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Fault Detection and Isolation using RBF Networks for Polymer Electrolyte Membrane Fuel Cell

Authors: Mahanijah Md Kamal., Dingli Yu


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.

Keywords: Fault Detection, Fault Isolation, radial basis function neural networks, Polymer electrolyte membrane fuel cell

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[1] L. A. M. Riascos, M. G. Sim├Áes and P. E. Miyagi, "Fault identification in fuel cells based on bayesian network diagnosis," in ABCM Symposium Series in Mechatronics, vol 2, pp. 757-764, 2006.
[2] 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," Journal of Power Sources, 192, pp. 216-223, 2009.
[3] J. T. Pukrushpan, H. Peng and A. G. Stefanopoulou, "Control-oriented modeling and analysis for automotive fuel cell systems," Journal of Dynamic Systems, Measurement and Control, vol. 126, pp. 14-25, 2004.
[4] J. T. Pukrushpan, A. G. Stefanopoulou and H. Peng, "Control of fuel cell breathing," IEEE Control Systems Magazines, vol. 24, no. 2, pp. 30-46, 2004.
[5] D. L. Yu, J. B. Gomm and D. Williams, "Sensor fault diagnosis in a chemical process via RBF neural networks," IFAC Journal: Control Engineering Practice, vol.7, no.1, pp. 49-55, 1999.
[6] M. K. Mahanijah and D.L. Yu, "Model-based fault detection for proton exchange membrane fuel cell systems," International Journal of Engineering, Science and Technology, vol. 3, no. 9, pp. 1-15, 2011.
[7] M. K. Mahanijah and D.L. Yu, "Fault detection and isolation for PEMFC systems under closed-loop control," in Proc. of UKACC International Conference on Control, UK, 2012, pp. 976-981.