Abstract The paper presents the possibility of using neural networks in the detection of stator and rotor electrical faults of induction motors. Fault detection and identification are based on the analysis of symptoms obtained from the fast Fourier transform of the voltage induced by an axial flux in a measurement coil. Neural network teaching and testing were performed in a MATLAB–Simulink environment. The effectiveness of various neural network structures to detect damage, its type (rotor or stator damage) and damage levels (number of rotor bars cracked or stator winding shorted circuits) is presented.


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    Title :

    Application of Neural Networks and Axial Flux for the Detection of Stator and Rotor Faults of an Induction Motor


    Contributors:

    Published in:

    Publication date :

    2019




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English


    Classification :

    BKL:    53.35 Stromrichter, Transformatoren / 53.33$jElektrische Maschinen und Antriebe / 53.33 Elektrische Maschinen und Antriebe / 53.35$jStromrichter$jTransformatoren



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