Adaptive neural network model based predictive control for air-fuel ratio of SI engines
Engineering Applications of Artificial Intelligence
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Fault detection (FD) scheme is developed for automotive engines in this paper. The method uses an independent Radial Basis Function (RBF) Neural Network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. The method is developed and the performance assessed using the engine benchmark, the Mean Value Engine Model (MVEM) with Matlab/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The simulation results showed that all the simulated faults can be clearly detected in the dynamic condition throughout the operating range.