A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery-Reference-Cited by-同舟云学术

A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery

Published:2023-03 Issue:3 Volume:53 Page:1982-1993
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Chen Zhuyun¹^ORCID,Liao Yixiao¹^ORCID,Li Jipu¹^ORCID,Huang Ruyi²^ORCID,Xu Lei¹,Jin Gang³^ORCID,Li Weihua²^ORCID

Affiliation:

1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

2. Pazhou Lab, Guangzhou, China

3. School of Mechanical and Automotive Engineering and the Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing, South China University of Technology, Guangzhou, China

Funder

National Key Research and Development Program of China

Basic and Applied Basic Research Foundation of Guangdong Province

Guangzhou Basic and Applied Basic Research Foundation

National Natural Science Foundation of China

Opening Project of Beijing Key Laboratory of Measurement Control of Mechanical and Electrical System Technology, Beijing Information Science Technology University

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10045549/09863762.pdf?arnumber=9863762

Reference44 articles.

1. Simultaneous fault type and severity identification using a two-branch domain adaptation network

2. Open Set Domain Adaptation by Backpropagation

3. Short-Time Wavelet Entropy Integrating Improved LSTM for Fault Diagnosis of Modular Multilevel Converter

4. Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning

5. Intelligent Fault Diagnosis for Chemical Processes Using Deep Learning Multimodel Fusion

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