PERFORMANCE COMPARISON BASED SELECTING AN OPTIMAL METHOD FOR FAULT DETECTION AND DIAGNOSIS METHODS FOR INDUCTION MOTOR

Abstract

Induction motor is one of the most common and basic element involved in large amount of electrical systems. Finding a fault in the earlier stage can prevent the entire electrical system and avoid large damages. To avoid that, it is necessary to find out a best method for fault detection and diagnosis for induction motors. In this study the author compared the performance of various fault detection and diagnosis method and selects the best optimal method for induction motors based on the accuracy. Motor Current Signature Analysis (MCSA), Auto Regressive (AR) model, Discrete Wavelet Transform (DWT) model and Wavelet Packet Distribution (WPD) Model are some of the famous approaches, which are experimented, compared and the performance is evaluated based on various fault indexing parameters. From the obtained results DWT is suggested for the best method using wavelet transforming techniques to predict the severe fault with taking the total power as an indexing parameter in Fault detection and diagnosis for Induction Motor in any electrical Systems