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

Authors: Amir H. Shamekhi, Mohammad H. Behroozi, Reza Chini

Addresses: Mechanical Eng. Department, K.N. Toosi University of Technology, Tehran, Iran. ' School of Mechanical Eng., University of Birmingham, Birmingham, B15 2TT, UK. ' Department of Eng. & Applied Science, Memorial University, P.O. Box 70, Canada

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.

Keywords: full-range fault diagnosis; mean value engine modelling; MVEM; artificial neural networks; ANNs; classifiers; normalised residuals; spark ignition engines; engine intake systems; SI engines; dynamic modelling; manifold air temperature; MAT sensorss.

DOI: 10.1504/IJVSMT.2011.039830

International Journal of Vehicle Systems Modelling and Testing, 2011 Vol.6 No.1, pp.21 - 55

Published online: 16 Oct 2014 *

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