Computational study demonstrates 99.85% fault classification accuracy in rolling bearings using optimized machine learning models, highlighting strong utility for predictive maintenance.
Rolling bearing faults frequently cause rotating equipment failure, leading to costly downtime and maintenance expenses. As a result, researchers have focused on developing effective methods for diagnosing these faults. In this paper, we explore the potential of Machine Learning (ML) techniques for classifying the health status of bearings. Our approach involves decomposing the signal, extracting statistical features, and using a feature selection employing Binary Grey Wolf Optimization. We then employ four different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF) to diagnose faults based on the reduced set of features. To evaluate the performance of our methods, we utilize several performance indicators. Our results demonstrate that four Machine Learning methods can achieve a high-accuracy fault classification result of 99.85%, better than state-of-the-art methods, highlighting their potential for use in predictive maintenance applications.
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Phan et al. (2024) studied this question.
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