Learning of physical health timestep using the LSTM network for remaining useful life estimation
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DOI: 10.1016/j.ress.2022.108717
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Cited by:
- Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
- Fernández, Juan & ChiachÃo, Juan & Barros, José & ChiachÃo, Manuel & Kulkarni, Chetan S., 2024. "Physics-guided recurrent neural network trained with approximate Bayesian computation: A case study on structural response prognostics," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
- Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
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Keywords
Prognostics and health management; Remaining useful life; Data quality; Physical health timestep; Long short-term memory neural network;All these keywords.
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