Multiscale Wavelet Prototypical Network for Cross-Component Few-Shot Intelligent Fault Diagnosis
Author:
Affiliation:
1. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
Funder
Key-Area and Development Program of Guangdong Province
National Natural Science Foundation of China
China Post-Doctoral Science Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/09999323.pdf?arnumber=9999323
Reference38 articles.
1. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults
2. Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis
3. Cross-Category Mechanical Fault Diagnosis Based on Deep Few-Shot Learning
4. An intelligent fault diagnosis model based on deep neural network for few-shot fault diagnosis
5. Data augmentation for rolling bearing fault diagnosis using an enhanced few-shot Wasserstein auto-encoder with meta-learning
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