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%.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - 2000|
|Event||SOFT COMPUTING TECHNIQUES AND APPLICATIONS - |
Duration: 1 Jan 2000 → …
|Name||ADVANCES IN SOFT COMPUTING|
|Conference||SOFT COMPUTING TECHNIQUES AND APPLICATIONS|
|Period||1/01/00 → …|
- engine misfire detection
- neural networks
- multi-layer Perceptron
- radial basis function
- condition monitoring
- fault classification
Li, Y., Pont, MJ., Parikh, CR., & Jones, NB. (2000). Using a combination of RBFN, MLP and kNN classifiers for engine misfire detection. In Unknown Host Publication (pp. 46-51). (ADVANCES IN SOFT COMPUTING).