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This is a Quantitative Study study.

October 9, 2025Open Access

Detection of Bearing Faults from Vibration Signals

Key Points

Random forest and xgboost achieved outstanding accuracy of 95.73%, indicating effective fault detection in bearings.
Precision, recall, and F1-score metrics reached 96% for xgboost, reinforcing its reliability in classification tasks.
Support vector machine showcased robust performance with 93.73% accuracy, while naive bayes exhibited lower accuracy at 92.40%.
Statistical features like standard deviation and root mean square proved crucial for improving model performance.

Abstract

Bearings are critical mechanical components in rotating machinery, playing a vital role in system safety and operational continuity. In this study, the Case Western Reserve University (CWRU) bearing dataset is used to perform fault classification using four machine learning algorithms: Random Forest, XGBoost, Support Vector Machine (SVM), and Naive Bayes. Based on statistical features extracted in the time domain, the performance of each model is evaluated using accuracy, precision, recall, and F1-score metrics. The results reveal that Random Forest and XGBoost algorithms achieved superior performance with 95.73% accuracy and 96% in precision, recall, and F1-score. The SVM model, with 93.73% accuracy, stands out as a robust alternative, while the Naive Bayes algorithm shows relatively lower performance with 92.40% accuracy. Additionally, an individual feature-based classification analysis indicates that standard deviation (sd) and root mean square (RMS) features contribute most significantly to model performance. This study provides a comprehensive performance analysis of traditional machine learning algorithms, offering a valuable reference for early and accurate detection of bearing faults.

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