Detection and Classification of Rotor broken bar Faults in Induction Motor Using Fuzzy Logic and Artificial Neural Networks

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kreangsuk Kraikitrat Somporn Ruangsinchaiwanich


In this paper, the detection and classification of broken rotor bar fault of induction motor using fuzzy logic and artificial neural networks is presented. Input data has been utilized to obtained data collection of the one phase stator current. Furthermore, fast fourier transform (FFT) is employed for converting original stator current waveform, which is time domain, to stator current signal, which is frequency domain, that is labeled as motor current signature analysis for collecting essential data in order for sending into artificial neural networks  and fuzzy logic later. Consequently, the fuzzy logic can perform very well for the detection and classification of broken rotor bar fault in term of lowest MSE and best accuracy that comparing with other data test (FLS: 99.84 %) (ANN: 93.54 %).


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