Quantitative Modeling and Simulation for Stator Inter-turn Fault Detection in Industrial Machine

This paper deals with quantitative modeling and simulation for stator inter-turn fault detection in industrial machine. Quantitative modeling and simulation for both healthy and faulty induction machine at different loading conditions are studied and validated with experimental results under same operating condition. Fault identification techniques are mainly categorized into signature extraction-based, model-based, and knowledge-based approach. Introduction of statistical feature extraction, feature selection, and fault classification using knowledge-based approach are intelligent methodology for stator inter-turn fault identification and diagnosis for industrial machine.