Multiscale Deep Graph Convolutional Networks for Intelligent Fault Diagnosis of Rotor-Bearing System Under Fluctuating Working Conditions-Reference-Cited by-同舟云学术

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

Published:2023-01 Issue:1 Volume:19 Page:166-176
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zhao Xiaoli¹^ORCID,Yao Jianyong¹^ORCID,Deng Wenxiang¹^ORCID,Ding Peng²^ORCID,Zhuang Jichao²^ORCID,Liu Zheng³^ORCID

Affiliation:

1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China

2. School of Mechanical Engineering, Southeast University, Nanjing, China

3. School of Engineering, University of British Columbia, Kelowna, BC, Canada

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/9424/9944026/09740445.pdf?arnumber=9740445

Reference32 articles.

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2. A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines

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4. Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis

5. Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis

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