Dually attentive multiscale networks for health state recognition of rotating

Recent advances in convolutional neural networks (CNN) have boosted the research on reliability monitoring of rotating machinery. In actual industry production, the mechanical equipment often operates under variable speed and strong noise conditions, so the discriminative fault-related features of the collected vibration signals are easily buried by interference information. Thus, it poses a huge challenge for the existing CNN models to achieve favorable diagnostic results. To address this issue, we put forward a dually attentive multiscale network (DAMN) for mechanical fault diagnosis. To begin with, a new hierarchical structure is built to make full use of the features from intermediate convolutional layers. Then, to explore abundant discriminative information from mechanical signals, a dually attentive multiscale module (DAMM) is introduced to guide the CNN model to extract multiscale and multilevel features. Further, a feature reinforcement module (FRM) is designed to expand receptive field and filter out unrelated interference information. Finally, embarking on the above improvements, an end-to-end CNN model named DAMN is built for intelligent fault diagnosis. Experimental results show that DAMN outperforms seven state-of-the-art methods for health recognition of rotating machinery.

Automated summary

This paper presents a dually attentive multiscale network (DAMN) for mechanical fault diagnosis. The network utilizes a hierarchical structure and a multiscale module to improve diagnostic performance, and a feature reinforcement module to reduce interference. Experimental results demonstrate that DAMN outperforms seven state-of-the-art methods for health recognition of rotating machinery.