RESEARCH PAPER
Temperature Prediction and Performance Comparison of Permanent Magnet Synchronous Motors Using Different Machine Learning Techniques for Early Failure Detection
1
Dicle University, Turkey
Submission date: 2024-06-11
Final revision date: 2024-07-01
Acceptance date: 2024-08-08
Online publication date: 2024-08-09
Publication date: 2024-08-09
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):192164
HIGHLIGHTS
- KNN Regressor achieved 99.65% training and 98.72% test accuracy for PMSM temperature prediction.
- Machine learning models replace costly sensors, enabling low-cost, real-time motor temperature monitoring.
- Model validation shows RMSE 2.16, R2 score 98.72, and CV R2 97.77%, proving practical effectiveness.
KEYWORDS
TOPICS
ABSTRACT
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.
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