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

•HHT-based method eliminates dynamic load noise and extracts degradation features.•Symbol-based GRU achieves reliable and efficient long-term prognostics.•Proposed data-driven method provides competitive prognostics horizon and accuracy.•Multiple failure thresholds can assess prognostics consistency...

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Veröffentlicht in:Reliability engineering & system safety Jg. 233; H. May; S. 109123
Hauptverfasser: Wang, Chu, Dou, Manfeng, Li, Zhongliang, Outbib, Rachid, Zhao, Dongdong, Zuo, Jian, Wang, Yuanlin, Liang, Bin, Wang, Peng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.05.2023
Elsevier
Schlagworte:
Automatic
Dynamic load
Electric power
Empirical mode decomposition
Engineering Sciences
Proton exchange membrane fuel cell
Remaining useful life
Symbolic representation gated recurrent unit
Time-frequency-energy spectrum
Symbolic representation gated recurrent unit
Empirical mode decomposition
Proton exchange membrane fuel cell
Time-frequency-energy spectrum
Remaining useful life
Dynamic load
ISSN:0951-8320, 1879-0836
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Zusammenfassung:•HHT-based method eliminates dynamic load noise and extracts degradation features.•Symbol-based GRU achieves reliable and efficient long-term prognostics.•Proposed data-driven method provides competitive prognostics horizon and accuracy.•Multiple failure thresholds can assess prognostics consistency and generalizability. 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.
ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2023.109123