Application of ANN for Induction Motor Fault Classification Using Hilbert Transform - Jawadekar, Anjali U.; Dhole, G. M.; Paraskar, S. R.; Beg, M.A. (Asian Journal of Electrical Sciences, 2012)
This paper addresses the development of new signal processing approach based on Hilbert transform to extract the fault features in number of induction motor conditions. The motor conditions considered are normal condition and motors with bearing defects like inner race, outer race, stator interturn faults and rotor bar crack. Present approach is based on extraction of envelopes of the stator currents by Hilbert Transform. Representative features like maximum and minimum value, mean, standard deviation and norm are obtained from current envelopes. These features are then used as input to Artificial Neural Network. Experimental results obtained show that diagnostic system using MLP neural network along with Hilbert Transform is capable of classifying multiple faults in induction motor with high accuracy recognition rate.