An approach to full-range fault diagnosis of spark ignition engines’ intake system using normalised residual and neural network classifiers

Abstract

One essential part of automated diagnosis systems for spark ignition (SI) engines is due to elements of air path system. The faults that occur in this subsystem can result in deviation in the air-fuel ratio, which causes increased emissions, misfire and especially loss of power and drivability problems. In this article, a model-based diagnosis system for the air-path of an SI engine is developed. In addition, a non-linear four-state dynamic model of an SI engine is used, and then the diagnosis system is designed in the framework of an Artificial Neural Network (ANN) classifier. Simulation results show that the constructed diagnosis system for seven fault modes considering all three kinds of common fault, including the manifold air temperature (MAT) sensor fault, which has been comparatively less evaluated than other elements, is applied successfully. As another remarkable aspect of this work, all classes of faults are diagnosed in their full possible over-reading (positive) and under-reading (negative) ranges.