Transferable mapping shift network for unsupervised domain adaptation using in vibration signal fault diagnosis under variable conditions
Author:
Funder
Project of the National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03143-y.pdf
Reference29 articles.
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2. Luo, S., Huang, X., Wang, Y., et al.: Transfer learning based on improved stacked autoencoder for bearing fault diagnosis. Knowl. Based Syst. 256, 109846 (2022)
3. Liu, Y., Shi, K., Li, Z., et al.: Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial networks. Measurement 180, 109553 (2021)
4. Li, X., Jiang, X., Wang, Q., et al.: Multi-perspective deep transfer learning model: a promising tool for bearing intelligent fault diagnosis under varying working conditions. Knowl. Based Syst. 243, 108443 (2023)
5. Ma, L., Jiang, B., Xiao, L., et al.: Digital twin-assisted enhanced meta-transfer learning for rolling bearing fault diagnosis. Mech. Syst. Signal Process. 200, 110490 (2023)
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