A parallel GRU with dual-stage attention mechanism model integrating uncertainty quantification for probabilistic RUL prediction of wind turbine bearings
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DOI: 10.1016/j.ress.2023.109197
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- Pan, Tongyang & Chen, Jinglong & Ye, Zhisheng & Li, Aimin, 2022. "A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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Cited by:
- 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).
- Yang, Shilong & Tang, Baoping & Wang, Weiying & Yang, Qichao & Hu, Cheng, 2024. "Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
- Zhu, Dongping & Huang, Xiaogang & Ding, Zhixia & Zhang, Wei, 2024. "Estimation of wind turbine responses with attention-based neural network incorporating environmental uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Ni, Qing & Ji, J.C. & Feng, Ke & Zhang, Yongchao & Lin, Dongdong & Zheng, Jinde, 2024. "Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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Keywords
Wind turbine bearing; Remaining useful life prediction; Uncertainty quantification; GRU; Attention mechanism; Monte carlo dropout; Kernel density estimation;All these keywords.
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