Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load
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DOI: 10.1016/j.ress.2023.109123
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- 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).
- Liu, Wenli & Liu, Fenghua & Fang, Weili & Love, Peter E.D., 2024. "Causal discovery and reasoning for geotechnical risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Zuo, Jian & Cadet, Catherine & Li, Zhongliang & Bérenguer, Christophe & Outbib, Rachid, 2024. "A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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Keywords
Proton exchange membrane fuel cell; Dynamic load; Empirical mode decomposition; Time-frequency-energy spectrum; Symbolic representation gated recurrent unit; Remaining useful life;All these keywords.
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