Dynamic fault detection and isolation for automotive engine air path by independent neural network model

Abstract

Fault detection and isolation have become one of the most important aspects of automobile design. A new fault detection and isolation scheme is developed for automotive engines in this paper. The method uses an independent radial basis function neural network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. Furthermore, another radial basis function network is used as a fault classifier to isolate occurred fault from other possible faults in the system by classifying fault characteristics embedded in the modelling errors. The performance of the developed scheme is assessed using an engine benchmark, the mean value engine model with Matlab/Simulink. Five faults have been simulated on the mean value engine model, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10–20% changes superimposed on the measured outputs of manifold pressure, manifold temperature and crankshaft speed sensors; the component fault considered is air leakage in the intake manifold; the actuator fault considered is the malfunction of fuel injector. The simulation results show that all the simulated faults can be clearly detected and isolated in dynamic conditions throughout the engine operating range.