Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain-Reference-Cited by-同舟云学术

Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain

Published:2022-12 Issue:6 Volume:27 Page:5254-5263
ISSN:1083-4435
Container-title:IEEE/ASME Transactions on Mechatronics
language:
Short-container-title:IEEE/ASME Trans. Mechatron.

Author:

Xiao Yiming¹,Shao Haidong¹^ORCID,Han SongYu¹,Huo Zhiqiang²^ORCID,Wan Jiafu³^ORCID

Affiliation:

1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China

2. Institute of Health Informatics, University College London, London, U.K.

3. Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou, China

Funder

National Natural Science Foundation of China

Natural Science Fund for Excellent Young Scholars of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/3516/9985402/09789130.pdf?arnumber=9789130

Reference42 articles.

1. Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning

2. Deep transfer learning with joint adaptation networks;long;Proc 34th Int Conf Mach Learn,2017

3. Deep Dynamic Adaptive Transfer Network for Rolling Bearing Fault Diagnosis With Considering Cross-Machine Instance

4. FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults

5. Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis

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