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An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system

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  • Shao, Meng
  • Zhu, Xin-Jian
  • Cao, Hong-Fei
  • Shen, Hai-Feng

Abstract

The commercial viability of PEMFC (proton exchange membrane fuel cell) systems depends on using effective fault diagnosis technologies in PEMFC systems. However, many researchers have experimentally studied PEMFC (proton exchange membrane fuel cell) systems without considering certain fault conditions. In this paper, an ANN (artificial neural network) ensemble method is presented that improves the stability and reliability of the PEMFC systems. In the first part, a transient model giving it flexibility in application to some exceptional conditions is built. The PEMFC dynamic model is built and simulated using MATLAB. In the second, using this model and experiments, the mechanisms of four different faults in PEMFC systems are analyzed in detail. Third, the ANN ensemble for the fault diagnosis is built and modeled. This model is trained and tested by the data. The test result shows that, compared with the previous method for fault diagnosis of PEMFC systems, the proposed fault diagnosis method has higher diagnostic rate and generalization ability. Moreover, the partial structure of this method can be altered easily, along with the change of the PEMFC systems. In general, this method for diagnosis of PEMFC has value for certain applications.

Suggested Citation

  • Shao, Meng & Zhu, Xin-Jian & Cao, Hong-Fei & Shen, Hai-Feng, 2014. "An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system," Energy, Elsevier, vol. 67(C), pages 268-275.
    • Handle: RePEc:eee:energy:v:67:y:2014:i:c:p:268-275
    DOI: 10.1016/j.energy.2014.01.079
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    1. Zhang, Caizhi & Zhang, Yuqi & Wang, Lei & Deng, Xiaozhi & Liu, Yang & Zhang, Jiujun, 2023. "A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    2. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
    3. Kim, Kyunghyun & Kim, Jaeyeon & Choi, Heesoo & Kwon, Obeen & Jang, Yujae & Ryu, Sangbong & Lee, Heeyun & Shim, Kyuhwan & Park, Taehyun & Cha, Suk Won, 2023. "Pre-diagnosis of flooding and drying in proton exchange membrane fuel cells by bagging ensemble deep learning models using long short-term memory and convolutional neural networks," Energy, Elsevier, vol. 266(C).
    4. Park, Jin Young & Lim, In Seop & Choi, Eun Jung & Kim, Min Soo, 2021. "Fault diagnosis of thermal management system in a polymer electrolyte membrane fuel cell," Energy, Elsevier, vol. 214(C).
    5. Andújar, J.M. & Segura, F. & Isorna, F. & Calderón, A.J., 2018. "Comprehensive diagnosis methodology for faults detection and identification, and performance improvement of Air-Cooled Polymer Electrolyte Fuel Cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 193-207.
    6. Heng Zhang & Zhongyong Liu & Weilai Liu & Lei Mao, 2022. "Diagnosing Improper Membrane Water Content in Proton Exchange Membrane Fuel Cell Using Two-Dimensional Convolutional Neural Network," Energies, MDPI, vol. 15(12), pages 1-15, June.
    7. Oh, Hwanyeong & Lee, Won-Yong & Won, Jinyeon & Kim, Minjin & Choi, Yoon-Young & Han, Soo-Bin, 2020. "Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 277(C).
    8. Young Park, Jin & Seop Lim, In & Ho Lee, Yeong & Lee, Won-Yong & Oh, Hwanyeong & Soo Kim, Min, 2023. "Severity-based fault diagnostic method for polymer electrolyte membrane fuel cell systems," Applied Energy, Elsevier, vol. 332(C).
    9. Liu, Zhongyong & Sun, Yuning & Tang, Xiawei & Mao, Lei, 2024. "Enabling unsupervised fault diagnosis of proton exchange membrane fuel cell stack: Knowledge transfer from single-cell to stack," Applied Energy, Elsevier, vol. 360(C).
    10. Feng Han & Ying Tian & Qiang Zou & Xin Zhang, 2020. "Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System," Energies, MDPI, vol. 13(10), pages 1-18, May.
    11. Asensio, F.J. & San Martín, J.I. & Zamora, I. & Garcia-Villalobos, J., 2017. "Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems," Energy, Elsevier, vol. 123(C), pages 585-593.
    12. Xu, Shuhui & Wang, Yong & Wang, Zhi, 2019. "Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method," Energy, Elsevier, vol. 173(C), pages 457-467.
    13. Wu, Kangcheng & Du, Qing & Zu, Bingfeng & Wang, Yupeng & Cai, Jun & Gu, Xin & Xuan, Jin & Jiao, Kui, 2021. "Enabling real-time optimization of dynamic processes of proton exchange membrane fuel cell: Data-driven approach with semi-recurrent sliding window method," Applied Energy, Elsevier, vol. 303(C).
    14. Qunli Wu & Hongjie Zhang, 2019. "A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    15. Weiwei Huo & Weier Li & Chao Sun & Qiang Ren & Guoqing Gong, 2022. "Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine," Energies, MDPI, vol. 15(6), pages 1-15, March.
    16. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    17. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    18. Benslama Sami & Nasri Sihem & Salsabil Gherairi & Cherif Adnane, 2019. "A Multi-Agent System for Smart Energy Management Devoted to Vehicle Applications: Realistic Dynamic Hybrid Electric System Using Hydrogen as a Fuel," Energies, MDPI, vol. 12(3), pages 1-20, February.
    19. Pei, Pucheng & Li, Yuehua & Xu, Huachi & Wu, Ziyao, 2016. "A review on water fault diagnosis of PEMFC associated with the pressure drop," Applied Energy, Elsevier, vol. 173(C), pages 366-385.
    20. Won, Jinyeon & Oh, Hwanyeong & Hong, Jongsup & Kim, Minjin & Lee, Won-Yong & Choi, Yoon-Young & Han, Soo-Bin, 2021. "Hybrid diagnosis method for initial faults of air supply systems in proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 180(C), pages 343-352.
    21. Yuan, Yi & Chen, Li & Lyu, Xingbao & Ning, Wenjing & Liu, Wenqi & Tao, Wen-Quan, 2024. "Modeling and optimization of a residential PEMFC-based CHP system under different operating modes," Applied Energy, Elsevier, vol. 353(PA).
    22. Yu Zang & Wei Shangguan & Baigen Cai & Huashen Wang & Michael G Pecht, 2019. "Methods for fault diagnosis of high-speed railways: A review," Journal of Risk and Reliability, , vol. 233(5), pages 908-922, October.
    23. Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.
    24. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.

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