Wavelet Based Fault Detection and Diagnosis Using Online MCSA of Stator Winding Faults Due to Insulation Failure in Industrial Induction Machine

A new effective approach for fault detection and diagnosis (FDD) technique for on-line condition monitoring (CM) of stator winding faults due to insulation failure in industrial induction machine is presented in this paper. FDD techniques are broadly classified into three categories; signature-extraction, model and knowledge-based approach. Recent advancement in computing performance and sensor technology permit the use of advanced systems to achieve this objective. Computer-aided advanced diagnosis system uses pattern recognition which is mainly comprises of feature extraction, feature selection and feature classification respectively. Introduction of wavelets in signature-based feature extraction process along with feature classification using knowledge-based approach is used for a reliable automatic identification of stator winding faults. Based on mathematical modeling for stator winding inter-turn fault; a simulink model is designed to validate mathematically obtained results and it is seen that results are in good agreement with the experimental results.