A novel transfer learning network with adaptive input length selection and lightweight structure for bearing fault diagnosis-Reference-Cited by-同舟云学术

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A novel transfer learning network with adaptive input length selection and lightweight structure for bearing fault diagnosis

Published:2023-08 Issue: Volume:123 Page:106395
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Tang Guiting,Yi Cai,Liu Lei,Yang Xingguo,Xu Du,Zhou Qiuyang,Lin Jianhui

Funder

Natural Science Foundation of Sichuan Province

Case Western Reserve University

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Chongqing Municipal Education Commission

State Key Laboratory of Traction Power

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference33 articles.

1. Layer normalization;Ba,2016

2. Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds;Cao;J. Manuf. Syst.,2022

3. Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network;Chen;IEEE Transactions on Industrial Informatics,2020

4. Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data;Cheng;Neurocomputing,2020

5. Domain-adversarial training of neural networks;Ganin,2017

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1. SDANet: Sub-domain adaptive network for multi-fault diagnosis of lithium-ion battery packs;Journal of Energy Storage;2024-09

2. A lightweight transformer based on feature fusion and global–local parallel stacked self-activation unit for bearing fault diagnosis;Measurement;2024-08

3. Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery;Engineering Applications of Artificial Intelligence;2024-08

4. A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples;Engineering Applications of Artificial Intelligence;2024-07

5. Bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features for bearing fault diagnosis;Engineering Applications of Artificial Intelligence;2024-07

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