Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloApplication of RBF neural networks for real-time pressure prediction in a diesel engine
Anno di pubblicazione2015
  • Elettronico
  • Cartaceo
Autore/iK.Bizon, G.Continillo, E.Mancaruso. B.M.Vaglieco
Affiliazioni autoriK.Bizon - Institute of Chemical and Process Engineering - Crocow University of Technology (Polland) G.continillo - Dept of Engineering -Unviersità degli Studi del Sannio - Benevento (Italy)
Autori CNR e affiliazioni
  • inglese
AbstractThis study aims at building efficient and robust artificial neural networks (ANN) in order to reconstruct the in-cylinder pressure of a Diesel engine starting from the signal of a low-cost accelerometer placed on the engine block. The accelerometer is a perfect non-intrusive replacement for expensive probes and is prospectively suitable for production vehicles. In this view, the ANN is meant to be efficient in terms of response time. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. The neural network considered here is based on radial basis functions (RBF). The network is trained using measurements from a single cylinder Diesel engine operating under varying conditions. Training data are composed of time series from the accelerometer and corresponding measured in-cylinder pressure signals. The network parameters, including the spread parameter of the radial basis functions and the number of neurons, are used to optimize the network quality. The RBF network is then validated. The results show good correspondence between the measured and the simulated pressure signal. The accuracy of the simulated pressure signals is analyzed in terms of mean square error and in terms of a number of parameters, such as pressure peak and mass burned fraction (MBF), and their angular locations. Robustness is sought with respect to changes in the engine parameters and in the nature of the fuel. The encouraging results indicate that the reconstruction model based on RBF neural networks can be incorporated in the design of fuel-independent real-time control of Diesel engines
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da497
Pagine a508
Pagine totali12
RivistaInternational Journal of Engineering & Technology
Editore: Science Publishing Corporation
Lingua: inglese
ISSN: 2227-524X
Titolo chiave: International Journal of Engineering & Technology
Numero volume della rivista4
Fascicolo della rivista4
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveAccelerometer; In-Cylinder Pressure; Internal Combustion Engine; Neural Networks; Radial Basis Functions
Link (URL, URI)-
Titolo parallelo-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IM — Istituto motori
Moduli/Attività/Sottoprogetti CNR
  • ET.P02.003.008 : Diagnostica e sensoristica per l'ottimizzazione dei processi termo-fluidodinamici nei motori a combustione interna
Progetti Europei-
2015P2713 (documento privato )
Tipo documento: application/pdf