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1、附录ResearchofidentificationofshaftorbitforrotatingmachineXIAOSheng-guang（TestCenterofChongqingUniversity，Chongqing400044，China）Abstract：Anovelapproachfortheidentificationofshaftorbitispresented.Thevibrationdisplacementsignalsacquiredintwomutuallyverticaldirection
2、sweretreatedthroughnoisesuppressionandfittedtoformashaftorbit.Thenthedirectionchangingcharacterwasextractedandallshaftorbitswereclassifiedandidentifiedwiththefunctiondiscriminatedmethodaccordingtothepatternrecognitiontheory.Eachtypeofshaftorbitwasdescribedindeta
3、ilwithonecharacter，whichcanhelptojudgetheoperationstatusofthemechanicalandtheextentofthefailure.Theanalysisandsimulationgotgoodresults.Keywords：Shaftorbits；Faultdiagnosis；Geometricfeatures；Patternrecognition；Thinningclassification1TheintroductionWiththedevelopme
4、ntofscienceandtechnologyandmodernindustry,torotatingmachinesthelarge-scale,high-speedandautomationdirection,theshapeofrotatingmachinerystatemonitoringandfaultdiagnosisisputforwardhigherrequest,theaxistrajectoryforrotatingmachineryisanimportantstatecharacteristic
5、parameters,canbesimpleandstraightview,vividlyreflecttherunningstatusofequipment.Throughtotheaxisoftrackobservation,candeterminesomeofthecommonfaults,suchasoilfilmvortexStill,oilfilmoscillation,shaftnotmedium.Thetraditionalaxislocusandshapethedynamiccharacteristi
6、csidentificationisbasedontheman-machinedialoguemode,seriousaffectthelevelofintelligentfaultdiagnosis.Inordertoimprovethedegreeofintelligentfaultdiagnosis,itisnecessarytoin-depthstudythetrajectoryoftheaxisofrotatingmachineryautomaticidentificationtechnology.Axisp
7、athatpresent,alreadyhaveseveralidentificationmethods,including[1-2]invariantmomentmethod,atwo-dimensionalimagegraylevelmatrix[3].literature[1-2]axispathwithsevenmomentinvariantsasfeaturevectors,recognitionbythedistancebetweenthecharacteristicsofaxialtrajectorysh
8、ape,literature[3]theaxistrajectoryimagecoding,usingneuralnetworkforidentification.Bothmethodscanbetteridentifyaxispath,butthemethodiscomplex,relativelylargeamountofca
