Meta-learning for few-shot bearing fault diagnosis under complex working conditions-Reference-Cited by-同舟云学术

Meta-learning for few-shot bearing fault diagnosis under complex working conditions

Published:2021-06 Issue: Volume:439 Page:197-211
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Li Chuanjiang^ORCID,Li Shaobo,Zhang Ansi,He Qiang,Liao Zihao,Hu Jianjun

Funder

Department of Education of Guizhou Province

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference55 articles.

1. Deep learning for prognostics and health management: State of the art, challenges, and opportunities;Rezaeianjouybari;Meas. J. Int. Meas. Confed.,2020

2. Applications of machine learning to machine fault diagnosis: A review and roadmap;Lei;Mech. Syst. Signal Process.,2020

3. A survey on Deep Learning based bearing fault diagnosis;Hoang;Neurocomputing.,2019

4. A novel method of bearing fault diagnosis in time-frequency graphs using inceptionresnet and deformable convolution networks;Li;IEEE Access,2020

5. Artificial intelligence for fault diagnosis of rotating machinery: A review;Liu;Mech. Syst. Signal Process.,2018

Cited by 169 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Few-shot bearing fault diagnosis by semi-supervised meta-learning with graph convolutional neural network under variable working conditions;Measurement;2025-01

2. A novel transformer-based few-shot learning method for intelligent fault diagnosis with noisy labels under varying working conditions;Reliability Engineering & System Safety;2024-11

3. Learning to generalize with latent embedding optimization for few- and zero-shot cross domain fault diagnosis;Expert Systems with Applications;2024-11

4. An information fusion-based meta transfer learning method for few-shot fault diagnosis under varying operating conditions;Mechanical Systems and Signal Processing;2024-11

5. A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges;Journal of Sound and Vibration;2024-11