Abstract
Electric vehicles are one of the most important means in the industrial sector due to their frequent use and depend primarily on electric motors. Electric motors of all types, synchronous and asynchronous, face many faults in the rotor and stator, affecting the performance's reliability. Researchers are seeking to find ways that enable us to detect and diagnose faults in electric motors based on smart and fast methods. Early detection of problems in electric motors is vital, especially in areas such as electric vehicles. This study focuses on magnetic rotor breakage (MRB) in permanent magnet synchronous motors (PMSM). We use a simulation tool such as COMSOL Multiphysics as a simulation tool. This platform is a widely used software for modeling and analyzing complex electromagnetic systems. The study also addresses fault detection using machine learning. This involves using data analysis and pattern recognition techniques to distinguish between normal and defective states of the motor. This is an important step to improve the reliability of motors and identify potential failures in advance. Five different machine learning algorithms such as Extreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB), Naive Bayes (NB), and Random Forest (RF) are used in the study. Data from four different cases obtained from the PMSM design were used to train and test the machine-learning models. The results obtained show how accurate the proposed models are in diagnosing PMSM problems, especially MRB.
Published Version
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