Fault Classification in Induction Motor basedon Stray Flux and Stator Current Using Machine Learning /

Abstract

Induction motors are commonly used for different industrial work purposes. And consider the most used machine on the planet due to its strong and rigid structure and good-quality output. Therefore, due to their heavy use in industries, and commercial and residential buildings they may experience various faults in the stator and rotor, such as broken bar (BRB), full pole pitch (FPP), static eccentricity (SE), etc., and their early detection is necessary to avoid further damage in the system. Fault detection in induction motors is usually done by measuring noise, temperature, vibration, and electrical quantities, including current, flux, and torque. There are many other techniques used in the literature by the scientist for the fault detection induction motor. In this work, a deep neural network (DNN) machine learning (ML) algorithm is proposed and designed according to the simulated data. The designed DNN is then compared with support vector machine (SVM) and random forest classifiers (RFC) for the detection and classification of faults in induction motors using stator current and stray flux. ANSYS Maxwell-based simulations are performed for four different loading conditions (25%, 50%, 75%, and 100%) of the induction motor to obtain the stator current and stray flux data on normal and faulty conditions (BRB1, BRB2, BRB3, FPP, and SE). The results indicate that the proposed deep neural network algorithm has shown better accuracy for stray flux compared with SVM and RFC on 100 % loading conditions. Moreover, DNN performed well for other loading conditions in case of stray flux. However, for stator current, the overall performance of all machine learning algorithms was less efficient.