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

Li, Y; Pont, MJ; Parikh, CR; Jones, NB

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%.

Citation

Li, Y., Pont, M., Parikh, C., & Jones, N. (1999, July). Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. Presented at Recent Advances in Soft Computing Techniques and Applications, Leicester, UK

• Presentation Conference Type: Other
• Conference Name: Recent Advances in Soft Computing Techniques and Applications
• Conference Location: Leicester, UK
• Start Date: Jul 1, 1999
• End Date: Jul 2, 1999
• Publication Date: Jul 1, 2000
• Deposit Date: Jul 27, 2015
• Series Title: ADVANCES IN SOFT COMPUTING
• Keywords: Engine misfire detection, neural networks, multi-layer perceptron, radial basis function, condition monitoring, fault classification
• Additional Information: Event Type : Conference