Domain Discrepancy-Guided Contrastive Feature Learning for Few-Shot Industrial Fault Diagnosis Under Variable Working Conditions
Author:
Affiliation:
1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, China
2. School of Engineering, University of British Columbia, Kelowna, BC, Canada
Funder
National Natural Science Foundation of China
Guangxi Science and Technology through Major Project Guike
Scientific Research and Technology Development in Liuzhou
Fundamental Research Funds for the Central Universities
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/ielx7/9424/10214691/10032199.pdf?arnumber=10032199
Reference35 articles.
1. Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
2. A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition
3. A systematic review of deep transfer learning for machinery fault diagnosis
4. An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition
5. Meta-learning for few-shot bearing fault diagnosis under complex working conditions
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