Detection and classification of induction motor faults using Motor Current Signature Analysis and Multilayer Perceptron

Fault detection and classification of electrical motors is important in order to avoid unpredicted breakdown of electrical motors. The inherent failures due to unavoidable electrical stresses in motors results in motors experiencing stator faults, rotor faults and unbalanced voltage faults. If these faults are not identified in the early stage, it may become catastrophic to the operation of the motor. In this paper, the detection and classification of induction motor faults due to electrical related failure using Motor Current Signature Analysis (MCSA) and Multilayer Perceptron (MLP) neural network is proposed. Data collection of current signal of motors with different fault conditions is carried out by using laboratory experiments. The data collected which consists of the three phase stator current signal in different motor fault conditions is analysed using motor current signature analysis (MCSA) method. Power spectral density (PSD) method is then utilized to extract three phase stator current signals to obtain the frequency spectrum of stator currents via Fast Fourier Transform (FFT) as the data input which is fed into the MLP neural network classifier. As it is important to choose proper training algorithm for training the MLP neural network, therefore six different MLP neural network training algorithms are compared in terms of their accuracy, mean square error (MSE), number of iterations and training time.