Dually attentive multiscale networks for health state recognition of rotating machinery

Yadong Xu, Xiaoan Yan, Beibei Sun and Zheng Liu

Reliability Engineering and System Safety, 2022, vol. 225, issue C

Abstract: 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.

Keywords: Convolutional neural network (CNN); Fault diagnosis; Vibration signals; Dually attentive multiscale module (DAMM); Feature reinforcement module (FRM); Dually attentive multiscale networks (DAMN) (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002654

DOI: 10.1016/j.ress.2022.108626

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