三相異步電動(dòng)機(jī)故障診斷中英文翻譯、外文翻譯、外文文獻(xiàn)翻譯

1、翻譯部分英文原文FaultDiagnosisofThreePhaseInductionMotorUsingNeuralNetworkTechniquesAbstract:Faultdiagnosisofinductionmotorisgainingimportanceinindustrybecauseoftheneedtoincreasereliabilityandtodecreasepossiblelossofproductionduetomachinebreakdown.Duetoenvironmentalstressandmanyothersreasonsdifferentfaultso

2、ccurininductionmotor.Manyresearchersproposeddifferenttechniquesforfaultdetectionanddiagnosis.However,manytechniquesavailablepresentlyrequireagooddealofexpertisetoapplythemsuccessfully.Simplerapproachesareneededwhichallowrelativelyunskilledoperatorstomakereliabledecisionswithoutadiagnosisspecialistto

3、examinedataanddiagnoseproblems.Inthispapersimple,reliableandeconomicalNeuralNetwork(NN)basedfaultclassifierisproposed,inwhichstatorcurrentisusedasinputsignalfrommotor.ThirteenstatisticalparametersareextractedfromthestatorcurrentandPCAisusedtoselectproperinput.Dataisgeneratedfromtheexperimentationons

4、peciallydesigned2Hp,4pole50Hz.threephaseinductionmotor.Forclassification,NNslikeMLP,SVMandstatisticalclassifiersbasedonCARTandDiscriminantAnalysisareverified.RobustnessofclassifiertonoiseisalsoverifiedonunseendatabyintroducingcontrolledGaussianandUniformnoiseininputandoutput.IndexTerms:Inductionmoto

5、r,Faultdiagnosis,MLP,SVM,CART,DiscriminantAnalysis,PCAI.INTRODUCTIONINDUCTIONmotorsplayanimportantroleasprimemoversinmanufacturing,processindustryandtransportationduetotheirreliabilityandsimplicityinconstruction.Inspiteoftheirrobustnessandreliability,theydooccasionallyfail,andunpredicteddowntimeisob

6、viouslycostlyhencetheyrequiredconstantattention.Thefaultsofinductionmotorsmaynotonlycausetheinterruptionofproductoperationbutalsoincreasecosts,decreaseproductqualityandaffectthesafetyofoperators.Ifthelifetimeofinductionmachineswasextended,andefficiencyofmanufacturinglineswasimproved,itwouldleadtosma

7、llerproductionexpensesandlowerpricesfortheenduser.Inordertokeepmachinesingoodcondition,sometechniquesi.e.,faultmonitoring,faultdetection,andfaultdiagnosishavebecomeincreasinglyessential.Themostcommonfaultsofinductionmotorsarebearingfailures,statorphasewindingfailures,brokenrotorbarorcrackedrotorend-

8、ringsandair-gapirregularities.Theobjectiveofthisresearchistodevelopanalternativeneuralnetworkbasedincipientfault-detectionschemethatovercomethelimitationsofthepresentschemesinthesensethat,theyarecostly,applicableforlargemotors,furthermoremanydesignparametersarerequestedandespeciallyconcerningtolongt

9、imeoperatingmachines,theseparameterscannotbeavailableeasily.Ascomparedtoexistingschemes,proposedschemeissimple,accurate,reliableandeconomical.Thisresearchworkisbasedonrealtimedataandsoproposedneuralnetworkbasedclassifierdemonstratestheactualfeasibilityinarealindustrialsituation.Fourdifferentneuralne

10、tworkstructuresarepresentedinthispaperwithallkindsofperformancesandabout100%classificationaccuracyisachieved.II.FAULTCLASSIFICATIONUSINGNNTheproposedfaultdetectionanddiagnosisschemeconsistsoffourproceduresasshowninFig.1:Datacollection&acquisitionFeatureextractionFeatureselectionFaultclassificationDa

11、taCollectionandDataacquisitionInthispaperthemostcommonfaultsnamelystatorwindinginterturnshort(I),rotordynamiceccentricity(E)andbothofthem(B)areconsidered.BTFFig.1.GeneralBlockDiagramofproposedclassifierForexperimentationanddatagenerationthespeciallydesigned2HP,threephase,4pole,415V,50Hzinductionmoto

12、risselected.ExperimentalsetupisasshowninFig.2.Fig.2.ExperimentalSetupTheloadofthemotorwaschangedbyadjustingthespringbalanceandbelt.ThreeACcurrentprobeswereusedtomeasurethestatorcurrentsignalsfortestingthefaultdiagnosissystem.Themaximumfrequencyofusedsignalwas5kHzandthenumberofsampleddatawas25OO.From

13、thetimewaveformsofstatorcurrentsasshowninFig.3,noconspicuousdifferenceexistsamongthedifferentconditions.Healthy(H)ysB-llmEcc,rrtncrt,Tli*MWVWt口ntmieiBmH伯jFig.3.ExperimentalWaveformsofStatorcurrentFeatureExtractionThereisaneedtocomeupwithafeatureextractionmethodtoclassifyfaults.Inordertoclassifythedi

14、fferentfaults,thestatisticalparametersareused.Tobeprecise,samplestatisticswillbecalculatedforcurrentdata.Overallthirteenparametersarecalculatedasinputfeaturespace.Minimumsetofstatisticstobeexaminedincludestherootmeansquare(RMS)ofthezeromeansignal(whichisthestandarddeviation),themaximum,andminimumval

15、uestheskewnesscoefficientandkurtosiscoefficient.Pearsonscoefficientofskewness,gdefinedby:2WhereXdenotesmean,xdenotesmedianandSdenotesthesamplestandardXdeviation.Thesamplecoefficientofvariationvisdefinedby;rSvXTherthsamplemomentaboutthesamplemeanforadatasetisgivenby;m2denotesspreadaboutthecenter,m3re

16、ferstoskewnessaboutthecenter;m4denoteshowmuchdataismassedatthecenter.Second,thirdandfourthmomentsareusedtodefinethesamplecoefficientofskewness,gandthesamplecoefficientofkurtosis,gqasfollows.g3g4Q)Thesamplecovariancebetweendimensionsjandkisdefinedas;(x-x)(x-x)ijjikkC=4jk(n-1)andk,risdefinedas;jkcr=jk

17、(7)jkS-SjkFeatureSelectionBeforeafeaturesetisfedintoaclassifier,mostsuperiorfeaturesprovidingdominantfault-relatedinformationshouldbeselectedfromthefeatureset,andirrelevantorredundantfeaturesmustbediscardedtoimprovetheclassifierperformanceandavoidthecurseofdimensionality.HerePrincipalComponentAnalys

18、is(PCA)techniqueisusedtoselectthemostsuperiorfeaturesfromtheoriginalfeatureset.PrincipalComponents(PCs)arecomputedbyPearsonrule.TheFig.4isrelatedtoamathematicalobject,theeigenvalues,whichreflectthequalityoftheprojectionfromthe13-dimensionaltoalowernumberofdimensions.po1J廠-L=二=-_=0,Vi.NiwhereG(,b2rep

19、resentsaGaussianfunction,Nisthenumberofsamples,aareasetofimultipliers(oneforeachsample),(10)(11)J(x)=d(蘭daG(x-x,2b2)+b)iijjiji=1andM=ming(x).iiandchooseacommonstartingmultiplieralearningrate耳,andasmallthreshold.Then,whileiMt,wechooseapatternxandcalculateanupdateAa=q(1-g(x)andperformtheupdateTOC o 1-

20、5 h ziiiIfa(n)+Aa0iia(n+1)=a(n)+Aa(n)iii(12)(13)b(n+1)=b(n)+dAaiiAnd訐a(n)+Aal.ikeliliacid5:EsdiaidPtajHcin4:I:jrfrhaidLikclihckid5:匚ERT町ini6:CJtRTrTwciingFig.10(a).VariationofAverageClassificationAccuracyonTestingonTestdataandCVdatawithMethodandMeasureofTreesFig.lO(b).VariationofAverageClassificatio

21、nAccuracyonTestingonTestdataandCVdatawithDepthofTreesDiscriminantAnalysisDiscriminantanalysisisatechniqueforclassifyingasetofobservationsintopredefinedclasses.Thepurposeistodeterminetheclassofanobservationbasedonasetofvariablesknownaspredictorsorinputvariables.Themodelisbuiltbasedonasetofobservation

22、sforwhichtheclassesareknown.Basedonthetrainingset,thetechniqueconstructsasetoflinearfunctionsofthepredictors,knownasdiscriminantfunctions,suchthatL二bx+bx+.+bx+c,where1122nnthebsarediscriminantcoefficients,thexsaretheinputvariablesorpredictorsandcisaconstant.DiscriminantanalysisisdoneusingXLSTAT-2009

23、.VariousmodelsarecheckedandresultsareshowninFig.11.ltisobservedthatoptimumaverageclassificationaccuracyontestingontestdataandCVdataisfoundtobe91.77and80percent,respectively.Titincn*T上口xta口H上in已c-nCDatM1:3:lirwu.T(l2:Slcjiwisc4:11旺kMiLidFig.11.VariationofAverageClassificationAccuracyonTestingonTestda

24、taandCVdatawithModelofDAIII.NOISESUSTAINABILITYOFCLASSIFIERSincetheproposedclassifieristobeusedinrealtime,wheremeasurementnoiseisanticipated,itisnecessarytochecktherobustnessofclassifiertonoise.Tochecktherobustness,C-UniformandGaussiannoisewithmeanvaluezeroandvariancevariesfrom1to20%isintroducedinin

25、putandoutputandaverageclassificationaccuracyontestingdatai.e.unseendataischecked.ItisseenthatSVMbasedclassifieristhemostrobustclassifierinthesensethatitcansustainbothuniformandGaussiannoisewith14%and20%varianceininputandoutput,respectively.ResultsareasshowninTableIG-GaussianNoiseU-UniformNoiseTVlll.

26、l.IEFFECTOFNOEEObiAVERACECLASJilFICATinNAHriJRACYWlIk：N(；I.TSSIIILRriSTI：l)ONTKSTlMtJDATANNModelMLI”SVMNeri聊inItijhjIItijiuIOufiu4%VajianssAverageCkissilldticiTicmTc?slingcmDatLijclI)a1aTypecilNciiweuGU匚1.1u1iaaioaiaa100iaaiaaiaaiaa2iaa100iaa100iaaiaaiaaiaa3iaa100Kid100100aa661004iaaioaiaa10066.7iaa

27、iaaiaa5jaaiaa66.766.7iaaiaa6iaa100Kid100100aaaa1007iaaioaiaa100iaaiaaiaaiaa*jaaiaaiaaiaaiaaiaa9iaaioaiaa100iaaiaaiaaiaaiaiaaioaiaa100iaa66.7iaaiaait75iaaiaaiaaiaaiaa12iaaioaiaa100iaaiaaiaaiaa13laaiaaiaai(n66.766.7iaaiaa14100IGOiaa100iaaiaoiaaiaa15iaa75iaa10066.7iaaiaa1615iaa5a5a66.766.7iaaiaa1775757

28、57566.766.7iaa66.71甘iaaioa755a33333.3iaaiaa19laaiaa7575iaaiaaiaa66.72aiaa62755Q3333.3ma100IV.RESULTSANDDISCUSSIONInthispaper,theauthorsevaluatedtheperformanceofthedevelopedANNbasedclassifiersfordetectionoffourfaultconditionsofthreephaseinductionmotorandexaminedtheresults.MLPNN,andSVMareoptimallydesi

29、gnedandaftercompletionofthetraining,thelearnednetworkistestedtodetectdifferenttypesoffaults.SimilarlystepsizeisvariedinSVMand0.7stepsizeisfoundtobeoptimum.TheseconfirmourideathattheproposedfeatureselectionmethodbasedonthePCAcanselectthemostsuperiorfeaturesfromtheoriginalfeatureset,andtherefore,isapo

30、werfulfeatureselectionmethod.Alsoproposedclassifierisenoughrobusttothenoise,inthesensethatclassifiergivessatisfactoryresultsforUniformandGaussiannoisewith14%varianceininputandwith20%varianceinoutput.ComparativeresultsareshowninFig.12andTableII.fDIJJnuloyuo-p呂一.一顯呂。c01Do5-Fig.l2.Comparativeanalysisof

31、variousclassifierw.r.t.Averageclassificationaccuracy.TABLEIICOMPARATIVERESULTSOFNNBASEDCLASSIFIERST-TimercquiivdTierepciclipereKcniiptninmsNumbercil匚tunnediim伺商違侶Pk7rl(BinajiceTetlinganTcwl【如口TetlingciiiCVIhtiTwMojcOliscTwdMin.DeservedAverageSOMax.CJhjflmedMin.fJhsmedAverageSOi.-JMSE0207157a.aai773C

32、LCM業(yè)J；OJ06550.1267J7&0035d(20.0413cun殆44RcjiccjHCinvclnc(n9S254.14423aaKL133339.2222右.04甘4PCPTCCTTlC5MSE0.09926a.asasOJOS9150.0(.0941340.054S40.061920.007Q.6932fi4RCTCCITtCcirrednew(nSMJSS99.611.944oaSK.SS9S.7223514中文譯文基于神經(jīng)網(wǎng)絡(luò)技術(shù)的三相異步電動(dòng)機(jī)故障診斷摘要：異步電機(jī)故障診斷在工業(yè)中十分重要，因?yàn)樾枰岣呖煽啃院徒档陀捎跈C(jī)器故障造成的生產(chǎn)損失。由于環(huán)境壓力和許多其他的原因使

33、異步電動(dòng)機(jī)發(fā)生不同的故障。許多研究者提出了不同的故障檢測和診斷技術(shù)。然而，目前許多技術(shù)需要提供良好的專業(yè)知識才能在應(yīng)用中獲得成功。更簡單的方法是可以使用相對不熟練的操作在不需要診斷專家仔細(xì)考察數(shù)據(jù)和診斷問題的情況下做出可靠的判斷。本文提出了簡單，可靠，經(jīng)濟(jì)的基于神經(jīng)網(wǎng)絡(luò)（NN）的故障分類，其中電機(jī)定子電流作為輸入信號。從定子電流中提取13個(gè)參數(shù)并使用PCA來選擇合適的輸入。數(shù)據(jù)來自于特別設(shè)計(jì)的2馬力、4極50HZ的三相異步電動(dòng)機(jī)試驗(yàn)。為了分類，如神經(jīng)網(wǎng)絡(luò)像MLP、SVM和基于CART的統(tǒng)計(jì)分類器以及判別分析歐進(jìn)行了驗(yàn)證。對噪聲分類器的魯棒性的驗(yàn)證，也通過在輸入和輸出引進(jìn)高斯和統(tǒng)一控制進(jìn)行了驗(yàn)證

34、。關(guān)鍵詞：異步電動(dòng)機(jī)，故障診斷，MLP，SVM，CART，判別分析，PCA引言異步電機(jī)作為主驅(qū)動(dòng)設(shè)備在生產(chǎn)、工業(yè)和運(yùn)輸中由于其可靠性和結(jié)構(gòu)簡單發(fā)揮重要作用。盡管由于它們的穩(wěn)定性和可靠性，它們偶爾也會發(fā)生故障和意外停機(jī)，造成很大的損失。因此，它們需要不斷的關(guān)注。感應(yīng)電動(dòng)機(jī)的故障不僅會導(dǎo)致產(chǎn)品的運(yùn)作中斷，而且增加成本，降低產(chǎn)品質(zhì)量，影響操作人員的安全。如果延長異步電機(jī)壽命和提高生產(chǎn)線效率,這將花費(fèi)更少的生產(chǎn)費(fèi)用，使終用戶可以以更低的價(jià)格購買。為了保持機(jī)器的狀態(tài)良好，一些技術(shù)，如故障監(jiān)測，故障檢測和故障診斷變得越來越重要。感應(yīng)電動(dòng)機(jī)的最常見的故障是軸承故障，定子繞組故障，轉(zhuǎn)子斷條或氣隙不合理。本研究

35、的目的是發(fā)展一種替代人工神經(jīng)網(wǎng)絡(luò)的早期故障檢測計(jì)劃，克服在這個(gè)方案上的局限性，目前的方法很昂貴，對于大型電動(dòng)機(jī)適用，而且許多設(shè)計(jì)參數(shù)要求，特別是涉及到長時(shí)間運(yùn)作的機(jī)器，這些參數(shù)不能提供方便。相對于現(xiàn)有的方案，這項(xiàng)方案很簡單，準(zhǔn)確，可靠和經(jīng)濟(jì)。這項(xiàng)研究工作是基于實(shí)時(shí)數(shù)據(jù)等，提出基于神經(jīng)網(wǎng)絡(luò)分類器顯示一個(gè)真正的產(chǎn)業(yè)狀況的實(shí)際可行性。本文提出四個(gè)不同神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)在與各種各樣的表現(xiàn)，并且達(dá)到100%分類準(zhǔn)確性。用神經(jīng)網(wǎng)絡(luò)分析故障如圖.1所顯示，提出的故障檢測和診斷計(jì)劃包括四個(gè)步驟：1數(shù)據(jù)的收集與采集特征提取特征選擇故障分類數(shù)據(jù)收集和數(shù)據(jù)采集在本中最常見的故障，即定子繞組匝間短路（1），轉(zhuǎn)子動(dòng)態(tài)偏心（E

36、）和二者皆有（B）。TflinedClassifterET圖1一般結(jié)構(gòu)圖用于實(shí)驗(yàn)和數(shù)據(jù)生成而特別設(shè)計(jì)的2馬力，三相，四極，415V,50Hz的異步電動(dòng)機(jī)已選好。所做實(shí)驗(yàn)設(shè)圖2所示。圖2實(shí)驗(yàn)裝置調(diào)整彈簧秤和傳送帶改變馬達(dá)的裝載。三個(gè)交流電流探針，用于測量定子測試故障診斷系統(tǒng)的電流信號。最大使用頻率信號是5kHz，被抽樣的數(shù)據(jù)的數(shù)量是2500。定子電流波形圖3所示，在不同的條件下，沒有明顯的差異存在。Ec匚crrtricitvCEj1JU_nilHI1LJWwvw口-0Timr:hwwi訶4亠圖3定子電流實(shí)驗(yàn)波形特征提取有必要得出一個(gè)根據(jù)提取特征判斷分類故障的方法。為了對不同的故障，使用了統(tǒng)計(jì)參量

37、。確切地說，對當(dāng)前的數(shù)據(jù)進(jìn)行統(tǒng)計(jì)將會得出樣品資料。全部13個(gè)參數(shù)作為輸入特征計(jì)算?？疾斓淖钚〗y(tǒng)計(jì)量包括平均信號（這里是標(biāo)準(zhǔn)差）的均方根（RMS）,最高和最低值的偏斜系數(shù)和峰度系數(shù)。皮爾遜的偏斜系數(shù)，g2定義為：(1)其中X表示平均值，X表示位數(shù)，S表示樣本標(biāo)準(zhǔn)差。樣本變化參數(shù)v定義為:xrSX(2)數(shù)據(jù)的樣本平均數(shù)在rth采樣時(shí)刻的值為:m表示中心的范圍，m是指對中心偏度；m表示中心集合的數(shù)據(jù)數(shù)量。其次，第三和234第四的時(shí)刻是用來定義樣本偏度系數(shù)的g和樣品的峰度系數(shù)g如下：434(4)(5)尺寸之間的樣本協(xié)方差j和k是指(X-X)(X-X)(6)jjikkC=-4-jk(n1)普通關(guān)聯(lián)系數(shù)j

38、和k，r被定義為：7)cr二jkjkS-Sjk特征選擇在將一個(gè)特征送入分類器前，最明顯的故障提供相關(guān)的信息優(yōu)勢，應(yīng)從設(shè)置功能中選擇功能不相干的或多余的功能必須被丟棄，以提高分類器的性能，避免維度的危害。這里的主成分分析(PCA)技術(shù)用于從原來的特征中選擇最優(yōu)的特征。主成分(PCs)的計(jì)算由皮爾遜規(guī)則完成。圖4與一個(gè)數(shù)學(xué)對象有關(guān)，即特征值，這個(gè)特征值反映了從13個(gè)維到一個(gè)較低維的投影質(zhì)量。圖4主成分，特征值和百分比變化故障分類（1）基于多層感知器神經(jīng)網(wǎng)絡(luò)分類器LC簡單的多層感知器（MLP）神經(jīng)網(wǎng)絡(luò)，作為一個(gè)故障分類。四個(gè)處理單元輸出層中使用的電機(jī)有四個(gè)條件，即合理，匝間故障，偏心這兩種故障。如圖

39、5所示的結(jié)果，選擇5個(gè)項(xiàng)目合作安排為輸入，因此在輸入層PE的數(shù)量為5。iMEEanTriuX*廠111114.5D.431.3D.I32DJ31.1DCSSuniitcicTFCs詔血5*圖5（a）微型和小型企業(yè)的平均變化對培訓(xùn)和電腦的數(shù)量作為輸入.G-Entb-U2OE-更一&墨$0,Viwh,.N(9)iiii=1其中2)是一個(gè)高斯函數(shù)，N為樣本數(shù)，a是一個(gè)乘數(shù)集(每個(gè)樣品1個(gè))iJ(x)=dCdaG(x-x,2a2)+b)iijjiji=1(10)及M=ming(x)(11).11選擇一個(gè)常見的起始乘數(shù)a、學(xué)習(xí)速率耳，和一個(gè)小的閾值。然后，當(dāng)Mt,我們選一個(gè)模i式x和一個(gè)校驗(yàn)Aa=n(1

40、-g(x)，執(zhí)行校驗(yàn)。TOC o 1-5 h ziii如果a(n)+Aa0iia(n+1)=a(n)+Aa(n)iiib(n+1)=b(n)+dAa(12)ii如果a(n)+Aa0iia(n+1)=a(n)iib(n+1)=b(n)(13)之后，只有一部分不為于零(稱為支撐向量)。這是很容易實(shí)現(xiàn)核算法從g(x)開始可以計(jì)算i算法各局部乘數(shù)，在輸入文件中可得到所需的反應(yīng)。事實(shí)上,表現(xiàn)為多元化g(x)是錯(cuò)誤的,i所以它可以激活被包括在這個(gè)框架中的神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)。核心算法是RBF網(wǎng)絡(luò)的本質(zhì)使其輸出測試由：f(x)=sgn(daG(x-x22)一b)iiiiicspportvectors與成本函數(shù)的誤

41、差準(zhǔn)則J(t)1工(d(t)-(tanh(y,(t)221i=1(14)(15)圖7(a)電腦輸入的培訓(xùn)和變形系數(shù)的均方誤差的變化TiiilBaljUCVDaUSTBtDiix電腦的信息都將輸入以及步長通過檢查平均最低均方誤差與平均分類精度萊選擇，結(jié)果如圖7所示。圖7(b)電腦輸入的檢測試驗(yàn)數(shù)據(jù)、訓(xùn)練數(shù)據(jù)、變形系數(shù)的平均分類精度的變化基于支持向量機(jī)分類器的設(shè)計(jì),規(guī)格：輸入數(shù)：5步長：0.7每個(gè)時(shí)間的樣本：0.693ms數(shù)量的連接權(quán)：264設(shè)計(jì)分類器進(jìn)行訓(xùn)練和測試使用類似數(shù)據(jù)和計(jì)算結(jié)果顯示在圖8和圖9圖8最小均方誤差的變化對測試的平均測試數(shù)據(jù)，變形系數(shù)數(shù)據(jù)和訓(xùn)練數(shù)據(jù)的轉(zhuǎn)移(n)圖9最小均方誤差變

42、化的平均訓(xùn)練變異系數(shù)多種多樣的集合分類和樹狀結(jié)構(gòu)分類和樹狀結(jié)構(gòu)是特征空間的劃分的過程，是二進(jìn)制遞歸的數(shù)據(jù)集。所產(chǎn)生的樹木包括內(nèi)部節(jié)點(diǎn)和終端節(jié)點(diǎn)或葉節(jié)點(diǎn)。第一階段叫樹的構(gòu)造，第二階段是樹修剪分類樹，開發(fā)利用XLSTAT-2009各種各樣的方法、措施和最大的樹的深度,結(jié)果顯示如圖10。研究表明,優(yōu)化平均分類精度進(jìn)行測試的試驗(yàn)數(shù)據(jù)分別是90.91%和80%。JT*rtDalaTetiunEonfDati1:Pcomvi2:Chiidl.ilsdilicKKJ3:EK(7haid-Ptajscm4:I:(7io.kJLikdihciad5:匚星RT町ini6:CJtRT-Twaing7:Out?si占

43、弓Mrly一顯-c圖10(a)平均分類精度的變化對測試試驗(yàn)數(shù)據(jù)和變異系數(shù)數(shù)據(jù)的方法與措施oooa口ooo口口口C5J1匚二丸口冷-lT*!-込1DA圖10(b)平均分類精度的變化對測試試驗(yàn)數(shù)據(jù)和變異系數(shù)數(shù)據(jù)與深度的樹狀結(jié)構(gòu)(4)判斷分析判別分析對一組觀察數(shù)據(jù)進(jìn)行預(yù)定義的技術(shù)。其目的是要確定一個(gè)觀察組作為一個(gè)已知輸入變量或預(yù)測變量的基礎(chǔ)。該模型建立了一套已知的觀測數(shù)據(jù)。該套觀測有時(shí)被稱為基于訓(xùn)練集的培訓(xùn)，這項(xiàng)技術(shù)建立了一套線性函數(shù)的預(yù)測因子，叫做判別函數(shù)，即L二bx+bx+.+bx+c，b是判別系數(shù),，x為輸入變量或預(yù)測因子即c為常數(shù)。這些1122nn判別函數(shù)用于預(yù)測一個(gè)未知的新觀測類。用XLS

44、TAT-2009判別分析各種模式檢測與結(jié)果如圖11所示，觀察表明，優(yōu)化平均分類精度進(jìn)行測試的試驗(yàn)數(shù)據(jù)分別是91.77%和80%。I:Slcjiwisc-kirwo-id3:Icirward2:Slcjiwise13-ajcJcwaid4：圖11平均分類精度的變化對檢測試驗(yàn)數(shù)據(jù)與模型的數(shù)據(jù)和變化系數(shù)3.分類器的噪音可持續(xù)性由于提出的分類器，用于實(shí)時(shí)情況，測量噪聲情況是非常必要的。檢查系統(tǒng)的穩(wěn)定性,高斯噪聲方差值為零，輸入和輸出從1變化到20%,并且測試平均分類精度的數(shù)據(jù)。它是基于支持向量機(jī)分類器的分類，具有較強(qiáng)的穩(wěn)定性，它能維持兩者統(tǒng)一和高斯噪聲的輸入和輸出方差在14%和20%之間，結(jié)果顯示見表1。G-高斯噪音U-均衡噪音TAI1I.I：IIJTIiCTCH-NOI