@inproceedings{c96ab61e33e146c080cedc904da18423,
title = "A Machine Learning Framework for Bearing Fault Detection in Three-Phase Induction Motors",
abstract = "Three-phase induction motors are widely employed in industry due to their rugged performance and easy maintenance. Bearing faults in three phase induction motors are responsible for 40%-50% of unplanned shutdowns in industrial settings. Therefore, early detection of bearing faults is essential to implement preventive measures and enhance planning of maintenance strategies. This paper thus proposes a machine learning (ML) framework that consistently monitors acceleration and temperature of bearing to detect bearing faults. The results show that the ML framework using k-nearest neighbor (k-NN) and support vector machine (SVM) approaches is better than the variation-based thresholding approach, where the former method is able to detect faulty conditions with more than 99% accuracy.",
keywords = "Bearing fault, condition monitoring, electric motors, fault detection, machine learning",
author = "Wesam Rohouma and Ayham Zaitouny and Wahid, {Md Ferdous} and Hassan Ali and Refaat, {Shady S.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 4th International Conference on Smart Grid and Renewable Energy, SGRE 2024 ; Conference date: 08-01-2024 Through 10-01-2024",
year = "2024",
doi = "10.1109/SGRE59715.2024.10429024",
language = "English",
series = "4th International Conference on Smart Grid and Renewable Energy, SGRE 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "4th International Conference on Smart Grid and Renewable Energy, SGRE 2024 - Proceedings",
}