Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load

Chu Wang, Manfeng Dou, Zhongliang Li, Rachid Outbib, Dongdong Zhao, Jian Zuo, Yuanlin Wang, Bin Liang and Peng Wang

Reliability Engineering and System Safety, 2023, vol. 233, issue C

Abstract: Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC). For the prognostics of PEMFC operating under dynamic load, the challenges come from extracting degradation features, improving prediction accuracy, expanding the prognostics horizon, and reducing computational cost. To address these issues, this work proposes a data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is used to extract health indicator in dynamic operating conditions and symbolic-based gated recurrent unit model is used to enhance the accuracy of life prediction. Comparing with other state-of-the-art methods, the proposed data-driven prognostics approach provides a competitive prognostics horizon with lower computational cost. The prognostics performance shows consistency and generalizability under different failure threshold settings.

Keywords: Proton exchange membrane fuel cell; Dynamic load; Empirical mode decomposition; Time-frequency-energy spectrum; Symbolic representation gated recurrent unit; Remaining useful life (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:233:y:2023:i:c:s0951832023000388

DOI: 10.1016/j.ress.2023.109123

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