Open AccessDeep residual network for enhanced fault diagnosis of rotating machinery
Author(s) -
Shoucong Xiong,
Tielin Shi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1707/1/012010
Subject(s) - residual , deep learning , computer science , artificial intelligence , fault (geology) , process (computing) , artificial neural network , block (permutation group theory) , representation (politics) , architecture , network architecture , machine learning , algorithm , geology , art , geometry , mathematics , computer security , seismology , politics , political science , law , visual arts , operating system
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.