Fault detection of pem fuel cell for vehicle systems using neutral network models

This paper presents the neural network modeling method to perform fault detection for proton exchange membrane fuel cell dynamic systems under an open-loop scheme. these methods use a radial basis function neural network and a multilayer perceptron neural network to perform fault identification. five types of faults which commonly happened in the vehicle systems have been introduced to the modified benchmark model developed by michigan university. the developed algorithm of rbf and mlp network models are implemented on matlab/simulink environment using the healthy data sets and faulty data sets obtained from the simulation. all five simulated faults have been successfully detected where the residual is designed sensitive to fault amplitude as low as +10% of their nominal values. thus,it is possible to apply the developed algorithm to real dynamics system of vehicles for monitoring and maintenance purposes. © 2015 penerbit utm press. all rights reserved.