Intelligent diagnosis of bearing faults via support vector machine and multinomial logistic regression: Performance evaluation and analysis

Abstract

Bearings are critical components in rotating machinery and their failure can lead to catastrophic outcomes, including system downtime and financial losses. Accurate fault detection and prediction in bearings can significantly improve the reliability and efficiency of industrial systems. The reliable operation of rotating machinery is critically dependent on early and accurate detection of bearing faults. This research presents an intelligent fault classification and prediction framework utilizing Support Vector Machine (SVM) and Multinomial Logistic Regression (MLR) models applied to vibration signal data. The dataset consists of multiple fault categories including inner race, outer race, and ball defects under various severity levels. After preprocessing and feature extraction, both SVM and MLR models were trained and evaluated using a confusion matrix, precision, recall, and F1-score metrics. The SVM model demonstrated superior classification performance, particularly in accurately detecting complex fault patterns, achieving an overall accuracy of 96%, compared to 94% with logistic regression. Comparative analysis highlights the strengths of SVM in handling non-linear decision boundaries, while logistic regression offers simpler interpretability and faster training times. The results show that SVM provides high accuracy in detecting and classifying different types of bearing faults, making it a suitable method for real-time condition monitoring applications.