关键词: 滚动轴承, 故障诊断, 特征提取, 随机森林

Abstract: Due to selection difficulties for different bearing data feature, and low accuracy problems of single classifier method in the fault diagnosis of rolling bearing, this paper proposes a rolling bearing fault diagnosis algorithm with random forest based on Classification And Regression Tree（CART）. Random forest is an ensemble learning method which contains a variety of classifiers. The accuracy of rolling bearing fault diagnosis is improved by “integrated” thought of random forest. First, time domain statistical indicators are extracted from the vibration signals of rolling bearings and will be used as feature vectors. Then, the random forest algorithm is utilized for the fault diagnosis of rolling bearing. Compared with the traditional algorithm （SVM, kNN and ANN） and single CART, diagnostic results proposed in this paper indicate that random forest algorithm has high diagnostic accuracy by using the bearing data of SQI-MFS experimental platform.

Key words: rolling bearing, fault diagnosis, feature extraction, random forest