Machine Learning Algorithms for Bearing Fault Diagnosis Using Time-Domain Features

Abstract

Rolling Element Bearing (REB) failures represent a major challenge in the maintenance of rotating machinery, as they compromise the reliability and operational continuity of systems. The analysis of vibration signals at the bearing level provides an effective approach for the early detection of anomalies and their classification, thereby helping to anticipate breakdowns and enhance equipment safety. In this work, the the well-known bearing dataset of Case Western Reserve University (CWRU) is utilized to perform fault classification using four machine learning algorithms: K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). The study particularly addresses feature extraction in the time domain, such as root mean square (RMS), standard deviation, kurtosis, and other statistical indicators.