ensemble methods for bearing fault detection using vibration data

The detection of bearing faults plays a critical role in predictive maintenance for industrial machinery, where unexpected failures can result in significant costs and safety hazards. this study proposes an ensemble machine learning approach that combines random forest, support vector machine (svm), and logistic regression to achieve higher accuracy in fault detection using vibration data analysis. by employing a voting classifier to integrate these models, the approach demonstrates notable improvements in diagnostic accuracy and robustness compared to individual models. the findings highlight the ensemble method's potential as a superior diagnostic tool for bearing fault detection, surpassing conventional machine learning techniques.