Wavelet and SFAM based classification of induction motor stator winding short circuit faults and incipient insulation failures

Inter-turn fault in stator windings is one of the prime reasons behind a significant percentage of induction motor failures. Inter-turn short circuit fault may develop either due to complete failure of insulation between turns causing direct interturn short circuit fault or due to partial degradation of insulation between turns resulting in incipient insulation fault. Common protection schemes accompanied with induction motors in industrial applications normally fail to detect these faults especially when a minor number of turns are involved in such faults. Detection of these faults at their early stage can substantially reduce the possibilities of serious damage to the motor and consequently financial losses, environmental damage and probable personnel injury etc. Detection of such faults becomes a tricky task when these two different types of faults exhibit identical unbalance in motor supply currents. In the present study, two different series of experiments were carried out on an induction motor which was subjected to operate under direct short circuit fault and also under equivalent incipient insulation fault in stator winding. Using Extended Park´s Vector Approach, Park´s Vector Modulus (PVM) have been estimated from captured three phase current signals under different operating conditions of the motor. Several fault features were extracted from AC components present in PVMs by applying Continuous Wavelet Transform (CWT). Then, the fault features were fed to a Simplified Fuzzy Art-Map (SFAM) based classifier which has been found to perform accurately in identifying separately the direct inter-turn short circuit fault levels and equivalent incipient insulation failure conditions.