A cross-domain intelligent fault diagnosis method based on deep subdomain adaptation for few-shot fault diagnosis
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Anhui Provincial Education Departmen
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04749-4.pdf
Reference42 articles.
1. Wang B, Wang B, Ning Y (2022) A novel transfer learning fault diagnosis method for rolling bearing based on feature correlation matching. Measurement Science and Technology 33(12):125006
2. Xiao Y, Shao H, Han S et al (2022) Anovel joint transfer network for unsupervised bearing fault diagnosis from simulation domain to experimental domain. IEEE/ASME Transactions on Mechatronics 27(6):5254–5263
3. Yang B, Lei Y, Xu S et al (2021) An optimal transport-embedded similarity measure for diagnostic knowledge transferability analytics across machines. IEEE Transactions on Industrial Electronics 69(7):7372–7382
4. Peng B, Xia H, Lv X et al (2022) An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network. Applied Intelligence 52(3):3051–3065
5. Wu Y, Zhao R, Jin W et al (2021) Intelligent fault diagnosis of rolling bearings using a semi-supervised convolutional neural network. Applied Intelligence 51(4):2144–2160
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1. A novel meta-learning network with adversarial domain-adaptation and attention mechanism for cross-domain for train bearing fault diagnosis;Measurement Science and Technology;2024-09-13
2. Structured Prediction in Latent Subspace for Unsupervised Fault Diagnosis With Small and Imbalanced Data;IEEE Sensors Journal;2024-08-01
3. Multiscale dilated convolution and swin-transformer for small sample gearbox fault diagnosis;Applied Intelligence;2024-06-13
4. A multi-source subdomain adaptation fault diagnosis method based on unidirectional movement of the target domain;Measurement Science and Technology;2024-02-21
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