Detection of broken rotor bar faults in induction motor at low load using neural network
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Date
2016
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Abstract
The knowledgeofthebrokenrotorbarscharacteristicfrequenciesandamplitudeshasagreatimportance for allrelateddiagnosticmethods.Themonitoringofmotorfaultsrequiresahighresolutionspectrumto separatedifferentfrequencycomponents.TheDiscreteFourierTransform(DFT)hasbeenwidelyusedto achieve theserequirements.However,atlowslipthistechniquecannotgivegoodresults.Asasolution for theseproblems,this paper proposesanefficient techniquebasedonaneuralnetworkapproachand Hilbert transform(HT)forbrokenrotorbardiagnosisininductionmachinesatlowload.TheHilbert transform isusedtoextractthestatorcurrentenvelope(SCE).Twofeaturesareselectedfromthe(SCE) spectrum (theamplitudeandfrequencyoftheharmonic).Thesefeatureswillbeusedasinputforneural network.Theresultsobtainedareastonishinganditiscapabletodetectthecorrectnumberofbroken rotor bars under different load conditions.