Vibration-based bearing fault classification using classical machine learning models: a comparative study

doi:10.1080/14484846.2026.2639233

Vibration-based bearing fault classification using classical machine learning models: a comparative study

2026-03-01

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Bhende, Amit R. *

DOI：10.1080/14484846.2026.2639233

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Rolling element bearing faults are a major source of failure in rotating machinery, making accurate and reliable fault diagnosis essential for condition monitoring and predictive maintenance. This study presents a supervised machine learning (ML) based framework for bearing fault classification using statistical features extracted from vibration signals. Standard preprocessing techniques, including feature extraction, feature standardisation, and label encoding, were employed to prepare the data for model training. Multiple classifiers were evaluated, and their performance was assessed using standard evaluation metrics and confusion matrix analysis. The results show that the Decision Tree (DT) classifier achieved the highest classification accuracy of 93.57%, indicating its strong capability to model nonlinear relationships between vibration features and bearing fault categories. The k-Nearest Neighbour (kNN) classifier recorded a slightly lower accuracy of 92.52% but achieved a high AUC of 98.60%, reflecting excellent class separability and generalisation capability. The strong AUC value suggests that kNN effectively distinguishes between healthy and faulty bearing conditions as well as among different fault categories when vibration features are properly scaled. Overall, the results indicate that while tree-based models offer higher raw classification accuracy, probabilistic and distance-based approaches provide strong generalisation performance and robust fault separability. The proposed approach shows strong generalisation capability, with only limited misclassification observed between certain fault types exhibiting similar vibration characteristics. Feature importance analysis further highlights the critical role of higher-order statistical features in distinguishing bearing health conditions. Overall, the findings confirm the effectiveness of the proposed framework for vibration-based bearing fault diagnosis and its potential applicability in real-world condition monitoring systems.

Keywords:

Bearing faults

vibration analysis

Logistic Regression

Decision Tree

Gaussian Naive Bayes

k-Nearest Neighbours (kNN)

Vibration-based bearing fault classification using classical machine learning models: a comparative study

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