Deep residual network for enhanced fault diagnosis of rotating machinery. (November 2020)

• Record Type: Journal Article
• Authors: Xiong, Shoucong; Shi, Tielin
• Is Part Of: Journal of physics. Volume 1707(2020)
• Publication Date: 2020-11
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1707/1/012010
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Deep residual network (DRN) is a recently-developed powerful algorithm in the deep learning filed. This paper introduces the superiority of DRN into the fault diagnosis of rotating machinery for simplifying traditional diagnosing process as well as enhancing predicting performance. DRN can not only extract features automatically from raw or processed signals but also benefit from its especially deep architecture to continually improve representation capacity without worrying gradient divergence issues. The functions of DRN result from the unique structure design called residual building block, which will be described clearly with the network overall architecture in this paper. Additionally, the comparison of the DRN with other machine learning-based and neural network-based fault diagnosis methods are presented.