Temperature Prediction and Performance Comparison of Permanent Magnet Synchronous Motors Using Different Machine Learning Techniques for Early Failure Detection

Electric motors are increasingly used in various products, including turbines and electric vehicles. Precise temperature measurement is essential for the safe operation of a Permanent Magnet Synchronous Motor. Direct temperature detection of the permanent magnet and stator involves significant costs and hardware requirements. To overcome these challenges, Machine Learning models can eliminate the need for specialized sensors. This study used four diverse regression algorithms: Linear, K-Nearest Neighbor, XGBoost, and AdaBoost. The objective of this study is to model a Permanent Magnet Synchronous Motor used in electric vehicles and predict the temperatures of some of its parameters. The K-Nearest Neighbor Regressor outperformed the other algorithms, achieving a training accuracy of 99.65%, test accuracy of 98.72%, root-mean-square error of 2.16, R2 score of 98.72, and Cross-Validation R2 of 97.77%. These results enable low-cost, real-time temperature monitoring of electrical machinery, enhancing power density, safety, and efficiency.