Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery. (October 2022) - Xu, Yadong
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. Highlights: A multiscale denoising module (MDM) is developed to explore multilevel features and filter out irrelevant information. A feature enhancement module (FEM) is used to make the CNN model perceive more contextual information. A joint attention module (JAM) is introduced to make the CNN model pay more attention to discriminative features. An end-to-end CNN model called AM-DRCN is developed, and its effectiveness has been verified by extensive experimental results.