A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04777-0.pdf
Reference67 articles.
1. Kundu P, Chopra S, Lad BK (2019) Multiple failure behaviors identification and remaining useful life prediction of ball bearings. J Intell Manuf 30:1795–1807
2. Zhang Y, Hutchinson P, Lieven NA, Nunez-Yanez J (2020) Remaining useful life estimation using long short-term memory neural networks and deep fusion. IEEE Access 8:19033–19045
3. Huang C-G, Huang H-Z, Li Y-F (2019) A bidirectional LSTM prognostics method under multiple operational conditions. IEEE Trans Industr Electron 66(11):8792–8802
4. Yao F, He W, Wu Y, Ding F, Meng D (2022) Remaining useful life prediction of lithium-ion batteries using a hybrid model. Energy 248:123622
5. Jiang G, Zhou W, Chen Q, He Q, Xie P (2022) Dual residual attention network for remaining useful life prediction of bearings. Measurement 199:111424
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