礦井提升機(jī)外文翻譯

word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯外文翻譯部分：英文原文Mine-hoistfault-conditiondetectionbasedonthewaveletpackettransformandkernelPCAAbstract:Anewalgorithmwasdevelopedtocorrectlyidentifyfaultconditionsandaccuratelymonitorfaultdevelopmentinaminehoist.ThenewmethodisbasedontWaveletPacketTransform(WPT)andkernelPCA(KernelPrincipalComponentAnalysis,KPCA).Fornon-linearmonitoringsystemsthekeytofaultdetectionisthextractingofmainfeatures.Thewaveletpackettransformisanoveltechniqueofsignalprocessingthatpossessesexcellentcharacteristicsoftime-frequencylocalization.Itissuitableforanalyzingtime-varyingortransientsignals.KPCAmtheoriginalinputfeaturesintoahigherdimensionfeaturespacethroughanon-linmapping.Theprincipalcomponentsarethenfoundinthehigherdimensionfeaturespace.TheKPCAtransformationwasappliedtoextractingthemainnonlinearfeaturesfromexperimentalfaultfeaturedataafterwaveletpackettransformation.Tresultsshowthattheproposedmethodaffordscrediblefaultdetectionandidentification.Keywords:kernelmethod;PCA;KPCA;faultconditiondetection1IntroductionBecauseaminehoistisaverycomplicatedandvariablesystem,thehoistwillinevitablygeneratesomefaultsduringlong-termsofrunningandheavyloading.Thiscanleadtoequipmentbeingdamaged,toworkstoppage,toreducedoperatingefficiencyandmayevenposeathreattothesecurityofminepersonnel.Thereforetheidentificationofrunningfaultshasbecomeanimportantcomponentofthesafetsystem.Thekeytechniqueforhoistconditionmonitoringandfaultidentificationisextractinginformationfromfeaturesofthemonitoringsignalsandthenofferingajudgmentalresult.However,therearemanyvariablestomonitorinaminehoistand,also,therearemanycomplexcorrelationsbetweenthevariablesandtheworkingequipment.Thisintroducesuncertainfactorsandinformationasmanifestedbycomplexformssuchasmultiplefaultsorassociatedfaults,whichintroduceconsiderabledifficultytofaultdiagnosisandidentification[1].Therearecurrentlymanyconventionalmethodsforextractingminehoistfaultfeatures,suchasPrincipaComponentAnalysis(PCA)andPartialLeastSquares(PLS)[2].Thesemethodshavebeenappliedtotheactualprocess.However,thesemethodsareessentiallyalineartransformationapproach.Butheactualmonitoringprocessincludesnonlinearityindifferentdegrees.Thus,researchershaveproposedaseriesofnonlinearmethodsinvolvingcomplexnonlineartransformations.Furthermore,thesenon-linearmethodsareconfinedtofaultdetection:Faultvariableseparationandfaultidentificationarestilldifficultproblems.ThispaperdescribesahoistfaultdiagnosisfeatureexactionmethodbasedontheWaveletPacketTransform(WPT)andkernelprincipalcomponentanalysis(KPCA).WeextractthefeaturesbyWPTandthenextractthemainfeaturesusingaKPCAtransform,whichprojectslow-dimensionalmonitoringdatasamplesintoahigh-dimensionalspace.Thenwedoadimensionreductionandreconstructionbacktothesingularkernelmatrix.Afterthat,thetargetfeatureisextractedfromthereconstructednonsingularmatrix.Inthiswaytheexacttargetfeatureisdistinctstable.Bycomparingtheanalyzeddataweshowthatthemethodproposedinthispaperiseffective.FeatureextractionbasedonWPTandKPCA2.1WaveletpackettransformThewaveletpackettransform(WPT)method[3],whichisageneralizationofwaveletdecomposition,offersarichrangeofpossibilitiesforsignalanalysis.Thefrequebandsofahoist-motorsignalascollectedbythesensorsystemarewide.Theusefuinformationhideswithinthelargeamountofdata.Ingeneral,somefrequenciesoftsignalareamplifiedandsomearedepressedbytheinformation.Thatistosay,thebroadbandsignalscontainalargeamountofusefulinformation:Buttheinformationcannotbedirectlyobtainedfromthedata.TheWPTisafinesignalanalysismethothatdecomposesthesignalintomanylayersandgivesabetterresolutioninthetime-frequencydomain.Theusefulinformationwithinthedifferentfrequencybandswillbeexpressedbydifferentwaveletcoefficientsafterthedecompositionofthesignal.Theconceptof“energyinformation”ispresentedtoidentifynewinformationhiddenthedata.Anenergyeigenvectoristhenusedtoquicklymineinformationhidingwithinthelargamountofdata.Thealgorithmis:Step1:Performa3-layerwaveletpacketdecompositionoftheechosignalsandextractthesignalcharacteristicsoftheeightfrequencycomponents,fromlowtointhe3rdlayer.Step2:Reconstructthecoefficientsofthewaveletpacketdecomposition.Use3jS(j=0,1,…,7)todenotethereconstructedsignalsofeachfrequencybandrangeinthe3rdlayer.Thetotalsignalcanthenbedenotedas:s7S（1）3jj0Step3:ConstructthefeaturevectorsoftheechosignalsoftheGPR.Whenthecouplingelectromagneticwavesaretransmittedundergroundtheymeetvariousinhomogeneousmedia.Theenergydistributingoftheechosignalsineachfrequencybandwillthenbedifferent.Assumethatthecorrespondingenergyof3jS(j=0,1,…,7)canberepresentedas3jE(j=0,1,…,7).ThemagnitudeofthedisperpointsofedthereconstructedsignaljSis:3jkx(j=0,1,…,7;k=1,2,…,n),wherenisthelengthofthesignal.Thenwecanget:word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯ES(t)2dtnx2（2）3j3jjkk1Considerthatwehavemadeonlya3-layerwaveletpackagedecompositionoftheechosignals.Tomakethechangeofeachfrequencycomponentmoredetailedthe2-rankstatisticalcharacteristicsofthereconstructedsignalisalsoregardedasafeaturevector:1n2D (xx)（3）3jn jk jkk1Step4:The3jEareoftenlargesowenormalizethem.AssumethatE7E3j2,J0thusthederivedfeaturevectorsare,atlast:T=[E/1,E/1,,E/1,E/1]（4） 30 31 36 37Thesignalisdecomposedbyawaveletpackageandthentheusefulcharacteristicinformationfeaturevectorsareextractedthroughtheprocessgivenabove.Comparedtoothertraditionalmethods,liketheHilberttransform,approachesbasedontheWPanalysisaremorewelcomeduetotheagilityoftheprocessanditsscientificdecomposition.2.2KernelprincipalcomponentanalysisThemethodofkernelprincipalcomponentanalysisapplieskernelmethodstoprincipalcomponentanalysis[4–5].LetxRN,k1,2,...,M,Mx0.Theprincipalcomponentistheelementatthek kk11diagonalafterthecovariancematri，CxMMxxThasbeendiagonalized.ijj1Generallyspeaking,thefirstNvaluesalongthediagonal,correspondingtothelargeeigenvalues,aretheusefulinformationintheanalysis.PCAsolvestheeigenvaluesandeigenvectorsofthecovariancematrix.Solvingthecharacteristicequation[6]: 1M(x)x（5）c M j jj1wheretheeigenvalues0andtheeigenvectorsRN\0isessenceofPCA.Letthenonlineartransformations,:RNF,xX,projecttheoriginalspaceintofeaturespace,F.Thenthecovariancematrix,C,oftheoriginalspacehasthefollowingforminthefeaturespace:C1M(x)(x)T（6） M i jJ1NonlinearprincipalcomponentanalysiscanbeconsideredtobeprincipalcomponentanalysisofCinthefeaturespace,F.Obviously,alltheeigenvaluesofC(0)andeigenvectors,VF\{0}satisfyV=CV.Allofthesolutionsareinthesubspacethattransformsfrom(x),i1,2,...,Mj((x)V)(x)CV,k1,2,...,M（7） k kThereisacoefficientLetiVM(x)（8） i ii1FromEqs.(6),(7)and(8)wecanobtain:Ma((x)(x)) i k ji1 （9）1Ma((x)M(x))((x)(x))M i k j k j i1 j1wherek=1,2,…..,M.DefineAasanM×Mrankmatrix.Itselementsare:A(x)(x)（10）ij i jFromEqs.(9)and(10),wecanobtainMAa=A2a.Thisisequivalentto:Ma=Aa.(11)MakeasA’seigenvalues,and,,...,,asthecorresponding1 2 M 1 2 Meigenvector.Weonlyneedtocalculatethetestpoints’projontheeigenvectorsctonsVkthatcorrespondtononzeroeigenvaluesinFtodotheprincipalcomponentextraction.Definingthisasitisgivenby:k(Vk(x))Mk((x)x)(12) i i ki1Principalcomponentweneedtoknowtheexactformofthenon-linearimage.Alsoasthedimensionofthefeaturespaceincreasestheamountofcomputationgoesupexponentially.BecauseEq.(12)involvesaninner-productcomputation,(x)(x)iaccordingtotheprinciplesofHilbert-SchmidtwecanfindakernelfunctionthatsatisfiestheMercerconditionsandmakesK(x,x)(x)(x)ThenEq.(12)can i ibewritten:(Vk(x))Mk(K(x,x))(13) i i ki1HereistheeigenvectorofK.Inthiswaythedotproductmustbedoneintheoriginalspacebutthespecificformofxneednotbeknown.Themapping,x,andthefeaturespace,F,areallcompletelydeterminedbythechoiceofkernelfunction[7–8].2.3DescriptionofthealgorithmThealgorithmforextractingtargetfeaturesinrecognitionoffaultdiagnosisis:Step1:ExtractthefeaturesbyWPT;Step2:Calculatethenuclearmatrix,K,foreachsample,xRN(i1,2,...,N)intheioriginalinputspace,andK((x)(x)) ij iStep3:Calculatethenuclearmatrixafterzero-meanprocessingofthemappingdatainfeaturespace;Step4:SolvethecharacteristicequationMa=Aa;Step5:ExtractthekmajorcomponentsusingEq.(13)toderiveanewvector.BecausethekernelfunctionusedinKPCAmettheMercerconditionsitcanbeusedinsteadoftheinnerproductinfeaturespace.Itisnotnecessarytoconsiderthepreciseformthenonlineartransformation.Themappingfunctioncanbenon-linearandthedimensionsofthefeaturespacecanbeveryhighbutitispossibletogetthemainfeaturecomponentseffectivelybychoosingasuitablekernelfunctionandkernelparameters[9].3ResultsanddiscussionThecharacterofthemostcommonfaultofaminehoistwasinthefrequencyoftheequipmentvibrationsignals.Theexperimentusedthevibrationsignalsofaminehoisastestdata.Thecollectedvibrationsignalswerefirstprocessedbywaveletpacket.Thenthroughtheobservationofdifferenttime-frequencyenergydistributionsinalevelofthewaveletpacketweobtainedtheoriginaldatasheetshowninTable1byextractingthefeaturesoftherunningmotor.Thefaultdiagnosismodelisusedforfaultidentificationorclassification.Experimentaltestingwasconductedintwoparts:ThefirstpartwascomparingtheperformanceofKPCAandPCAforfeatureextractionfromtheoriginaldata,namely:ThedistributionoftheprojectionofthemaincomponentsofthetestedfaultsamplesThesecondpartwascomparingtheperformanceoftheclassifiers,whichwereconstructedafterextractingfeaturesbyKPCAorPCA.Theminimumdistanceandnearest-neighborcriteriawereusedforclassificationcomparison,whichcanalsotestheKPCAandPCAperformance.Inthefirstpartoftheexperiment,300faultsampleswereusedforcomparingbetweenKPCAandPCAforfeatureextraction.TosimplifythecalculationsaGaussiankernelfunctionwasused:xy2K(x,y)(x),(y)exp()1022Thevalueofthekernelparameter,,isbetween0.8and3,andtheintervalis0.4whenthenumberofreduceddimensionsisascertained.Sothebestcorrectclassificationrateatthisdimensionistheaccuracyoftheclassifierhavingthebestclassificationresults.Inthesecondpartoftheexperiment,theclassifiers’recognitirateafterfeatureextractionwasexamined.Comparisonsweredonetwoways:theminimumdistanceorthenearest-neighbor.80%ofthedatawereselectedfortrainingandtheother20%wereusedfortesting.TheresultsareshowninTables2and3.FromTables2and3,itcanbeconcludedfromTables2and3thatKPCAtakeslesstimeandhasrelativelyhigherrecognitionaccuracythanPCA.4ConclusionsAprincipalcomponentanalysisusingthekernelfaultextractionmethodwasdescribed.Theproblemisfirsttransformedfromanonlinearspaceintoalinearhigherdimensionspace.Thenthehigherdimensionfeaturespaceisoperatedonbytakingtheinnerproductwithakernelfunction.Thistherebycleverlysolvescomplexcomputingproblemsandovercomesthedifficultiesofhighdimensionsandlocalminimization.Ascanbeseenfromtheexperimentaldata,comparedtothetraditionalPCAtheKPCAanalysishasgreatlyimprovedfeatureextractionandefficiencyinrecognitionfaultstates.word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯ReferencesRibeiroRL.Faultdetectionofopen-switchdamageinvoltage-fedPWMmotordrivesystems.IEEETransPowerElectron,2003,18(2):587–593.SottileJ.Anoverviewoffaultmonitoringanddiagnosisinminingequipment.IEEETransIndAppl,1994,30(5):1326–1332.PengZK,ChuFL.Applicationofwavelettransforminmachineconditionmonitoringandfaultdiagnostics:areviewwithbibliography.MechanicalSystemsandSignalProcessing,2003(17):199–221.RothV,SteinhageV.Nonlineardiscriminantanalysisusingkernelfunction.In:AdvancesinNeuralInformationProceedingSystems.MA:MITPress,2000:568–574.TwiningC,TaylorC.Theuseofkernelprincipalcomponentanalysistomodeldatadistributions.PatternRecognition,2003,36(1):217–227.MullerKR,MikaS,RatschS,etal.Anintroductiontokernel-basedlearningalgorithms.IEEETransonNeuralNetwork,2001,12(2):181.XiaoJH,FanKQ,WuJP.AstudyonSVMforfaultdiagnosis.JournalofVibration,Measurement&Diagnosis,2001,21(4):258–262.ZhaoLJ,WangG,LiY.StudyofanonlinearPCAfaultdetectionanddiagnosismethod.InformationandControl,2001,30(4):359–364.XiaoJH,WuJP.Theoryandapplicationstudyoffeatureextractionbasedonkernel.ComputerEngineering,2002,28(10):–386.中文譯文基于小波包變換和核主元分析技術(shù)的礦井提升機(jī)的自我故障檢測(cè)摘要：這是一種新的運(yùn)算法，它能正確識(shí)別礦井提升機(jī)的故障并且準(zhǔn)確地監(jiān)測(cè)礦井提升機(jī)故障的發(fā)展過程。這種方法是基于小波包變換（WPT）和核主成份分析（KPCA，核主成份分析）技術(shù)。對(duì)于非線性監(jiān)聽系統(tǒng)，故障檢測(cè)的關(guān)鍵是提取主要特征。小波包變換是時(shí)間頻率的局部化分析，尤其適合于非平穩(wěn)信號(hào)。KPCA就是將最初輸入的數(shù)據(jù)特征透過非線性映射映射到高維特征空間，然后在高維特征空間發(fā)現(xiàn)其主要組成部分。KPCA變換適用于從經(jīng)過小波包變換的實(shí)驗(yàn)故障特征數(shù)據(jù)中提取主要的非線性特征。結(jié)果表示，該方法能提供可靠的故障檢測(cè)和鑒定。關(guān)鍵詞：核心方法;主成分分析;核主元分析;故障檢測(cè)1介紹因?yàn)榈V井提升機(jī)是一種復(fù)雜的可變性比較大的系統(tǒng)，提升機(jī)在長(zhǎng)期運(yùn)行和重載情況下難免會(huì)產(chǎn)生一些故障。這些都有可能損壞設(shè)備，停工，降低工作效率，甚至對(duì)我們員工的安全帶來威脅。因此，運(yùn)行中故障的檢測(cè)已經(jīng)變成安全系統(tǒng)的一個(gè)重要組成部分。提升機(jī)狀態(tài)監(jiān)測(cè)與故障識(shí)別的關(guān)鍵技術(shù)是從監(jiān)測(cè)信號(hào)特征中提取的信息和提供一個(gè)判斷的結(jié)果。但是，在礦井提升機(jī)的檢測(cè)中有很多不同的情況，而且在各種各樣的工作設(shè)備之間有許多復(fù)雜的相互關(guān)系。這里不確定因素和數(shù)據(jù)由復(fù)雜的形式所表現(xiàn)，如多個(gè)故障或相關(guān)故障，這些故障的診斷和鑒定是相當(dāng)困難的。目前有許多傳統(tǒng)的方法可以提取礦井提升機(jī)故障特征，如主成分分析（PCA）和偏最小二乘法（PLS）。這些方法已經(jīng)被熟練的運(yùn)用于我們的實(shí)際生產(chǎn)中來。然而，這些方法基本上是一個(gè)線性變換方法。但實(shí)際監(jiān)測(cè)過程包括不同程度的非線性。因此我們的研究員已經(jīng)提出了一系列涉及復(fù)雜的非線性變換非線性方法。此外，這些非線性方法只限于故障檢測(cè)，故障變量分離和故障識(shí)別仍然是難以解決的問題。這篇論文是介紹了一種基于小波包變換（WPT）和核主成份分（KPCA）的礦井提升機(jī)故障診斷的特征提取方法。我們用WPT提取特征數(shù)據(jù)然后用核主成分分析變換提取主要數(shù)據(jù)特征，這種變換將低維的監(jiān)測(cè)數(shù)據(jù)樣本映射到高維的特征空間。然后我們做了降維和重建并備份到奇異核矩陣。在這之后，目標(biāo)特征從重構(gòu)的非奇異矩陣提取出來。用這樣的方法我們得到清楚又穩(wěn)定的目標(biāo)特征。通過比較分析數(shù)據(jù)，我們得出本文提出的方法是有效的。2基于小波包變換和主成分分析技術(shù)的特征提取2.1小波包變換小波包變換（小波包變換）方法[3]，這是一種小波的分解的概括，為信號(hào)分析提供了很多可能。傳感器系統(tǒng)收集到的升降器的信號(hào)頻帶是非常廣泛的。有用的信息隱藏在大量的數(shù)據(jù)中。一般情況下，某些頻率的信號(hào)被放大，某些頻率的信號(hào)被抑制。這就是說，這些寬帶信號(hào)包含大量有用的信息：但是從這些信息中不能直接獲得有用數(shù)據(jù)。小波包變換是一個(gè)很好的信號(hào)分析方法，它把信號(hào)分解成很多層的信號(hào)并在時(shí)頻域給出了一個(gè)更好的分辨率，不同頻段內(nèi)的有用信息在信號(hào)分解后將被不同的小波系數(shù)表達(dá)。該信號(hào)的提出，是以確定新的信息隱藏在數(shù)據(jù)的中新信息。然后一種能量特征向量快速挖掘隱藏在大量的數(shù)據(jù)中的有用信息。該算法是：第1步：將回波信號(hào)執(zhí)行3層小波包分解，并提取8個(gè)頻率成分的信號(hào)特征在第三層，從低到高。第2步：重構(gòu)小波包分解的系數(shù)。利用3jS(j=0,1,…,7)指每個(gè)重建信號(hào)的頻帶范圍內(nèi)的第3層?？偟男盘?hào)就可以被命名為：s7S（1）3jj0第3步：構(gòu)建的探地雷達(dá)回波信號(hào)的特征向量。當(dāng)電磁波的耦合傳輸他們滿足各種地下非均勻介質(zhì)。能源分布的回波信號(hào)在每個(gè)頻帶然后將不同：承擔(dān)相應(yīng)的能量3jS(j=0,1,…,7)可以代表3jE(j=0,1,…,7).的規(guī)模分散點(diǎn)的重建信號(hào)3jS是jkx(j=0,1,…,7;k=1,2,…,n),word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯word文檔可自由復(fù)制編輯其中n是長(zhǎng)度的信號(hào)。然后，我們可以得到：ES(t)2dtnx2（2）3j3jjkk1考慮到我們做的只有3層的回波信號(hào)的小波包分解。為了使每個(gè)頻率成分的變化更詳細(xì)，重構(gòu)信號(hào)的2級(jí)的統(tǒng)計(jì)特性也被視為一個(gè)特征向量：1n2D (xx)（3）3jn jk jkk1(4)第4步3jE往往大，所以我們將他們標(biāo)準(zhǔn)化。假設(shè)E7E3j2，從而得J0出的特征向量是，最后：T=[E/1,E/1,,E/1,E/1] 30 31 36 37信號(hào)通過小波包變換分解，然后提取有用的特征信息的特征向量通過上述過程。相對(duì)于其他傳統(tǒng)方法，像希爾伯特變換，基于小波包變換分析方法更受歡迎，這是由于它敏捷的過程和它的科學(xué)分解。2.2核主成份分析核主成分份析方法就是將核心方法應(yīng)用在主成分分析法中[4-5]。使xRN,k1,2,...,M,Mx0.主要組成部分是在對(duì)角線元素后，協(xié)方差矩陣，k kk11MCxxT已是結(jié)尾。一般而言，第一次N值山對(duì)角線長(zhǎng)，相應(yīng)的大特征值，M ijj1是有用的信息在數(shù)據(jù)分析.PCA解決了特征值和特征向量的協(xié)方差矩陣。求解特征方程[6]：c1M(x)xM j jj1如果特征值和特征向量0，RN/0是屬于PCA的。使非線性變換，RNF,xX項(xiàng)目原始空間到特征空間，樓然后，協(xié)方差矩陣，中，原來的空間具有下列表格中的功能空間：C1M(x)(x)T(6) M i jJ1非線性主成分分析可被認(rèn)為是主成分分析的功能空間，樓顯然，所有的C(0)抗原值和特征向量，VF\{0}滿足VCV。所有的解決方案是在子這一轉(zhuǎn)變從(x),i1,2,...,M j ((x)V)(x)CV,k1,2,...,M（7） k k使系數(shù)i可以得到VM(x)（8） i ii1從678式我們可以得到Ma((x)(x)) i k ji1 (9)1Ma((x)M(x))((x)(x))M i k j k j i1 j1使k=1,2,…,M定義A是M×M的矩陣，它的要點(diǎn)是A(x)(x)（10）ij i j從9和10式我們可以得到MAa=A2a這就相當(dāng)于Ma=Aa.（11）使作為A的特征值，以及,...,相應(yīng)的特征向量。我們只需 1 2 M 1 2 M要計(jì)算測(cè)試點(diǎn)的預(yù)測(cè)的特征向量對(duì)應(yīng)的非零特征值的F這樣做主要成分的提取。界定這種因?yàn)樗怯桑?Vk(x))Mk((x)x)(12) i i ki1主要組成部分，我們需要知道確切形式的非線性圖像。還為層面的特征空間增加了計(jì)算量隨之呈指數(shù)。由于均衡器。由于式（12）涉及內(nèi)積計(jì)算，(x)(x)i根據(jù)希爾伯特-施密特的原則，我們可以找到一個(gè)內(nèi)積函數(shù)，滿足的默瑟條件下，方程（12）K(x,x)(x)(x)可以改寫成(Vk(x))Mk(K(x,x))i i i i ki1這里是K的一個(gè)變量。這樣，點(diǎn)積必須在原來的空間，但(x)的具體形式?jīng)]必要知道。特征空間F，完全取決于選擇的核心特征[7-8]。2.3說明算法在故障診斷的識(shí)別中提取目標(biāo)特征的算法是：第1步：用小波包變換提取特征;第2步：計(jì)算每個(gè)樣本的核矩陣，KxRN(i1,2,...,N)在原始的空間輸入，和K((x)(x))i ij i第3步：在特征空間進(jìn)行測(cè)繪數(shù)據(jù)的均值處理，然后計(jì)算核矩陣;第4步：求解特征方程Ma=Aa;第5步：利用方程提取的K式的重要組成部分(13)，制定出一個(gè)新的載體。由于核函數(shù)在核主成分分析要滿足Mercer的條件，可用于代替內(nèi)積的特征空間。沒有必要考慮的具體形式的非線性變換。映射功能可以非線性和特征空間的尺寸可以很高，但有它可能得到有效的主要成分通過選擇合適的核函數(shù)和內(nèi)核參數(shù)[9]。3結(jié)果與討論礦井提升機(jī)的最常見的故障特征可以在設(shè)備振動(dòng)信號(hào)的頻率中提取出來。實(shí)驗(yàn)中使用礦井提升機(jī)的振動(dòng)信號(hào)作為測(cè)試數(shù)據(jù)，將收集到的振動(dòng)信號(hào)首先進(jìn)行小波包處