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One of the most important tasks in industries is considered to be the condition monitoring of induction motors. However, most of the traditional methods employed for this purpose suffer from certain limitations in accomplishing this task. A successful implementation of condition monitoring requires the development of a simple but reliable detector of various faults. This paper investigates the performance of Discrete Wavelet Transform and Radial Basis Function based Neural Network for incipient stator fault diagnosis in induction motors. Wavelet analysis helps in the extraction of important features from the faulty signal and neural network classifies the fault type depending on the nature of fault. A mean absolute percentage error of 1.4236% between the actual values and the predicted values by the neural network show the effectiveness of the proposed approach.

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