Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data-Reference-Cited by-同舟云学术

Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data

Published:2022-03 Issue: Volume:191 Page:108663
ISSN:0003-682X
Container-title:Applied Acoustics
language:en
Short-container-title:Applied Acoustics

Author:

Li Guoqiang,Wu Jun,Deng Chao,Wei Meirong,Xu Xuebing

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Acoustics and Ultrasonics

Reference33 articles.

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2. Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization;Zhu;Appl Acoust,2021

3. Ensemble generalized multiclass support vector machine-based health evaluation of complex degradation systems;Wu;IEEE/ASME Trans Mechatron,2020

4. Fault diagnosis and severity analysis of rolling bearings using vibration image texture enhancement and multiclass support vector machines;Kumar Jha;Appl Acoust,2021

5. Reliability prediction of machinery with multiple degradation characteristics using double-Wiener process and Monte Carlo algorithm;Cheng;Mech Syst Sig Process,2019

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