Rotating Machine Systems Fault Diagnosis Using Semisupervised Conditional Random Field-Based Graph Attention Network-Reference-Cited by-同舟云学术

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Rotating Machine Systems Fault Diagnosis Using Semisupervised Conditional Random Field-Based Graph Attention Network

Published:2021 Issue: Volume:70 Page:1-10
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Tang Yao^ORCID,Zhang Xiaofei^ORCID,Zhai Yujia^ORCID,Qin Guojun^ORCID,Song Dianyi^ORCID,Huang Shoudao^ORCID,Long Zhuo^ORCID

Funder

National Science Foundation of China

Key Research and Development Program of Hunan Provence

Foundation of Key Laboratory of Science and Technology on Integrated Logistics Support

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9259274/09461746.pdf?arnumber=9461746

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SCG-GFFE: A Self-Constructed graph fault feature extractor based on graph Auto-encoder algorithm for unlabeled single-variable vibration signals of harmonic reducer;Advanced Engineering Informatics;2024-10

2. Multiscale Channel Attention-Driven Graph Dynamic Fusion Learning Method for Robust Fault Diagnosis;IEEE Transactions on Industrial Informatics;2024-09

3. Graph neural networks for anomaly detection and diagnosis in hydrogen extraction systems;Engineering Applications of Artificial Intelligence;2024-09

4. Source-free domain adaptation method for fault diagnosis of rotation machinery under partial information;Reliability Engineering & System Safety;2024-08

5. Improved adaptive war strategy optimization algorithm assisted-adaptive multi-head graph attention mechanism network for remaining useful life of complex equipment;AIP Advances;2024-05-01

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