Comparative Study of Decision Tree Models for Bearing Fault Detection and Classification

Fault diagnosis of bearings is essential in reducing failures and improving functionality and reliability of rotating machines. As vibration signals are non-linear and non-stationary, extracting features for dimension reduction and efficient fault detection is challenging. This study aims at evaluating performance of decision tree-based machine learning models in detection and classification of bearing fault data. A machine learning approach combining the tree-based classifiers with derived statistical features is proposed for localized fault classification. Statistical features are extracted from normal and faulty vibration signals though time domain analysis to develop tree-based models of AdaBoost (AD), classification and regression trees (CART), LogitBoost trees (LBT), and Random Forest trees (RF). The results confirm that machine learning classifiers have satisfactory performance and strong generalization ability in fault detection, and provide practical models for classify running state of the bearing.