Design and Fault Diagnosis of Induction Motor Using ML-Based Algorithms for EV Application

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.

Mathematical Decomposition of Fault Signature using Multinomial Identity and Differentiating for Detection of Broken Rotor Bar in Induction Motor

“…identifying and diagnosing faults in IMs before they lead to motor failure, and include techniques such as vibration analysis, Motor Current Signal Analysis (MCSA), and motor current signature analysis [8,[10][11][12][13][14][15][16][17]. By implementing these fault diagnosis methods, the reliability of IMs can be enhanced, thereby improving the overall performance and safety of EVs.…”

“…Therefore, any failure in any of its components can directly impact the reliability of the powertrain and the safety of passengers [6,7]. As a result, it is crucial to develop an electric motor that enhances the efficiency and performance of EVs [8,9]. Among the various types of electric motors used in EVs, the induction motor (IM) is more effective and economical than others due to its reliability, simple mechanical design, and effective field-weakening characteristics [8].…”

FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization

“…Aishwarya and Brisilla used various machine-learning techniques (like SVM, K-nearest neighbors (k-NN), ML perceptron (MLP), Random Forest (RF), Decision Tree (DT), etc.) to implement a fault detection strategy in the designed induction motors under variable load conditions [11]. Dutta et al present a case study of a machine-learning (ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive [12].…”

Margin-Maximized Hyperspace for Fault Detection and Prediction: A Case Study With an Elevator Door

“…Fault detection and diagnosis (FDD) on a variety of mechatronics systems, including motors [1][2][3][4][5][6][7][8], wind turbines [9], [10], automobiles [11], gas turbines [12][13][14], and HVAC systems [15][16][17][18] have been investigated worldwide because manufacturers and customers require to minimize the downtime of these systems to reduce operational and maintenance costs [19]. FDD can be classified into three approaches: physics-based, knowledge-based, and data-driven approaches [20].…”