Browsing by Author "BESSAM Besma"

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    Detection of broken rotor bar faults in induction motor at low load using neural network
    (2016) BESSAM Besma
    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.
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    DWT and Hilbert Transform for Broken Rotor Bar Fault Diagnosis in Induction Machine at Low Load
    (2015) BESSAM Besma
    In this paper a new technique for broken rotor bars diagnosis in induction machine at low load and non stationary state is proposed. The technique is used in order to remedy the problem from using the classical signal-processing technique FFT by analysis of stator current envelope. The proposed method is based from using discrete wavelet transform (DWT) and Hilbert transform. The Hilbert transform is used to extract the stator current envelope. Then this signal is processed via DWT. The efficiency of the proposed method is verified by simulation tests.
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    Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor
    (2015-12-15) BESSAM Besma
    It is well known that stator winding faults such the inter-turn short circuit are the most frequent source of breakdowns in induction motors. Early detection of any small inter-turn short circuit and location of the faulty phase at different load would eliminate some subsequent damage to adjacent coils and stator core, reducing then the repair cost. To achieve this purpose, the present paper presents a new method of diagnosis and detection of inter turn short circuit fault using discrete wavelet transform (DWT) and neural networks (NN). This method consists in analyzing the stator current by DWT in order to compute the energy associated with the stator fault in the frequency bandwidth. Then, this energy is used as input for a NN classifier. The results obtained are astonishing and the approach is able to detect any small number of shorted turns and the faulty phase even under different load of the machine.