Semisupervised Momentum Prototype Network for Gearbox Fault Diagnosis Under Limited Labeled Samples

Author:

Zhang Xiaolong1,Su Zuqiang1ORCID,Hu Xiaolin2,Han Yan1ORCID,Wang Shuxian1

Affiliation:

1. Chongqing University of Posts and Telecommunications, Chongqing, China

2. Chongqing Innovation Center of Industrial Big-Data Company Ltd., Chongqing, China

Funder

National Natural Science Foundation of China

Science and Technology Research Program of Chongqing Municipal Education Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Reference42 articles.

1. A relation-based semisupervised method for gearbox fault diagnosis with limited labeled samples;ruan;IEEE Trans Instrum Meas,2021

2. A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning

3. On the importance of initialization and momentum in deep learning;sutskever;Proc Int Conf Mach Learn,2013

4. Learning to self-train for semi-supervised few-shot classification;sun;Proc Neural Inf Process Syst,2019

5. Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

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