Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloTowards on-line prediction of the in-cylinder pressure in diesel engines from engine vibration using artificial neural networks
Anno di pubblicazione2013
Formato
  • Elettronico
  • Cartaceo
Autore/iBizon K.; Continillo G.; Mancaruso E.; Vaglieco B.M.
Affiliazioni autoriBizon K.- Universita Del Sannio, Italy Continillo G. - Università del Sannio, Italy
Autori CNR e affiliazioni
  • EZIO MANCARUSO
  • BIANCA MARIA VAGLIECO
Lingua/e
  • inglese
AbstractThis study aims at building efficient and robust artificial neural networks (ANN) able to reconstruct the in-cylinder pressure of Diesel engines and to identify engine conditions 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 artificial neural network is meant to be efficient in terms of response time, i.e. fast enough for on-line use. In addition, robustness is sought in order to provide flexibility in terms of operation parameters. Here we consider a feed-forward neural network based on radial basis functions (RBF) for signal reconstruction, and a feed-forward multi-layer perceptron network with tan-sigmoid transfer function for signal classification. The networks are trained using measurements from a three-cylinder real engine for various operating conditions. The RBF neural network is trained with time series from in-cylinder pressure signals and vibration signals measured on a cylinder which is distant from the one in which the pressure signal is measured. The accuracy of the predicted pressure signals is analyzed in terms of mean square error and in terms of a number of pressure-derived parameters. The location of the accelerometer has little influence on the accuracy of the reconstruction. This is confirmed also by the fact that the perceptron network, constructed in the second part of the work, is able to distinguish, from the accelerometer signal, among motored and fired conditions for any of the cylinders. Here, training data are again composed of time series obtained from the accelerometer, plus the corresponding target classes (fired/non-fired). Despite of the noisy character of the vibration signal and the distance from the cylinders, the perceptron network classifies correctly almost 100% of the signals. Copyright © 2013 SAE International.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali8
RivistaSAE technical paper series
Attiva dal 1979
Editore: Society of Automotive Engineers - Warrendale, Penn.
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0148-7191
Titolo chiave: SAE technical paper series
Titolo proprio: SAE technical paper series.
Titolo abbreviato: SAE tech. pap. ser.
Titolo alternativo: Society of Automotive Engineers technical paper series
Numero volume della rivista-
Fascicolo della rivista-
DOI10.4271/2013-24-0137
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84890346337)
Parole chiaveSimulation and Mideling, Vibration, Diesel/Compression Ignition engine
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-84890346337&partnerID=q2rCbXpz
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioniSAE PAPER 2013-24-0137
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-
Allegati
2013P2796 (documento privato )
Tipo documento: application/pdf