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1、附录ResearchofidentificationofshaftorbitforrotatingmachineXIAOSheng-guang（TestCenterofChongqingUniversity，Chongqing400044，China）Abstract：Anovelapproachfortheidentificationofshaftorbitispresented.Thevibrationdisplacementsignalsacquiredintwomutuallyverticaldirectionsweretreatedt
2、hroughnoisesuppressionandfittedtoformashaftorbit.Thenthedirectionchangingcharacterwasextractedandallshaftorbitswereclassifiedandidentifiedwiththefunctiondiscriminatedmethodaccordingtothepatternrecognitiontheory.Eachtypeofshaftorbitwasdescribedindetailwithonecharacter，whichca
3、nhelptojudgetheoperationstatusofthemechanicalandtheextentofthefailure.Theanalysisandsimulationgotgoodresults.Keywords：Shaftorbits；Faultdiagnosis；Geometricfeatures；Patternrecognition；Thinningclassification1TheintroductionWiththedevelopmentofscienceandtechnologyandmodernindust
4、ry,torotatingmachinesthelarge-scale,high-speedandautomationdirection,theshapeofrotatingmachinerystatemonitoringandfaultdiagnosisisputforwardhigherrequest,theaxistrajectoryforrotatingmachineryisanimportantstatecharacteristicparameters,canbesimpleandstraightview,vividlyreflect
5、therunningstatusofequipment.Throughtotheaxisoftrackobservation,candeterminesomeofthecommonfaults,suchasoilfilmvortexStill,oilfilmoscillation,shaftnotmedium.Thetraditionalaxislocusandshapethedynamiccharacteristicsidentificationisbasedontheman-machinedialoguemode,seriousaffect
6、thelevelofintelligentfaultdiagnosis.Inordertoimprovethedegreeofintelligentfaultdiagnosis,itisnecessarytoin-depthstudythetrajectoryoftheaxisofrotatingmachineryautomaticidentificationtechnology.Axispathatpresent,alreadyhaveseveralidentificationmethods,including[1-2]invariantmo
7、mentmethod,atwo-dimensionalimagegraylevelmatrix[3].literature[1-2]axispathwithsevenmomentinvariantsasfeaturevectors,recognitionbythedistancebetweenthecharacteristicsofaxialtrajectoryshape,literature[3]theaxistrajectoryimagecoding,usingneuralnetworkforidentification.Bothmetho
8、dscanbetteridentifyaxispath,butthemethodiscomplex,relativelylargeamountofca
