Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 226, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2022.108714
Keywords
Fault diagnosis; Vibration signals; Multiscale denoising module (MDM); Feature enhancement module (FEM); Joint attention module (JAM)
Funding
- National Key Research and Development Program of China [2019YFB2006404]
- Fundamental Research Funds for the Central Universities [2242019K3DN05]
- National Natural Science Foundation of China [52005265]
- Postgraduate Research & Practice Innovation Program of Jiangsu Province, China [SJCX21_0044]
- China Scholarship Council (CSC)
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This study proposes an attention-based multiscale denoising residual convolutional neural network (AM-DRCN) for fault diagnosis in noisy industrial scenes. By introducing a multiscale denoising module, a feature enhancement module, and a joint attention module, the model effectively extracts key features and filters out noise.
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.
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