Efficient and Explainable Bearing Condition Monitoring with Decision Tree-Based Feature Learning

Bearings are critical components in rotating machinery, where early fault detection is essential to prevent unexpected failures and reduce maintenance costs. This study presents an efficient and interpretable framework for bearing condition monitoring by combining the Wavelet Packet Transform (WPT)-based feature extraction with a Decision Tree (DT) classifier. The WPT technique decomposes vibration signals into multiple frequency bands to extract energy-based features that capture key fault characteristics. Leveraging these features, the DT classifier provides transparent diagnostic rules, enabling a clear understanding of the decision-making process. The proposed method offers a superior balance between diagnostic accuracy, computational efficiency, and explainability compared to conventional black-box models. It is well suited for real-time and resource-constrained industrial applications. Furthermore, feature importance analysis reveals the most influential frequency components associated with different fault types, offering valuable insights for predictive maintenance strategies. The proposed WPT-DT framework represents a practical and scalable solution for intelligent fault diagnosis in the context of Industry 4.0 and smart maintenance systems.