Mahanijah Md Kamal. and Dingli Yu
Fault Detection and Isolation using RBF Networks for Polymer Electrolyte Membrane Fuel Cell
459 - 463
2013
7
4
International Journal of Electrical and Computer Engineering
https://publications.waset.org/pdf/4674
https://publications.waset.org/vol/76
World Academy of Science, Engineering and Technology
This paper presents a new method of fault detection and isolation (FDI) for polymer electrolyte membrane (PEM) fuel cell (FC) dynamic systems under an openloop 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 realtime 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 R2009aSimulink environment. The simulation results confirm the effectiveness of the proposed method for FDI under an openloop condition. By using this method, the RBF networks able to detect and isolate all five faults accordingly and accurately.
Open Science Index 76, 2013