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Fault detection and isolation for Polymer Electrolyte Membrane Fuel Cell systems by analyzing cell voltage generated space

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  • Li, Zhongliang
  • Outbib, Rachid
  • Giurgea, Stefan
  • Hissel, Daniel
  • Li, Yongdong

Abstract

This paper proposes a data-driven diagnostic approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems. Fault detection and isolation (FDI) is realized by analyzing individual cell voltages. A feature extraction method Fisher Discriminant Analysis (FDA) and a multi-class classification method Directed Acyclic Graph Support Vector Machine (DAGSVM) are utilized successively to extract the useful features from raw data and classify the extracted features into various classes related to health states. Experimental data of two different stacks are used to validate the proposed approach. The results show that five concerned faults can be detected and isolated with a high accuracy. Moreover, the light computational cost of the approach enhances the possibility of its online implementation.

Suggested Citation

  • Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Li, Yongdong, 2015. "Fault detection and isolation for Polymer Electrolyte Membrane Fuel Cell systems by analyzing cell voltage generated space," Applied Energy, Elsevier, vol. 148(C), pages 260-272.
    • Handle: RePEc:eee:appene:v:148:y:2015:i:c:p:260-272
    DOI: 10.1016/j.apenergy.2015.03.076
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    References listed on IDEAS

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    1. Pei, Pucheng & Chen, Huicui, 2014. "Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: A review," Applied Energy, Elsevier, vol. 125(C), pages 60-75.
    2. Bonvini, Marco & Sohn, Michael D. & Granderson, Jessica & Wetter, Michael & Piette, Mary Ann, 2014. "Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques," Applied Energy, Elsevier, vol. 124(C), pages 156-166.
    3. Najafi, Massieh & Auslander, David M. & Bartlett, Peter L. & Haves, Philip & Sohn, Michael D., 2012. "Application of machine learning in the fault diagnostics of air handling units," Applied Energy, Elsevier, vol. 96(C), pages 347-358.
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    Citations

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    Cited by:

    1. Han, Jaeyoung & Yu, Sangseok & Yi, Sun, 2017. "Adaptive control for robust air flow management in an automotive fuel cell system," Applied Energy, Elsevier, vol. 190(C), pages 73-83.
    2. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Jemei, Samir & Giraud, Alain & Rosini, Sebastien, 2016. "Online implementation of SVM based fault diagnosis strategy for PEMFC systems," Applied Energy, Elsevier, vol. 164(C), pages 284-293.
    3. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Giraud, Alain & Couderc, Pascal, 2019. "Fault diagnosis for fuel cell systems: A data-driven approach using high-precise voltage sensors," Renewable Energy, Elsevier, vol. 135(C), pages 1435-1444.
    4. Pahon, E. & Yousfi Steiner, N. & Jemei, S. & Hissel, D. & Moçoteguy, P., 2016. "A signal-based method for fast PEMFC diagnosis," Applied Energy, Elsevier, vol. 165(C), pages 748-758.
    5. Brindha Ramasubramanian & Rayavarapu Prasada Rao & Vijila Chellappan & Seeram Ramakrishna, 2022. "Towards Sustainable Fuel Cells and Batteries with an AI Perspective," Sustainability, MDPI, vol. 14(23), pages 1-27, November.
    6. Liu, Dengcheng & Lin, Rui & Feng, Bowen & Han, Lihang & Zhang, Yu & Ni, Meng & Wu, Sai, 2019. "Localised electrochemical impedance spectroscopy investigation of polymer electrolyte membrane fuel cells using Print circuit board based interference-free system," Applied Energy, Elsevier, vol. 254(C).
    7. Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.

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