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1、ACOMPARISONOFARTIFICIALNEURALNETWORKSANDOTHERSTATISTICALMETHODSFORROTATINGMACHINECONDITIONCLASSIFICATIONA.C.McCormickandA.K.NandiAbstractStatisticalestimatesofvibrationsignalssuchasthemeanandvariancecanprovideindicationoffaultsinrotatingmachinery.Usingtheseestimatesjoi
2、ntlycangiveamorerobustclassi cationthanusingeachindividually.Arti cialneuralnetworkarchitecturesandsomestatisticalalgorithmsarecomparedwithemphasisontrainingrequirementsandreal-timeimplementationaswellasoverallperformance.IntroductionAnalysisofvibrationscanindicatefaul
3、tconditionsinrotatingmachinery[1]suchasshaftunbalanceorrubbing.Onecommonapproachistoestimatetime-invariantfeaturesfromthevibrationtimeserieswhichchangewhenafaultoccursinthemachine.Thesefeaturescanthenbeinputintosomeformofclassi cationsystemtodecidethemachine'scondition
4、.Arti cialneuralnetworkssuchasmulti-layerperceptrons(MLPs)provideasystemwhichcantheo-reticallyprovideBayesoptimalclassi cationoftheconditionbaseduponmanyfeatures[2].Trainingnetworkscanhowevertakeasubstantiallengthoftimeandisnotguaranteedto ndtheoptimalso-lution.Radialb
5、asisfunction(RBF)neuralnetworksprovideanalternativearchitecturewhichcanbetrainedinamuchshorterperiodoftime.Traditionalstatisticaldiscriminantanalysis[3]algorithmscanbeverysimpletoimplementanddonotrequireatimeconsumingtrainingalgorithm.Butinmanycases,theyrequirecertaina
6、ssumptionstobemadeabouttheinputdata.Iftheseassumptionsarenotvalid,theymaynotprovideasgoodasolutionasaneuralnetwork.ExperimentalSet-UpThevibrationsweremeasuredfromasmallexperimentalmachineset.Thisconsistedofanelectricmotorwhichdroveashaftwitha
ywheel.Smallweightscouldbe
7、attachedtothe
ywheelunbalancingtheshaftandrubbingcouldbeappliedusingascrewattachedtoaframe.Thevibrationsweremeasuredhorizontallyandverticallyusingaccelerometersattachedtoabearingblock.Thisset-upallowedthecreationoffourmachineconditions:NN-nofaultsapplied;NR-onlytherubf
8、aultisapplied;WN-onlytheunbalancefaultisapplied;WR-bothrubandunbalancefaultsareapplied.TheshaftDepartmentofElectronic
