Bearing fault diagnosis method based on comprehensive information divergence and improved BP-AdaBoost algorithm

Abstract

Bearings are an important part of rotating systems, and the long-term safe operation of mechanical equipment utilizing bearings is closely related to the bearing state; so, bearing fault diagnosis is of great significance. In this paper, a bearing fault diagnosis method based on comprehensive information divergence and improved BP (back propagation)-AdaBoost algorithm is proposed. First, the time domain, frequency domain, and time-frequency domain features under different states of the bearing are extracted to form a feature set. Then, the importance of each feature in fault classification is obtained by using the comprehensive information divergence index, and the feature sequence with decreasing importance is obtained. Finally, the most important features are selected as the input of the improved BP-AdaBoost classification model to train and obtain the bearing fault classification model. The experimental results show that the method has a good identification effect on bearing faults, and the stability of the model is high.