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Fault Detection and Diagnosis Methods for Polymer Electrolyte Fuel Cell System

고분자전해질연료전지를 위한 고장 검출 및 진단 기술

  • LEE, WON-YONG (Fuel Cell Research Center, Korea Institute of Energy Research) ;
  • PARK, GU-GON (Fuel Cell Research Center, Korea Institute of Energy Research) ;
  • SOHN, YOUNG-JUN (Fuel Cell Research Center, Korea Institute of Energy Research) ;
  • KIM, SEUNG-GON (Department of Advance Energy and System Technology, University of Science and Technology) ;
  • KIM, MINJIN (Fuel Cell Research Center, Korea Institute of Energy Research)
  • 이원용 (한국에너지기술연구원 연료전지연구실) ;
  • 박구곤 (한국에너지기술연구원 연료전지연구실) ;
  • 손영준 (한국에너지기술연구원 연료전지연구실) ;
  • 김승곤 (과학기술연합대학원대학교 신에너지 및 시스템 기술) ;
  • 김민진 (한국에너지기술연구원 연료전지연구실)
  • Received : 2017.06.01
  • Accepted : 2017.06.30
  • Published : 2017.06.30

Abstract

Fuel cell systems have to satisfy acceptable operating reliability, sufficient lifetime and price to enter the market in competition with existing products. Fuel cells are made up of complex element technologies and various problems related to the failure of the components can affect the reliability and safety of the system. This problem can be overcome by introducing a monitoring and supervisory control system in addition to automatic control to detect the failure of the fuel cell quickly and properly diagnose the performance degradation. For the fault detection and diagnosis of polymer electrolyte fuel cells, the model based method using the theoretical superposition value and the non-model based method of checking the signal tendency or the converted signal characteristic can be applied. The methods analyzed in this paper can contribute to the development of integrated monitoring and control technology for the whole system as well as the stack.

Keywords

References

  1. Paris Agreement - Status of Ratification, http://unfccc.int/paris_agreement/items/9444.php.
  2. J. Lamrminie and A. Dicks, "Fuel Cell System Explained", John Wiley, USA, 2003.
  3. F. Barbir, "PEM fuel cells: theory and practice. Academic Press Series series editor", Elsevier Academic Press, Netherlands, 2005.
  4. DOE, "The Department of Energy Fuel Cell Technology Office, Multi-Year Research, Development, and Demonstration Plan", 2016.
  5. NEDO, "Road map of hydrogen and fuel cell 2010", http://www.nedo.go.jp/library/battery_hydrogen.html.
  6. W. Y. Lee, "Computer-aided practical application of faults detection and diagnosis techniques in energy systems", Korea Institute of Energy Research Report, 1999, KIER-996816.
  7. R. Isermann, "Process fault detection based on modeling and estimation methods-A survey", Automatica, Vol. 20, No. 4, 1984, pp. 387-404. https://doi.org/10.1016/0005-1098(84)90098-0
  8. P. M. Frank, "Fault Diagnosis in Dynamic System Using Analytical and Knowledge-based Redundancy - A Survey and Some New Results", Automatica, Vol. 20, No. 3, 1990, pp. 459-474.
  9. Q. Yang, A. Aitouche, and B.O. Bouamama, "Structural analysis for air supply system of fuel cell", Int. Renew Energy Congr., 2009, pp. 5-7.
  10. A. Benmouna, M. Becherif, D. Depernet, F. Gustin, H. S. Ramadan, and S. Fukuhara, "Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system", Int. J. Hydrogen Energy, Vol. 42, 2017, pp. 1534-1543. https://doi.org/10.1016/j.ijhydene.2016.07.181
  11. Q. Yang, A. Aitouche, and B. O. BouamamaO, "Structural Analysis for Fault Detection and Isolation in Fuel Cell Stack System, Sustainability in Energy and Buildings", Sustainability in Energy and Buildings, 2009, pp. 239-254.
  12. L. Placca and Kouta R, "Fault tree analysis for PEM fuel cell degradation process modelling", Int. J. Hydrogen Energy, Vol. 36, No. 12, 2011, pp. 393-405.
  13. M. Whiteley, S. Dummet, and L. Jackson, "Failure Mode and Effect Analysis, and Fault Tree Analysis of Polymer Electrolyte Membrane Fuel Cells", Int. J. Hydrogen Energy, Vol. 41, 2016, pp. 1187-1202. https://doi.org/10.1016/j.ijhydene.2015.11.007
  14. H. Li, Y. Tang, Z. Wang, Z. Shi, Z. Wu, D. Song, J. Zhang, K. Fatih, J. Zhang, H. Wang, Z. Liu, and R. Abouatallah, "A review of water flooding issues in the proton exchange membrane fuel cell", J. Power Sources, Vol. 178, No. 1, 2008, pp. 103-117. https://doi.org/10.1016/j.jpowsour.2007.12.068
  15. K. Brik, F. Ben Ammar, A. Djerdir, and A. Miraoui, "Causal and fault trees analysis of proton exchange membrane fuel cell degradation", J. Fuel Cell Sci. Technol., Vol. 12, 2015, pp. 051002. https://doi.org/10.1115/1.4031584
  16. S. Collong and R. Kouta, "Fault tree analysis of proton exchange membrane fuel cell system safety", Int. J. Hydrogen Energy, Vol. 41, 2015, pp. 8248-8260.
  17. R. Isermann, "Model-based fault-detection and diagnosis - status and applications", Annu. Rev. Control, Vol. 29, 2005, pp. 71-85. https://doi.org/10.1016/j.arcontrol.2004.12.002
  18. R. Isermann, "Supervision, fault-detection and fault-diagnosis methods - An introduction", Control Eng. Pract., Vol. 5, No. 3, 1997, pp. 639-652. https://doi.org/10.1016/S0967-0661(97)00046-4
  19. V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, and K. Yin, "A review of process fault detection and diagnosis part I: quantitative model based methods", Comput Chem. Eng, Vol. 27, 2003, pp. 293-311. https://doi.org/10.1016/S0098-1354(02)00160-6
  20. R. Petrone, Z. Zheng, D. Hissel, M. C. Pera, C. Pianese, M. Sorrentino, M. Becherif, and N. Yousfi-Steiner, "A review on model-based diagnosis methodologies for PEMFCs", Int. J. Hydrogen Energy, Vol. 38, 2013, pp. 7077-7091. https://doi.org/10.1016/j.ijhydene.2013.03.106
  21. S. C. Marco and M. D. Mueller, "A Fault Diagnosis Toolbox Applying Classification and Inference Methods", IFAC Proceedings, Vol. 42, No. 8, 2009, pp. 486-491.
  22. C. Nan, F. Khan, and M. T. Iqbal, "Real-time fault diagnosis using knowledge-based expert system", Process Saf. Environ. Prot., Vol. 86, 2008, pp. 55-71. https://doi.org/10.1016/j.psep.2007.10.014
  23. Z. Li, R. Outbib, D. Hissel, and S. Giurgea, "Data-Driven diagnosis of PEM fuel cell: A comparative study", Control Eng. Pract., Vol. 28, 2014, pp. 1-12. https://doi.org/10.1016/j.conengprac.2014.02.019
  24. J. M. House, W. Y. Lee, and D. R. Shin, "Classification techniques for Fault Detection and Diagnosis of an Ai-Handling Unit", ASHRAE Trans. CH-99-18-5, 1999, pp. 1087-1097.
  25. Z. Li, R. Outbib, S. Giurgea, D. Hissel, S. Jemei, A. Giraud, and S. Rosini, "Online implementation of SVM based fault diagnosis strategy for PEMFC systems", Applied Energy, Vol. 164, 2016, pp. 284-293. https://doi.org/10.1016/j.apenergy.2015.11.060
  26. T. Escobet, A. Nebet, and F. Mugica, "PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques", Eng. Appl. Artif. Intell., Vol. 36, 2014, pp. 40-53. https://doi.org/10.1016/j.engappai.2014.07.008
  27. M. Shao, X. J. Zhu, H. F. Cao, and H. F. Shen, "An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system", Energy, Vol. 67, 2014, pp. 268-275. https://doi.org/10.1016/j.energy.2014.01.079
  28. Z. Zheng, M. C. Pera, D. Hissel, M. Becherif, K. S. Agbli and Y. Li, "A double-fuzzy diagnostic methodology dedicated to online fault diagnosis of proton exchange membrane fuel cell stacks", J. Power Sources, Vol. 271, 2014, pp. 570-581. https://doi.org/10.1016/j.jpowsour.2014.07.157
  29. T. J. Rato and M. S. Reis, "On-line process monitoring using local measures of association: Part I - Detection performance", Chemometr. Intell. Lab., Vol. 142, No. 15, 2015, pp. 255-264. https://doi.org/10.1016/j.chemolab.2015.02.011
  30. T. J. Rato and M. S. Reis, "On-line process monitoring using local measures of association: Part II - Design Issues and Fault diagnosis", Chemometr. Intell. Lab., Vol. 142, No. 15, 2015, pp. 255-264. https://doi.org/10.1016/j.chemolab.2015.02.011
  31. K. C. Roes, R. J. Does, and Y. Schurink, "Shewhart-type control charts for individual observations", Journal of Quality Technology, Vol. 25, No. 3, 1993, pp. 188-198. https://doi.org/10.1080/00224065.1993.11979453
  32. R. P. Leger, W. J. Garland, and W. S. Poehlman, "Fault detection and diagnosis using statistical control charts and artificial neural networks", Artificial Intelligence in Engineering, Vol. 12, No. 1, 1998, pp. 35-47. https://doi.org/10.1016/S0954-1810(96)00039-8
  33. A. Khorasani, S. Asghari, A. Mokmeli, M. H. Shahsamand, and B. F. Imani, "A diagnosis method for identification of the defected cell(s) in the PEM fuel cells", Int. J. Hydrogen Energy, Vol. 35, 2010, pp. 9269-9275. https://doi.org/10.1016/j.ijhydene.2010.04.157
  34. G. Tian, S. Wasterlain, I. Endichi, D. Candusso, F. Harel, X. Francois, M. C. Pera, D. Hissel, and J. M. Kauffmann, "Diagnosis methods dedicated to the localization of failed cells within PEMFC stacks", J. Power Sources, Vol. 182, 2008, pp. 449-461. https://doi.org/10.1016/j.jpowsour.2007.12.038
  35. M. Ibrahim, U. Antoni, N. Y. Steiner, S. Jemei, C. Kokonendji, B. Ludwig B, P. Mocoteguy, and D. Hissel, "Signal-based diagnostics by wavelet Transform for proton exchange membrane fuel cell", Energy Proced., Vol. 74, 2015, pp. 1508-1516. https://doi.org/10.1016/j.egypro.2015.07.708
  36. E. Pahon, N. Yousfi Steiner, S. Jemei, D. Hissel, P. Mocoteguyc, "A signal-based method for fast PEMFC diagnosis", Appl. Energy, Vol. 165, No. 7, 2016, pp. 748-758. https://doi.org/10.1016/j.apenergy.2015.12.084
  37. N. R. Farnum, "Control charts for short runs: nonconstant process and measurement error", Journal of Quality Technology, Vol. 24, No. 3, 1992, pp. 138-144. https://doi.org/10.1080/00224065.1992.11979384
  38. D. C. Montgomery, J. B. Keats, G. C. Runger, and W. S. Messina, "Integrating statistical process control and engineering process control", Journal of Quality Technology, Vol. 26, No. 2, 1994, pp. 79-87. https://doi.org/10.1080/00224065.1994.11979508
  39. K. C. Roes, R. J. Does, and Y. Schurink, "Shewhart-type control charts for individual observations", Journal of Quality Technology, Vol. 25, No. 3, 1993, pp. 188-198. https://doi.org/10.1080/00224065.1993.11979453
  40. C. Damour, M. Benne, B. Grondin-Perez, and M. Bessafi, "Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition", J Power Sources, Vol. 299, 2015, pp. 596-603. https://doi.org/10.1016/j.jpowsour.2015.09.041
  41. B. Legros, P. X. Thivel, Y. Bultel, M. Boinet, and R. P. Nogueira, "Electrochemical Impedance and Acoustic Emission Survey of water desorption in nafion membranes", Electrochem Solid-State Lett, Vol. 12, 2009, pp. B116. https://doi.org/10.1149/1.3131728
  42. Z. Zheng, R. Petrone, M. C. Pera, D. Hissel, M. Becherif, C. Pianese C, N. Yousfi Steiner, and M. Sorrentino, "A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems", Int. J. Hydrogen Energy, Vol. 38, 2013, pp. 8914-8926. https://doi.org/10.1016/j.ijhydene.2013.04.007
  43. K. Teranishi, S. Tsushima, and S. Hirai S. "Analysis of water transport in PEFCs by magnetic resonance imaging measurement", J. Electrochem. Soc., Vol. 153, 2006, pp. A664-A668. https://doi.org/10.1149/1.2167954
  44. J. Wu, X. Z. Yuan, H. Wang, M. Blanco, J. J. Martin, and J. Zhang, "Diagnostic tools in PEM fuel cell research: Part I Electrochemical techniques", Int. J. Hydrogen Energy, Vol. 33, 2008, pp. 1735-1746. https://doi.org/10.1016/j.ijhydene.2008.01.013
  45. X. Yuan, H. Wang, J. C. Sun, and J. Zhang, "AC impedance technique in PEM fuel cell diagnosis-A review", Int. J. Hydrogen Energy, Vol. 32, 2007, pp. 4365-4380. https://doi.org/10.1016/j.ijhydene.2007.05.036
  46. G. Mousa, F. Golnaraghi, J. Devaal, and A. Young, "Detecting proton exchange membrane fuel cell hydrogen leak using electrochemical impedance spectroscopy method", J. Power Sources, Vol. 246, 2014, pp. 110-116. https://doi.org/10.1016/j.jpowsour.2013.07.018
  47. S. Fukuhara, N. Marx, K. Ettihir, L. Boulon, Y. Ait-Amirat, and M. Becherif, "A lumped fluidic model of an anode chamber for fault tolerant strategy design", Int. J. Hydrogen Energy, Vol. 41, No. 50, 2016, pp. 37-47.
  48. T. Escobet, D. Feroldi, S. de Lira, V. Puig, J. Quevedo, J. Riera, and M. Serra, "Model-based fault diagnosis in PEM fuel cell systems", J. Power Sources, Vol. 192, 2009, pp. 216-223. https://doi.org/10.1016/j.jpowsour.2008.12.014
  49. G. Tina, S. Wasterlain, D. Candusso, F. Harel, D. Hissel, and X. Francois, "Identification of failed cells inside PEMFC stacks in two cases: Anode/cathode crossover and anode/cooling compartment leak", Int. J. Hydrogen Energy, Vol. 35, 2010, pp. 2772-2776. https://doi.org/10.1016/j.ijhydene.2009.05.015
  50. S. Lira, V. Puig, J. Quevedo, and A. Husar, "LPV observer design for PEM fuel cell system: Application to fault detection", Int. J. Power Source, Vol. 196, 2011, pp. 4298-4305. https://doi.org/10.1016/j.jpowsour.2010.11.084
  51. W. Y. Lee, G. G. Park, T. H. Yang, Y. G. Yoon, and C. S. Kim, "Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks", Int. J. Hydrogen Energy, Vol. 29, 2004, pp. 961-966.
  52. A. Saengrung, A. Abtahi, and A. Zilouchian, "Neural network model for a commercial PEM fuel cell system", J. Power Sources, Vol. 172, 2007, pp. 749-759. https://doi.org/10.1016/j.jpowsour.2007.05.039
  53. K. M. Kamal, D. Yu, and D. L. Yu, "Fault detection and isolation for PEM fuel cell l Stack with independent RFB model", Eng. Appl. Artif. Intell., Vol. 28, 2014, pp. 52-63. https://doi.org/10.1016/j.engappai.2013.10.002
  54. N. Y. Steiner, D. Hissel, P. H. Mocoteguy, and D. Candusso, "Diagnosis of polymer electrolyte fuel cells failure modes (flooding & drying out) by neural networks modeling", Int. J. Hydrogen Energy, Vol. 36, 2011, pp. 3067-3075. https://doi.org/10.1016/j.ijhydene.2010.10.077
  55. D. Hissel, M. C. Pera, and J. M. Kauffmann, "Diagnosis of automotive fuel cell power generators", J. Power Sources, Vol. 128, 2004, pp. 239-246. https://doi.org/10.1016/j.jpowsour.2003.10.001
  56. E. Lechartier, E. Laffly, M. C. Pera, R. Gouriveau, D. Hissel, and N. Zerhouni, "Proton exchange membrane fuel cell behavioral model suitable for prognostics", Int. J. Hydrogen Energy, Vol. 40, 2015, pp. 8384-8397. https://doi.org/10.1016/j.ijhydene.2015.04.099
  57. Z. D. Zhong, X. J. Zhu, and G. Y. Cao, "Modeling a PEMFC by a support vector machine", J. Power Sources, Vol. 160, No. 1, 2006, pp. 293-298. https://doi.org/10.1016/j.jpowsour.2006.01.040
  58. X. Li, G. Y. Cao, and X. J. Zhu, "Modeling and control of PEMFC based on least squares support vector machines", Energy Convers. Manage., Vol. 47, No. 7, 2006, pp. 1032-1050. https://doi.org/10.1016/j.enconman.2005.04.002
  59. C. H. Li, X. J. Zhu, G. Y. Cao, S. Sui, and M. R. Hu, "Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines", J. Power Sources, Vol. 175, No. 1, 2008. pp. 303-316. https://doi.org/10.1016/j.jpowsour.2007.09.049
  60. J. Hua, L. Lu, M. Ouyang, J. Li, and L. Xu, "Proton exchange membrane fuel cell system diagnosis based on the signed directed graph method", J. Power Sources, Vol. 196, 2011, pp. 5881-5888. https://doi.org/10.1016/j.jpowsour.2011.03.008
  61. N. Chatti, B. Ould-Bouamama, A. L. Gehin, and R. Merzouki, "Merging Bond Graph and Signed Directed Graph to improve FDI procedure", European Control Conference (ECC), 2013, pp. 1457-1462.