Domain Conditioned Joint Adaptation Network for Intelligent Bearing Fault Diagnosis Across Different Positions and Machines-Reference-Cited by-同舟云学术

Domain Conditioned Joint Adaptation Network for Intelligent Bearing Fault Diagnosis Across Different Positions and Machines

Published:2023-02-15 Issue:4 Volume:23 Page:4000-4010
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Kuang Jiachen¹^ORCID,Xu Guanghua²^ORCID,Tao Tangfei¹^ORCID,Wu Qingqiang¹^ORCID,Han Chengcheng¹,Wei Fan¹^ORCID

Affiliation:

1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

2. School of Mechanical Engineering and the State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, China

Funder

National Key Research and Development Program of China

Ministry of Industry and Information Technology of China [Research and Verification of Remote Operation and Maintenance Standards for tunnel boring machine (TBM)]

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10043099/10017183.pdf?arnumber=10017183

Reference43 articles.

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3. Multiscale Deep Graph Convolutional Networks for Intelligent Fault Diagnosis of Rotor-Bearing System Under Fluctuating Working Conditions

4. Bi-classifier determinacy maximization for unsupervised domain adaptation;li;arXiv 2012 06995,2020

5. A deep partial adversarial transfer learning network for cross-domain fault diagnosis of machinery

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