Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection

Li, Y, Pont, MJ, Parikh, CR and Jones, NB 2000, Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection , in: Recent Advances in Soft Computing Techniques and Applications, 1-2 July 1999, Leicester, UK.

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Abstract

In this paper, we apply radial basis function networks (RBFN), multilayer perceptron (MLP) and a conventional statistical classifier, k-nearest neighbour (kNN), to the detection of misfires in a petrol engine. Used alone, each classifier is shown to provide a similar level of performance. We then demonstrate that by combining these techniques using a simple `majority voting' algorithm, the overall performance of the system is improved by approximately 10%.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Engine misfire detection, neural networks, multi-layer perceptron, radial basis function, condition monitoring, fault classification
Schools:	Schools > School of Computing, Science and Engineering
Journal or Publication Title:	Advances in Soft Computing
Publisher:	Physica-Verlag: Heidelberg
Refereed:	Yes
Series Name:	ADVANCES IN SOFT COMPUTING
Funders:	Non funded research
Depositing User:	Yuhua Li
Date Deposited:	27 Jul 2015 16:55
Last Modified:	05 Apr 2016 18:18
URI:	http://usir.salford.ac.uk/id/eprint/33143

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