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One-dimensional multi-scale domain adaptive network for bearing-fault diagnosis under varying working conditions
Wang K(王锴)1,2,3; Zhao W(赵威)1,2,3,4; Xu AD(徐皑冬)1,2,3; Zeng P(曾鹏)1,2,3; Yang SK(杨顺昆)5
Department工业控制网络与系统研究室
Source PublicationSensors (Switzerland)
ISSN1424-8220
2020
Volume20Issue:21Pages:1-17
Indexed ByEI
EI Accession number20204409417957
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China (grant no. 2018YFB1702202)
Keyworddomain adaptation fault diagnosis convolutional neural network multi-scale features distribution discrepancy
Abstract

Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27845
Collection工业控制网络与系统研究室
Corresponding AuthorXu AD(徐皑冬)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Recommended Citation
GB/T 7714
Wang K,Zhao W,Xu AD,et al. One-dimensional multi-scale domain adaptive network for bearing-fault diagnosis under varying working conditions[J]. Sensors (Switzerland),2020,20(21):1-17.
APA Wang K,Zhao W,Xu AD,Zeng P,&Yang SK.(2020).One-dimensional multi-scale domain adaptive network for bearing-fault diagnosis under varying working conditions.Sensors (Switzerland),20(21),1-17.
MLA Wang K,et al."One-dimensional multi-scale domain adaptive network for bearing-fault diagnosis under varying working conditions".Sensors (Switzerland) 20.21(2020):1-17.
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