Few-Shot Learning for Fault Diagnosis With a Dual Graph Neural Network
Author:
Affiliation:
1. College of Electronics and Information Engineering, Tongji University, Shanghai, China
2. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/9989328/09882368.pdf?arnumber=9882368
Reference30 articles.
1. Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images
2. A Deep Nonnegative Matrix Factorization Approach via Autoencoder for Nonlinear Fault Detection
3. Real-Time Fault Detection and Identification for MMC Using 1-D Convolutional Neural Networks
4. A multi-representation-based domain adaptation network for fault diagnosis
5. Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
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