Abstract—Bearing faults are a major cause of degradation in Brushless DC (BLDC) motors, making reliable detection of different bearing fault conditions essential for maintaining industrial equipment. This study proposes a diagnostic method that combines ReliefF feature selection with a Random Forest classifier to identify both single and compound bearing faults. Unlike earlier studies that applied this combination mainly to single-fault or binary classifications, the present work addresses a more realistic seven-class bearing fault problem. From the vibration signals, eighteen statistical features were calculated, and ReliefF was employed to identify the features that most effectively distinguish among the bearing fault categories. The resulting feature ranking improves separability, especially for compound-bearing faults that often exhibit overlapping spectral characteristics. With these selected features, the Random Forest model achieved strong diagnostic performance, demonstrating that the proposed framework offers an efficient and practical solution for identifying complex bearing fault conditions in BLDC motors.

Keywords—compound fault diagnosis, Brushless DC (BLDC) motor bearings, ReliefF feature ranking, random forest classifier, vibration signal analysis, multi-class fault classification

Cite: Meiyanto Eko Sulistyo, Didik Djoko Susilo, Muhammad Nizam, Ubaidillah, and Felly Anta, "Diagnosis of Multiple Bearing Faults in BLDC Motors Using ReliefF Feature Selection and Random Forest Classifier," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 2, pp. 222-231, 2026. doi: 10.18178/ijmerr.15.2.222-231

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).