A Method Based on Multi-Graph Convolutional Network with Graph Fusion for Rolling Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

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A Method Based on Multi-Graph Convolutional Network with Graph Fusion for Rolling Bearing Fault Diagnosis

Published:2023-11-10 Issue: Volume: Page:230-235
ISSN:
Container-title:2023 3rd International Conference on Digital Society and Intelligent Systems (DSInS)
language:
Short-container-title:

Author:

Wang Qichen¹,Li Chaofan¹,Han Yufei¹,Li Tianrui²,Zhang Fan²

Affiliation:

1. Southwest Jiaotong University,Tangshan Institute,Tangshan,China,063000

2. Southwest Jiaotong University,School of Computing and Artificial Intelligence,Chengdu,China,611756

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10455090/10455096/10455163.pdf?arnumber=10455163

Reference11 articles.

1. The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study

2. Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

3. GCG: Graph Convolutional network and gated recurrent unit method for high-speed train axle temperature forecasting

4. Graph Convolutional Network-Based Method for Fault Diagnosis Using a Hybrid of Measurement and Prior Knowledge

5. Oversmoothing Relief Graph Convolutional Network-Based Fault Diagnosis Method With Application to the Rectifier of High-Speed Trains

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