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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| Vydáno v: | Reliability engineering & system safety Ročník 233; číslo May; s. 109123 |
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| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.05.2023
Elsevier |
| Témata: | |
| ISSN: | 0951-8320, 1879-0836 |
| On-line přístup: | Získat plný text |
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| Abstract | •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. |
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| AbstractList | 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. •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. |
| ArticleNumber | 109123 |
| Author | Wang, Peng Dou, Manfeng Zuo, Jian Zhao, Dongdong Wang, Chu Liang, Bin Wang, Yuanlin Li, Zhongliang Outbib, Rachid |
| Author_xml | – sequence: 1 givenname: Chu orcidid: 0000-0002-7515-3875 surname: Wang fullname: Wang, Chu email: chu.wang@etu.univ-amu.fr organization: School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China – sequence: 2 givenname: Manfeng surname: Dou fullname: Dou, Manfeng organization: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China – sequence: 3 givenname: Zhongliang surname: Li fullname: Li, Zhongliang email: zhongliang.li@lis-lab.fr organization: LIS Lab (UMR CNRS 7020), Aix-Marseille Université, Marseille 13397, France – sequence: 4 givenname: Rachid surname: Outbib fullname: Outbib, Rachid organization: LIS Lab (UMR CNRS 7020), Aix-Marseille Université, Marseille 13397, France – sequence: 5 givenname: Dongdong surname: Zhao fullname: Zhao, Dongdong organization: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China – sequence: 6 givenname: Jian surname: Zuo fullname: Zuo, Jian organization: LIS Lab (UMR CNRS 7020), Aix-Marseille Université, Marseille 13397, France – sequence: 7 givenname: Yuanlin surname: Wang fullname: Wang, Yuanlin organization: School of Automation, Northwestern Polytechnical University, Xi'an 710072, China – sequence: 8 givenname: Bin surname: Liang fullname: Liang, Bin organization: Department of Automation, Tsinghua University, Beijing 100084, China – sequence: 9 givenname: Peng surname: Wang fullname: Wang, Peng email: wang_peng@xatu.edu.cn organization: School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China |
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| Keywords | Symbolic representation gated recurrent unit Empirical mode decomposition Proton exchange membrane fuel cell Time-frequency-energy spectrum Remaining useful life Dynamic load |
| Language | English |
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| Snippet | •HHT-based method eliminates dynamic load noise and extracts degradation features.•Symbol-based GRU achieves reliable and efficient long-term... Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC). For the prognostics of PEMFC... |
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| SubjectTerms | 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 |
| Title | Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load |
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