Fan Fault Diagnosis Based on Lightweight Multiscale Multiattention Feature Fusion Network
Zhixia Fan, Xiaogang Xu, Ruijun Wang, Huijie Wang
Abstract
Although the deep learning diagnosis model has been widely used in the fault diagnosis of rotating machinery. However, these methods lack the interpretability of the diagnostic process. In other words, it is still a difficult problem to understand that the structural function and the diagnosis process in the model correspond to each other. Therefore, this article discusses how to add multiscale and multiattention mechanism to lightweight network. From different scales, different dimensions, combined with the fault signal characteristics of centrifugal fan, the attention structure of cross layer fusion is designed. How to integrate different functions continuously and effectively to achieve better diagnostic performance is answered. The proposed lightweight multiscale multiattention feature fusion network adaptively recalibrates feature weights, which effectively enhances the fault feature learning ability and antinoise ability. Experimental results show that this network is stronger than other advanced diagnostic models.