FAULT DIAGNOSIS DETECTING USING MACHINE LEARNING

Abstract

The need for alternate transportation is driven by the increased fossil fuel cost and the adverse effects of climatic change. Electric vehicles (EVs) are the best option as they have less carbon footprint and reduced dependency on fossil fuels. Prodigious efforts to enhance the efficiency of EVs resulted in the development of highly efficient three-phase induction motors. Difficulties in designing highly efficient induction motors (IM) with high torque and power factors hindered the success of EV applications. Hence, our aim is to diagnosis fault in the designed IM under variable load conditions. The proposed EV motor is designed for 415V, 50Hz, and 5HP output power rating using ANSYS RMxprt simulation software. A fault detection strategy is also implemented with various machine learning (ML) techniques like Support Vector Machine (SVM), K-nearest neighbors (k-NN), ML perceptron (MLP), Random Forest (RF), Decision Tree (DT), Gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and Deep Learning (DL) for both healthy and faulty conditions. Short Circuit (SC), High Resistance connection (HRC), and Open-Phase circuit (OPC) are considered as faulty states for this study. Motor performance with variable load for all the states healthy and faulty are evaluated through machine learning.