Comparative analysis of supervised learning algorithms for predictive maintenance of bearings using vibration data

Bearing fault diagnosis plays a critical role in predictive maintenance of rotating machinery, where early and accurate detection can prevent catastrophic failures and reduce operational downtime. This study presents a comparative analysis of four supervised machine learning (ML) algorithms: decision tree, k‐nearest neighbours (k‐NN), logistic regression and Gaussian naive Bayes, for classifying bearing faults based on features extracted from vibration signals. The input features are standardised and class labels are encoded to ensure compatibility with ML workflows. A stratified train-test split is employed to maintain balanced class distribution across subsets. Each model is evaluated using key performance metrics: accuracy, precision, recall, F1 score and multi-class area under the curve (AUC). The results show that the decision tree classifier achieves the highest classification accuracy (93.57%), while logistic regression and k‐NN record the highest AUC scores (99.26% and 98.60%, respectively), reflecting strong generalisation and discriminatory capabilities. The findings indicate that while tree-based models offer superior raw classification accuracy, probabilistic and distance-based methods demonstrate excellent potential in handling multi-class bearing fault prediction tasks. This research underscores the importance of model selection and performance trade-offs in developing robust condition monitoring systems for industrial applications.