Identification and Classification of Stator Inter-Turn Faults in Induction Motor Using Wavelet Kernel Based Convolutional Neural Network

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This paper presents an efficient technique for early diagnosis of simultaneous faults in different phases of stator winding of a three-phase induction motor due to turn-to-turn short circuit. A real-life motor has been designed and manufactured with fault emulation features in all the phases of stator winding. Phase currents are recorded by a data acquisition system for different fault conditions. Wavelet kernel-based convolutional neural network (WK-CNN) has been employed for identification and classification of the faults using the recorded current signatures. Various mother wavelets have been tested as convolution filters to extract salient features from the recorded current signatures followed by updating the weights of the filter at each epoch by a supervised learning algorithm. The reason to use a deep framework based on CNN is that it eliminates the requirement of feature extraction and classification algorithms separately. The proposed method also shows promising results when signals are contaminated by the noises, which is always a challenge in an industrial environment. Comparative results show the effectiveness of the proposed technique over the state-of-the-art methods.