IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v233y2023ics0951832023000388.html
   My bibliography  Save this article

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

Author

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

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.

Suggested Citation

  • Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:reensy:v:233:y:2023:i:c:s0951832023000388
    DOI: 10.1016/j.ress.2023.109123
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023000388
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109123?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Jiusi & Jiang, Yuchen & Wu, Shimeng & Li, Xiang & Luo, Hao & Yin, Shen, 2022. "Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    4. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    5. Chen, Junxiong & Feng, Xiong & Jiang, Lin & Zhu, Qiao, 2021. "State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network," Energy, Elsevier, vol. 227(C).
    6. Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine & Gouriveau, Rafael, 2021. "Proton exchange membrane fuel cell system prognostics and decision-making: Current status and perspectives," Renewable Energy, Elsevier, vol. 179(C), pages 2277-2294.
    7. Liu, Hao & Chen, Jian & Hissel, Daniel & Lu, Jianguo & Hou, Ming & Shao, Zhigang, 2020. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    8. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    9. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Jouin, Marine & Bressel, Mathieu & Morando, Simon & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine & Jemei, Samir & Hilairet, Mickael & Ould Bouamama, Belkacem, 2016. "Estimating the end-of-life of PEM fuel cells: Guidelines and metrics," Applied Energy, Elsevier, vol. 177(C), pages 87-97.
    12. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    13. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Wang, Chu & Li, Zhongliang & Outbib, Rachid & Dou, Manfeng & Zhao, Dongdong, 2022. "Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 305(C).
    15. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    16. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
    17. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    18. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    19. Mezzi, Rania & Yousfi-Steiner, Nadia & Péra, Marie Cécile & Hissel, Daniel & Larger, Laurent, 2021. "An Echo State Network for fuel cell lifetime prediction under a dynamic micro-cogeneration load profile," Applied Energy, Elsevier, vol. 283(C).
    20. Chang, Yang & Fang, Huajing & Zhang, Yong, 2017. "A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery," Applied Energy, Elsevier, vol. 206(C), pages 1564-1578.
    21. Chen, Jinglong & Jing, Hongjie & Chang, Yuanhong & Liu, Qian, 2019. "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 372-382.
    22. Lorenzo, Charles & Bouquain, David & Hibon, Samuel & Hissel, Daniel, 2021. "Synthesis of degradation mechanisms and of their impacts on degradation rates on proton-exchange membrane fuel cells and lithium-ion nickel–manganese–cobalt batteries in hybrid transport applicati," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alfarizi, Muhammad Gibran & Ustolin, Federico & Vatn, Jørn & Yin, Shen & Paltrinieri, Nicola, 2023. "Towards accident prevention on liquid hydrogen: A data-driven approach for releases prediction," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    2. Fernández, Juan & Chiachío, Juan & Barros, José & Chiachío, 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).
    3. 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).
    4. Lv, Jianfeng & Shen, Xiaoning & Gao, Yabin & Liu, Jianxing & Sun, Guanghui, 2024. "The seasonal-trend disentangle based prognostic framework for PEM fuel cells," Renewable Energy, Elsevier, vol. 228(C).
    5. 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).
    6. Zuo, Jian & Steiner, Nadia Yousfi & Li, Zhongliang & Hissel, Daniel, 2024. "Health management review for fuel cells: Focus on action phase," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    2. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    3. He, Yuxuan & Su, Huai & Zio, Enrico & Peng, Shiliang & Fan, Lin & Yang, Zhaoming & Yang, Zhe & Zhang, Jinjun, 2023. "A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    7. Benaggoune, Khaled & Yue, Meiling & Jemei, Samir & Zerhouni, Noureddine, 2022. "A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 313(C).
    8. Lv, Jianfeng & Shen, Xiaoning & Gao, Yabin & Liu, Jianxing & Sun, Guanghui, 2024. "The seasonal-trend disentangle based prognostic framework for PEM fuel cells," Renewable Energy, Elsevier, vol. 228(C).
    9. Zhang, Chu & Hu, Haowen & Ji, Jie & Liu, Kang & Xia, Xin & Nazir, Muhammad Shahzad & Peng, Tian, 2023. "An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC," Applied Energy, Elsevier, vol. 330(PA).
    10. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    11. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    13. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    14. Fu, Song & Lin, Lin & Wang, Yue & Guo, Feng & Zhao, Minghang & Zhong, Baihong & Zhong, Shisheng, 2024. "MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Zhu, Ting & Chen, Zhen & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2024. "Adaptive staged remaining useful life prediction of roller in a hot strip mill based on multi-scale LSTM with multi-head attention," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    16. Zou, Xinyu & Tao, Laifa & Sun, Lulu & Wang, Chao & Ma, Jian & Lu, Chen, 2023. "A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    18. Zhang, Zhendong & He, Hongwen & Wang, Yaxiong & Quan, Shengwei & Chen, Jinzhou & Han, Ruoyan, 2024. "A novel generalized prognostic method of proton exchange membrane fuel cell using multi-point estimation under various operating conditions," Applied Energy, Elsevier, vol. 357(C).
    19. He, Jiabei & Tian, Yi & Wu, Lifeng, 2022. "A hybrid data-driven method for rapid prediction of lithium-ion battery capacity," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    20. Park, Hyung Jun & Kim, Nam H. & Choi, Joo-Ho, 2024. "A robust health prediction using Bayesian approach guided by physical constraints," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:233:y:2023:i:c:s0951832023000388. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.