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

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dc.contributor.author Du, H
dc.contributor.author Zhang, L
dc.contributor.author Shi, X
dc.date.accessioned 2010-05-28T09:48:33Z
dc.date.issued 2001-01
dc.identifier.citation IMechE, Part D, Journal of Automobile Engineering, 2001, 215 (6), pp. 761 - 767
dc.identifier.issn 0954-4070
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/9276
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.
dc.format Y
dc.publisher Professional Engineering Publishing Ltd
dc.title Reconstructing Cylinder Pressure from Vibration Signals Based on Radial Basis Function Network
dc.type Journal Article
dc.description.version Published
dc.parent IMechE, Part D, Journal of Automobile Engineering
dc.journal.volume 6
dc.journal.volume 215
dc.journal.number 6 en_US
dc.publocation UK en_US
dc.identifier.startpage 761 en_US
dc.identifier.endpage 767 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 091304 Dynamics, Vibration and Vibration Control
dc.for 090205 Hybrid Vehicles and Powertrains
dc.for 090204 Automotive Safety Engineering
dc.personcode 123171
dc.percentage 40 en_US
dc.classification.name Hybrid Vehicles and Powertrains en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords reconstruction, cylinder pressure, vibration signal, internal combustion engine, radial basis function network en_US
dc.description.keywords Science & Technology
dc.description.keywords Technology
dc.description.keywords Engineering, Chemical
dc.description.keywords Engineering
dc.description.keywords ENGINEERING, CHEMICAL
dc.description.keywords POSITIVE REAL SYSTEMS
dc.description.keywords MODEL-PREDICTIVE CONTROL
dc.description.keywords DISTURBANCE REJECTION
dc.description.keywords NONLINEAR-SYSTEMS
dc.description.keywords DESIGN
dc.description.keywords IDENTIFICATION
dc.description.keywords REACTOR
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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