Unbalanced fault diagnosis of rolling bearing by using transfer Adaptive Boosting with squeeze-and-excitation attention convolutional neural network


 For practical fault diagnosis issues, the normal data are always much more than the fault data, so this paper will focus on how to accurately classify the unbalanced datasets. Comparing to an individual model, ensemble model can combine multiple models together, thereby achieving higher identification accuracy. In this paper, a transfer Adaptive Boosting (AdaBoost) with squeeze-and-excitation attention convolutional neural network (SEACNN) is proposed to tackle the unbalanced fault diagnosis issues of rolling bearing. Firstly, SEACNN is designed to extract the representative fault features and improve the identification performance. Secondly, a new AdaBoost is designed for SEACNN to effectively tackle the unbalanced fault dataset. Thirdly, transfer learning is adopted while the learned knowledge of a SEACNN estimator is sequentially transferred to the next one, and the weights are updated during this process. Substantial experiments are conducted to sufficiently evaluate the effectiveness of the proposed method.