Intelligent vibration signal denoising method based on non-local fully convolutional neural network for rolling bearings

• Published in: ISA transactions 2022-03, Vol.122, p.13-23
• Main Authors: Han, Haoran, Wang, Huan, Liu, Zhiliang, Wang, Jiayi
• Format: Article
• Language: English
• Subjects: Convolutional neural network; Non-local block; Rolling bearing; Vibration signal denoising
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/j.isatra.2021.04.022

Convolutional neural networks (CNNs) have been widely applied to machinery health management in recent years, whereas research on data-driven denoising methods is relatively limited. Therefore, this paper proposes a robust denoising method based on a non-local fully convolutional neural network (NL-FCNN). In this neural network, the Leaky-ReLU activation function is employed to maintain the information contained in the negative value of the signal. The wide kernel principle is also adopted to enlarge the receptive field. Lastly, the non-local means (NLM) is utilized to construct non-local block (NLB), which could efficiently enhance the long-range dependencies learning ability of the network. This block could enormously improve the denoising performance of the network. Moreover, the proposed method exhibits better performance compared with the three conventional denoising methods under multiple noise levels on the Case Western Reserve University (CWRU) motor bearing dataset. Ultimately, we also demonstrate its application to rolling bearing fault diagnosis. •A principle that uses a machine learning approach to deal with rolling bearing VSD is proposed.•A DL-based end-to-end network for the VSD task is presented.•The use of NLB in the VSD network is explored.