Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery

Yadong Xu, Xiaoan Yan, Ke Feng, Xin Sheng, Beibei Sun and Zheng Liu

Reliability Engineering and System Safety, 2022, vol. 226, issue C

Abstract: CNN-based fault diagnosis approaches have achieved promising results in improving the safety and reliability of rotating machinery. Most of the existing CNN models are developed on the assumption that the collected data is high-quality. However, since rotating machinery usually operates under fluctuating conditions, the critical pulse information of the measured vibration signals is easily submerged in noise. To promote the adaptability of CNN in noisy industrial scenes, an attention-based multiscale denoising residual convolutional neural network (AM-DRCN) is put forward in this study. First of all, a multiscale denoising module (MDM) is introduced as the basic building unit to help the network explore multiscale features and filter out irrelevant information. Then, a feature enhancement module (FEM) is leveraged to expand the receptive field and make full use of the side-out features. Further, a joint attention module (JAM) is explored to integrate the extracted features effectively. Finally, a lightweight CNN model named AM-DRCN is developed based on the above improvements. The practicality and effectiveness of AM-DRCN for monitoring machine health and stability states are verified through three case studies.

Keywords: Fault diagnosis; Vibration signals; Multiscale denoising module (MDM); Feature enhancement module (FEM); Joint attention module (JAM) (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003386

DOI: 10.1016/j.ress.2022.108714

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