A Flexible Monitoring Framework via Dynamic-Multilayer Graph Convolution Network-Reference-Cited by-同舟云学术

A Flexible Monitoring Framework via Dynamic-Multilayer Graph Convolution Network

Published:2023 Issue: Volume:72 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhang Xian-Jie¹^ORCID,Ding Xiao¹^ORCID,Zhang Hai-Feng¹^ORCID,Pan Dong-Hui¹^ORCID,Zhong Kai²^ORCID

Affiliation:

1. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, School of Mathematical Science, Anhui University, Hefei, China

2. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China

Funder

National Natural Science Foundation of China

Anhui Provincial Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10147795.pdf?arnumber=10147795

Reference33 articles.

1. A Node-Level PathGraph-Based Bearing Remaining Useful Life Prediction Method

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

3. Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes

4. Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis

5. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

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