Diagnosis and classification of stator winding insulation faults on a three-phase induction motor using wavelet and MNN

The growing industrialization needs techniques to diagnose the incipient faults in induction motor at inception stage itself for avoiding the downtime of the production. In this regard detecting the faults in a 3-phase induction motor at an early stage is a vital component in process industries. The condition of the supply unbalance, under voltage and sudden load changes are other involuntary issues which may tend to exhibit current signature similar to the stator winding insulation faults. This paper proposes a robust technique to detect, classify various stator winding insulation faults and severity of stator inter-turn faults when an induction motor works under various operating conditions. In the present work, disturbance features are extracted from three phase residues which are obtained from wavelet multiresolution analysis. Three modular neural networks are implemented, in which one is used to classify various disturbances such as single phasing, supply unbalance, under voltage, stator inter-turn faults, sudden load change and phase faults, second one is used for classifying the stator winding phase faults and third one is used for identifying the faulty phase and severity level of stator inter-turn faults. Simulation and Experimental data demonstrate the validity of the proposed method and improvement in classification accuracy as compared to traditional method.