Robust Electric Motor Fault Classification with Extra Trees Classifier on Comprehensive Dataset

This paper presents a comparative study on the efficacy of machine learning algorithms in enhancing fault diagnosis in electric motors. Through detailed experimentation and analysis, our research highlights the superior performance of the Extra Trees Classifier in accurately classifying various motor faults. With an impressive accuracy rate of $84 \%$, the Extra Trees Classifier emerges as the top performer among the algorithms tested. Additionally, the Random Forest Classifier demonstrates considerable effectiveness with an accuracy of $82 \%$. These findings underscore the potential of machine learning techniques to significantly improve fault diagnosis and prognostication in industrial machinery. Notably, the analysis showcases the successful classification of diverse fault types, including Normal, Horizontal Misalignment, Imbalance, and various other fault conditions. Moreover, our study emphasizes the importance of ongoing research and development efforts to further refine these algorithms and address any remaining challenges in fault diagnosis within motor systems.