Reconstructing Cylinder Pressure from Vibration Signals Based on Radial Basis Function Network

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Show simple item record Du, Haiping en_US Zhang, Liang en_US Shi, Xizhi en_US
dc.contributor.editor en_US 2010-05-28T09:48:33Z 2010-05-28T09:48:33Z 2001 en_US
dc.identifier 2006007031 en_US
dc.identifier.citation Du Haiping, Zhang Liang, and Shi Xizhi 2001, 'Reconstructing Cylinder Pressure from Vibration Signals Based on Radial Basis Function Network', Professional Engineering Publishing Ltd, vol. 215, no. 6, pp. 761-767. en_US
dc.identifier.issn 0954-4070 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.description.abstract This paper presents an approach to reconstruct internal combustion engine cylinder pressure from the engine cylinder head vibration signals, using radial basis function (RBF) networks. The relationship between the cylinder pressure and the engine cylinder head vibration signals is analysed first. Then, an RBF network is applied to establish the non-parametric mapping model between the cylinder pressure time series and the engine cylinder head vibration signal frequency series. The structure of the RBF network model is presented. The fuzzy c-means clustering method and the gradient descent algorithm are used for selecting the centres and training the output layer weights of the RBF network respectively. Finally, the validation of this approach to cylinder pressure reconstruction from vibration signals is demonstrated on a two-cylinder, four-stroke direct injection diesel engine, with data from a wide range of speed and load settings. The prediction capabilities of the trained RBF network model are validated against measured data. en_US
dc.language en_US
dc.publisher Professional Engineering Publishing Ltd en_US
dc.title Reconstructing Cylinder Pressure from Vibration Signals Based on Radial Basis Function Network en_US
dc.parent IMechE, Part D, Journal of Automobile Engineering en_US
dc.journal.volume 215 en_US
dc.journal.number 6 en_US
dc.publocation UK en_US
dc.identifier.startpage 761 en_US
dc.identifier.endpage 767 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 090205 en_US
dc.personcode 996919 en_US
dc.personcode 0000032378 en_US
dc.personcode 0000030818 en_US
dc.percentage 40 en_US Hybrid Vehicles and Powertrains en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords reconstruction, cylinder pressure, vibration signal, internal combustion engine, radial basis function network en_US

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