{"title":"Fault Detection and Isolation using RBF Networks for Polymer Electrolyte Membrane Fuel Cell","authors":"Mahanijah Md Kamal., Dingli Yu","volume":76,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":459,"pagesEnd":464,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/4674","abstract":"
This paper presents a new method of fault detection and isolation (FDI) for polymer electrolyte membrane (PEM) fuel cell (FC) dynamic systems under an open-loop scheme. This method uses a radial basis function (RBF) neural network to perform fault identification, classification and isolation. The novelty is that the RBF model of independent mode is used to predict the future outputs of the FC stack. One actuator fault, one component fault and three sensor faults have been introduced to the PEMFC systems experience faults between -7% to +10% of fault size in real-time operation. To validate the results, a benchmark model developed by Michigan University is used in the simulation to investigate the effect of these five faults. The developed independent RBF model is tested on MATLAB R2009a\/Simulink environment. The simulation results confirm the effectiveness of the proposed method for FDI under an open-loop condition. By using this method, the RBF networks able to detect and isolate all five faults accordingly and accurately.<\/p>\r\n","references":"[1] L. A. M. Riascos, M. G. Sim\u251c\u00c1es and P. E. Miyagi, \"Fault identification\r\nin fuel cells based on bayesian network diagnosis,\" in ABCM\r\nSymposium Series in Mechatronics, vol 2, pp. 757-764, 2006.\r\n[2] T. Escobet, D. Feroldi, S. De Lira, V. Puig, J. Quevedo, J. Riera and M.\r\nSerra, \"Model-based fault diagnosis in PEM fuel cell systems,\" Journal\r\nof Power Sources, 192, pp. 216-223, 2009.\r\n[3] J. T. Pukrushpan, H. Peng and A. G. Stefanopoulou, \"Control-oriented\r\nmodeling and analysis for automotive fuel cell systems,\" Journal of\r\nDynamic Systems, Measurement and Control, vol. 126, pp. 14-25, 2004.\r\n[4] J. T. Pukrushpan, A. G. Stefanopoulou and H. Peng, \"Control of fuel cell\r\nbreathing,\" IEEE Control Systems Magazines, vol. 24, no. 2, pp. 30-46,\r\n2004.\r\n[5] D. L. Yu, J. B. Gomm and D. Williams, \"Sensor fault diagnosis in a\r\nchemical process via RBF neural networks,\" IFAC Journal: Control\r\nEngineering Practice, vol.7, no.1, pp. 49-55, 1999.\r\n[6] M. K. Mahanijah and D.L. Yu, \"Model-based fault detection for proton\r\nexchange membrane fuel cell systems,\" International Journal of\r\nEngineering, Science and Technology, vol. 3, no. 9, pp. 1-15, 2011.\r\n[7] M. K. Mahanijah and D.L. Yu, \"Fault detection and isolation for\r\nPEMFC systems under closed-loop control,\" in Proc. of UKACC\r\nInternational Conference on Control, UK, 2012, pp. 976-981.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 76, 2013"}