A Multiscale Graph Convolutional Neural Network Framework for Fault Diagnosis of Rolling Bearing
Author:
Affiliation:
1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China
2. School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Funder
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10172223.pdf?arnumber=10172223
Reference37 articles.
1. Semi-supervised classification with graph convolutional networks;kipf;arXiv 1609 02907,2016
2. Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
3. A Review on Explainability in Multimodal Deep Neural Nets
4. A Cross Working Condition Multiscale Recursive Feature Fusion Method for Fault Diagnosis of Rolling Bearing in Multiple Working Conditions
5. Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
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