Bearing fault diagnosis under class unbalanced data based on deep learning

Abstract

There is a huge difference in the number of operating samples and failure samples in industrial production. If the diagnostic model is trained through deep learning under an unbalanced data set, it will make the model recognize the faulty samples as normal samples. Aiming at this problem, an adaptive focus loss function mechanism is proposed. It can avoid over-learning large-scale samples during smallbatch imbalance training. At the same time, to improve the generalization ability of fault samples, a pretraining data enhancement mechanism is proposed. By using the rolling element bearing fault diagnosis data set, the effectiveness of the two mechanisms is verified. The two mechanisms can not only complete training tasks under unbalanced data and improve generalization ability.