Graph Convolutional Neural Network for Intelligent Fault Diagnosis of Machines via Knowledge Graph-Reference-Cited by-同舟云学术

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Graph Convolutional Neural Network for Intelligent Fault Diagnosis of Machines via Knowledge Graph

Published:2024-05 Issue:5 Volume:20 Page:7862-7870
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Mao Zehui¹^ORCID,Wang Huan¹^ORCID,Jiang Bin¹^ORCID,Xu Juan²^ORCID,Guo Huifeng³^ORCID

Affiliation:

1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

3. State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen, China

Funder

National Key Research and Development Program of China

ZTE Industry-University-Institute Cooperation Funds

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10521545/10454020.pdf?arnumber=10454020

Reference20 articles.

1. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

2. A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

3. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

4. Signal-Model-Based Fault Diagnosis in Windings of Synchronous Generator

5. Multiple-Model-Based Diagnosis of Multiple Faults With High-Speed Train Applications Using Second-Level Adaptation

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