.1ExperimentdesignandsampleRegularlyorirregularlycollectandanalyzeoilsamplestoobtaintheconcentrationofvariouselementsinoilsamples.Inmeantime,thoroughlyinspectanddeterminethehealthstatesofmachineweatbymeansofothermethodssuchasdissemblingmachine,measuringdebrisshapesandetcInthispaper，thehealthstateissimplyofbinary-value,ie.normalandabnormalOncesufficientdataarecollected,theexperimentisstoppedandsinglevaluemodelwillbebuilt2.2ModelingTheobserbvedhealthstatesaredefinedasfollows中北大学分校2007届本科毕业设计说明书第3页共21页Ontheotherhand,they-valueeventuatedbyamodelwillbearealnumbercolseto1to2。Whereyisafunctionofconditionvatiablessuchasconcentrationsofweardebris,ie.y=g(x1,x2,xn)Initiallyconsiderthefollowingregressionmodelsy=a0+(1)niix1Whereaistheregressioncoefficient,xtheconcentrationofelements,atheinterruption,andnthenumberofelementsintheoilanalysis.TheabovemodelregressioncanbecompletedinExcel,whichisapartofMicrosoftOfficeAccordingtotheregressionmodel(1),somecoefficientsareinsignificantintheregressionmodel.Inordertostressthesignificantelementsofthemodelasmuchaspossible,someinsignificantelementsshouldbedeletedfromthemodel.Theinsignificantelementsareindicatedbyp-valuesinExcelIfthep-valuesarelarge,itislikelythatthepossibilityoftherelatedelementregressioncoefficientsiszero,andwherethep-valuesaresmallerthepossibilityislessInthepaperthep-value0.1istakenasasignificantcriterionofelements,whichmeansthatthepossibilityofregressioncoefficientofasignigicantelementbeingzerowillbelessthan10%.Theproceduretodeletealloftheinsignificantelementsisasfollows.Step1Regressallofelementsofoilanalysis,andoutputthep-valuesofallelementsCheckthep-valuesandselectanelementrelatedtothemaximump-value.Step2Deletetheelementsrelatedtothemaximump-value.Againregresstheleftelementandoutputthep-valuesoftheelementsCheckthep-valuesandselectanelementrelatedtothemaximump-value.Repeatstep2untilthep-valuesoftheremsiningelementsarealllessthan0.1.Atthistime,themodelingprocedureisendedandtheresultmodelisy=a0+(2)miixa1中北大学分校2007届本科毕业设计说明书第4页共21页Althoughthestatevariavleyofthemodel(1)isonlybinarystates1and2,thevaluesoftheoutputyoftheresultmodel(2)willgenerallynotbeexactly1and2.Iftheoutputvaluesarelessthan1,thestateywillbelongtonormalandiftheoutputvaluesaremorethan2,thestateywillabnomal.Butifthevaluesarebetween1and2,itisvaguewhetherthestatesarenormalorabnormal.Thereforeathresholdvalueisneededtojudgethestatesoftheoutputvalues.3DeterminationofthethresholdvalueOncethemodelisbuilt,accordingtotheknownnormalandabnormalstateofvariabley,allsamplescanbedividedintothetwosub-samples(normalandabnormal),whichcanbetransferredintotwosingle-valuesamplesofyintermsoftheresultingmodel(2).Consideringthatthetwosingle-valuesamplesarefromtheresultingmodel,soitisreasonablethatbothofthesingle-valuesamplesobeyanormaldistribution.FitthemintotwodistributionfunctionsfA(y)andfN(y),anddeterninethemeans)andstandardNA,(deviations()ofthesefunctionsasinFigure1.NA,Figure1Thepossibledistributionfunctions(PDF)ofnormalandabnormalgroupsandthethresholdvalueForanyofyvaluesfromtheresultingmodel(2),itsiaproblemtobesolvedthatitbelongstonormalorabnormal.Forthisreasonathresholdvalueneedstobedeterminedwhichisacriticalvalueofyanddenotedas.Foranyvalue,therearetwotypesofjudgmenterrors.(a)Normalstatesiwronglyjudgedasanabnormalstatewiththeprobabilitu.1-FN(yc)(b)Abnormalstateiswronglyjudgedasanormalstatewiththeprobability.FA(yc)Thesumoftheerrorsisgivenby中北大学分校2007届本科毕业设计说明书第5页共21页S(yc)=1-FN(yc)+FA(yc)(3)WhereFA(yc)andFN(yc)are,respectively,theprobabilityfunctionofanormalstateandtheprobabilityfunctionofanabnormalstate.Forminimizingjudgmenterrors,itisobviousthatthevalueyisoptimallydeterminedbyminimizing.TheexistenceoftheminimalvalueyhasSeenprovedintheAppendix.AccordingtotheAppendixthethresholdvalueycanbeeasilydetermined.Nowgivenanobservation,wecancalculateayvalueusingthedevelopedmodel(2)andcompareitwiththethresholdvalue.Inthiswaythemonitoredmachinesstatecanbedetermined4NumericalexampleDataoftheexamplefromReference9isshowninTable1,whichcontains8elements(A1,Cu,Si,Pb,Cr,Mn,Ni,Fe).and1statevariable(State).FortheobserveddataofTable1themodelingprocedureisdescribedasfollowsAtfirstusing(1),weCanfindthatinsignificantelementsaresuccessivelyPbCrMnNiandFe,andthatA1CuandSiaresignificant.Forthe3elementsthep-valuesare,respectively,8.99，4.68and0.016472andtheyarefarlessthan0.1.Thuswe810610havetheregressedmodel中北大学分校2007届本科毕业设计说明书第6页共21页y=0.05166+0.549707Al0.19083Cu0.15495Si(4)Second,nowwecanusetheregressedmodel(4)tocomputethestatevaluesofsamplesanddividethesevaluesintotwogroupsbythemeansoftheknownnormalandabnormalstatesofsamples.Assumethatyvaluesforanygroupfollowthenormaldistribution.WehaveOncewehavetwodistributionsandthoseparameters,wecanoptimallyfindthethresholdvaluereferringtotheAppendix.Theresultis=14354withthewrongjudgmentprobability=397%.Thecurveofthetotalwrong-judgmentpossibilityviathresholdvalueyisshowninFigure2Now,wecanchedkthepredicitonpowerofthemodel.Forthemodelingsamples,thevaluesofstatevariableycomputedbymodel(4)arelistedintheycolumnofTable1.ThejudgedresultsofcomparingyvalueswiththethresholdvaluearelistedinthejudgmentcolumnofTable1,thereisnowrongjudgmentforallsamples.Thisindicatesthatthethresholdvalue1.4354withthewrongjudgmentprobability=3.97%isreasonableandthattheabovemodelingprocedureisalsoreliable.Inordertoverifyfurtherthecorrectionofthemodel(4)anditsthresholdvalue,wecanchecktheother5testingsamples,thecheckedresultsofthe5samplesareshowninTable2.FromTable2,wecanseethattherestillarenowrongjudgmentsforallsamples.Therefore,wecantakeadvantageofthemodel(4)andthethresholdvaluetojudgewhetheranynewoilsamplesarenormalorabnormalBasedonthejudgments,somesuggestionsoractionsofmaintenance中北大学分校2007届本科毕业设计说明书第7页共21页canbeobtained,whichwillsavemorecostsofmaintenance.5Conclusionsanddiscussions(a)TheabovemodelingprocedureisanimprovedversionofReference9,whichcaneffectivelydeletetheinsignificantelementsofoilanalysisdata.TheregressionmoduleofExcelcanverysimplyfinishthemodelingprocedure.Theregressedmodelcantransfertheoilsamplesintosingle-valuestateindexes.(b)Consideringbinary-stateoutcomefortheobservations,amethodforoptimallydeterminingthethresholdvaluehasbeenproved.Anumericalexamplehasverifiedthatthejudgedresultsofthemodelingandtestingsamplesareconsistentwiththeoutcomeofobservations.(c)Theaboveapproachhasafeatureofcondition-basedmaintenance.Forexample,itcanbeusdetopredictwhenamonitoreditemwillreachthethresholdvalueandtakenecessaryactions.(d)Incaseofnotenoughsamples,thejudgmentcorrectioncanbeimprovedbymodelingthecombinationofoldsamplesandnewsamples,asmorenewsamplesareobtained.Thustheapproachcanbeconsummatedbyreplenishingmorenewsamples.(e)Itisnotedthatthejudgmentmaybewrongwhenthey-valueisclosetothethresholdvalue.Toavoidthis,anintervalincludingyshouldbefurtherdetermined,withinwhichthejudgmentneedstobeconfirmedbyafurthercheckorothermethods.Itisthenextworktomaketheapproachpergect.References1V.M.MartinezandB.T.Martinez,etalResultsandbenefitsofanoilanalysisprogrammerformilwaylocomotivedieselengines.InsightVol45,No6,pp.402406,2003中北大学分校2007届本科毕业设计说明书第8页共21页2GNolletandD.Prince,Rotatingequipmentreliabilityforsurfaceoperation,PartOilanalysisinamineCIMBulletinVol96,No1067,pp.8286,20033R.W.ChapmanD.JHodgesandT,JNowell,Microtomacro-weardebrisanalysisasaconditionmonitoringtoolInsightVol44.No,8,pp.498502,20024S.Berg,AstudyofsamplewithdrawalforlubricatedsystemsPart2:practicalsamplewithdrawalandselectionofpropersamplingmethods,IndustrialLubricationandTribology,Vol53,No.3,pp.97107,20015R,Ong,J.H.DymondR.D.FindlayandB.Szabados,Systematicpracticalapproachtothestudyofbearingdamageinalargeoil-ring-lubricatedinductionmachine.IEEETransactionsonIndustryApplications,Vol36,No6,pp.17151724,20006W.WangP.A.ScarfandM.A.J.Smith,Ontheapplicationofamodelofcondition-basedmaintenanceJournaloftheOperationalResearchSooiety,Vol51,No.11,pp.12181227,20007G.Fisher,DonalueAFilterdebrisanalysisasafirst-lineconditionmonitoringtoolLubricationEngineerng,Vol56,No2,pp.1822，20008V.Macian,B.Tormos,P.OlmedaandLMontoro,Analyticalapproachtowearratedeterminationforinternalcombustionengineconditionmonitoringbasedonoilanalysis.TribologyInternational,Vol36,NO10,pp.771776,20039C.H.Zhao,X.P.Yanetal.Thepredictionofwaremodelbasedonstepwisepluralisticregression。ProceedingsofInternationalConferenceonIntelligentMaintenanceSystem,Xian,China,pp.6672,Oct2003BriefbiographiesFujun-qingisnowanassociateprofessorofchangshauniversityofscienceandtechnology,hisresearchfieldisinmechanicalvibration,faultdiagnosis,signalanalysisandsoon.Lihan-xiongisnowaprofessorofcentralsouthuniversity,hisresearchfieldsisinfuzzycontrol,processesandintelligentcontrol,processidentification,andsoon.Xiaoxin-huaisnowanassociateprofessorofchangshauniversityofscienceandtechnology.Hisresearchfieldsisincombustionengineengineering,reliabilityandmechanicaldesign.中北大学分校2007届本科毕业设计说明书第9页共21页AppencixLettheprobabilityfunctionofnormalstatesamplegroupbeandtheprobabilityfunctionofabnormalstatesamplegroupFN(yc)=(A-1)dteN2)1(2FA(yc)=(A-2)tA2)1(ThenthefunctionofwrongjudgmentprobabilityisS(yc)=1-FN(yc)+FA(yc)S(yc)=1-+(A-3)dteN21(2dteA2)1(Inodertominimizethewrongjudgmentprobability,differentiatetheprobability(a-3)respecttoy,thus=+(A-4)cdyS)(dteN2)1(2dteA2)1(=(A-5)teN2)1(2tA2)1(Simplifyingtheaboveequation,wehave(A-6)22)()(AcNcyyAeThetwosidesofequation(a-6)areactedonwithain(*)functionandletin，thenRNAtheequation(a-6)becomes（A-RyyAcNc22)()(7）Simplifyingandcollectingtheaboveequation(a-7),wehave中北大学分校2007届本科毕业设计说明书第10页共21页(A-8)0222222NANAcANAcNARyyGenerally,themeansofnormalandabnormalsamplegroupsaredifferentandthemeanismorethan,thatisand0(A-15)acb42Infact,theconditionfromequation(a-13)canbesimplifiedasf=22(A-16)2AN228NANARForthejudgmcntcquation,if，R=ln.Itisobvious0(A-17)18222NANANARfAndif0(A-18)182222ANNANARfUntiltonow,wehavetheproofthatthereareonlyrealrootsinthesolution(a-14).Thereforebothandarerealroots.Theyarethetwoextremepointsofthefunctionofwrongjudgmentprobability(a-3).Accordingtothefigure1ofdistributions,wecandirectlyobservethatoneoftherootscorrespondstoamaximumvalueofprobabiity(a-3),anothertoaminimumvalue,andtheroottotheminimumvalueshouldusuallybelessthanandmoreAthenthusbasedonthetheseroots,wecandeterminetheminimumthresholdvalueofNwrongjudgmentprobabilityasfollowsIf，thenN1cyAc19AIf，thenN2cyA(A-20)c中北大学分校2007届本科毕业设计说明书第12页共21页基于油液分析的机械磨损状态识别模式付俊庆李汉雄肖新华1长沙科技大学，汽车与机械工程学院，长沙410076，P.R.china2中南大学，机电学院，长沙410083，P.R.china摘要，本文提供了一个建模过程，这个过程源于回归模型基础上的单值测量方法和用以确定临界值为正常或异常的标准机械磨损状态的统计方法。用这种算法和标准验算了一个实数例子。结果表明,基于油液分析机械磨损状态正常与否的判断方法和算法基本符合客观事实。关键词，油液分析退回模型单值测量和临界值1引言石油分析方法已成为各行各业在世界范围内用于降低维修成本、提高生产率和可靠性的方法。目前，大部分石油分析仪使用发射光谱、电子显微镜、光学或铁等方法进行石油分析。石油分析的目的是探讨从机械中提取润滑油样本所起的滑润作用或设备的条件和设备推荐维修经营活动的行动未拆机器,按照一定的关系能够获得机械的油样样本，并且通过分析油液的油液样本和机械状态来评估原设备厂商。润滑油供应者和油样分析实验室提供具体的具有指导性的在油样中磨损金属的含量。这些限制提供了良好的解读石油分析数据的一般准则。但在石油数据分析中还有许多因素，按照石油分析数据很难直接判断出机械的磨损状态。在工程应用中，在塞米松或测量中的单值对于检测油样状态是必要的。从设备油液分析数据中导出机械的磨损率，用以确定引擎的磨损率（zwr）记作Z.(z)的指数值用来评价引擎正常或异常工作的磨损率以参考一个正常的磨损率(EM)。事实上,指数和参考指数应该是石油样本的随机变量而不是确定的数值，因此,该指数的统计模型需要发展一个依靠多元逐步回归的模式，这个模式删去了一些在油液分析的原始数据中无关紧要的元素和线性相关的元素，使石油分析数据转换成了单值。这个单值作为磨损状态的判断依据，但没有门槛或临界值,作为这个值的鉴定标准。因此,样本的这个值远偏离于正常状态的值1或异常状态的值2，还不清楚他们是属于正常状态还是异常状态。本文首先完善了这样一个建模函数，它是参考了基于回归模型的单值测量。然后提出了一种确定阈值的统计标准作为辨识正常或异常状态的方法。一个实数例子被所提供的方法和坚定标准所检验。结果表明中北大学分校2007届本科毕业设计说明书第13页共21页有所提供的方法演算出的值基本符合客观事实，因此这个方法和判别标准对于从油样中判断在正常或异常状态下机械的磨损状态是有价值的。2模拟过程2.1实验设计与抽样通过定期或不定期的收集和分析石油样本来获得油样中的各种元素浓度。在此期间,通过例如拆卸机械，测量碎片形状等其他方法来检查和确定机械的磨损状态。在这篇文章中，健康状态用二进制数值来表示，也就是说，正常状态和异常状态。一旦足够的数据被采集到,这个实验就会中止，单值模型将被建立。2.2建模被观察的健康状态定义如下1，正常状态状态=2，异常状态在另一方面,y值经过一个模型的验算将要得到一个接近于1或2的实数。这里y值是各种状态的函数，例如磨粒浓度，即y=g(x1,x2,xn)初步考虑下列回归模型y=a0+(1)niix1这里是回归系数，是元素的浓度，是中断，是石油分析中的元素数量。以上的回归模型可以在Excel上完成,这是一个微软办公软件的一个部分。根据回归模型(1),在回归模型中的一些系数是微不足道的。为了尽可能多的压缩模型中的大量元素，一些无关紧要的元素应该从模型中删掉.这些无关紧要的元素在Excel中由p值决定。如果p值很大，很可能是因为相关元素的回归系数等于零，并且p值越小这种可能性越小。在这篇文章中，p值等于0.1被作为元素的一个重要基准，0.1意味着一个有意义的元素的回归系数为零的可能性不足10%。删除所有的无关紧要的元素的步骤如下。第一步退回石油分析中的所有元素，输出所有元素的p值。检查p值并选出涉及最大p值的元素。中北大学分校2007届本科毕业设计说明书第14页共21页第二步删除涉及最大p值的元素。再一次退回到最左边的元素并且输出所有元素的p值。检查p值并选出涉及最大p值的元素。重复第二步直到剩余元素的p值全都小于0.1。这时，建模过程被完成，建模结果是y=a0+(2)miixa1虽然模型1中的状态变量y只是状态1和2，但结果模型2的输出y值一般不是准确的1和2。如果输出值小于1，状态值y将属于正常状态。如果输出值大于2，状态值y将是异常的。但如果值介于1和2之间，状态是正常还是异常将要是模糊的。因此,需要一个临界值来判断该状态的输出值.2.3临界值的测定一旦模型被建立，按照已知的正常和异常状态下的变量y，所有样品可分为两个小组样品(正常与异常)，它根据计算模型2可以转换成两个单样本的Y值。考虑到两个单样品值来自计算模型，所以两个单值样本服从正态分布是合理的。把它们代入两个分布函数fA(y)和fN(y)，并确定如1图中这些函数的系数和偏差。并确定如图1中这些函数的系数)和偏差(),(NA,图1正常或异常组的可能分布函数(PDF)和临界值（阈值）对于从计算模式2中得出的任何一个y值都是用于解决是属于正常还是异常这个问题的。为此需要有一个临界值来决定关键值y并记作。对于任何值，都有两种判断错误的类型，(a)正常的状态可能被错误的判断为异常的状态1-FN(yc)或(b)异常的状态可能被错误的判断为正常的状态FA(yc)错误的总和被给出如下S(yc)=1-FN(yc)+FA(yc)(3)中北大学分校2007届本科毕业设计说明书第15页共21页这里FA(yc)和FN(yc)分别是正常状态下的可能函数和异常状态下的可能函数。为减少判断失误,显然y值最好通过减少的s值来决定。最小y值的存在被看作在附录中的证明。按照附录临界值能很容易的被确定。现在按照观察，我们能用模型2来计算出y值并把它和临界值相比较。用这种方法来确定检测机械的状态。2.4数值例子在参考9中的数据出示在表1中，它包含了8个元素(铝、铜、硅、铅、铬、锰、镍、铁)和1状态变量（状态）。对表1中的数据建模过程描述如下。表1铁元素样本的磨损条件模型首先，使用（1），我们可以发现,锰、镍、铬、铁是无关紧要的元素，而铜、铝、硅,都是重要的元素.对于这三种元素，p值分别是8.99，4.68和0.016472810610他们都远远小于0.1。因此，我们得到了回归模型y=0.05166+0.549707Al-0.19089Cu-0.15495Si(4)其次，我们能够使用回归模型（4）来计算出样品的状态值，并把其通过已知的样中北大学分校2007届本科毕业设计说明书第16页共21页本的正常和异常状态分成两组。假设y值符合以下任何一组的正态分布，我们将要有系数=1.026557偏差=0.198596用于正常组系数=1.84667偏差=0.200158用于异常值图2由临界值得出的错误可能性曲线图一旦我们有了两个分布和参数,我们可以参考附录找出最佳临界值.结果为=1.4354的错误判断可能性为3.97%由临界值得出的错误可能性曲线图如图2所示。现在，我们可以检查这个模型的建模能力。为样本建模，由模型（4）计算出的状态变量y的值被列在表1的y栏中。Y值与临界值比较相比较的判断结果被放在表1中的判断栏，对于所有的样本没有错误的判断结果。这表明临界值1.4354被错误判断的可能性是3.97%，是合理的。以上所有的建模过程也都是合理的。为了进一步核实模型（4）以及它的临界值的正确性，我们可以检查其他5个测试样本，5个样本的检测结果被出示在表2中。从表2我们可以看出，在所有的样本中仍然没有错误的判断结果。