Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis With Few Labeled Data
Author:
Affiliation:
1. School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
2. Chinese Academy of Sciences, Institute of High Energy Physics, Beijing, China
Funder
National Natural Science Foundation of China
Basic and Applied Basic Research Foundation of Guangdong Province
Intelligent Manufacturing prognostics and health management (PHM) Innovation Team Program
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/9717300/09794441.pdf?arnumber=9794441
Reference50 articles.
1. Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning
2. Learning to compare: Relation network for few-shot learning;sung;arXiv 1711 06025,2017
3. A new graph-based semi-supervised method for surface defect classification
4. Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes
5. Graph Cardinality Preserved Attention Network for Fault Diagnosis of Induction Motor under Varying Speed and Load Condition
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