http://www.cnr.it/ontology/cnr/individuo/prodotto/ID66140
Reconstruction of in-cylinder pressure in a Diesel engine from vibration signal using a RBF neural network model (Articolo in rivista)
- Type
- Label
- Reconstruction of in-cylinder pressure in a Diesel engine from vibration signal using a RBF neural network model (Articolo in rivista) (literal)
- Anno
- 2011-01-01T00:00:00+01:00 (literal)
- Alternative label
Bizon K. 1, Continillo G. 1, Mancaruso E. 2, Vaglieco B.M. 2 (2011)
Reconstruction of in-cylinder pressure in a Diesel engine from vibration signal using a RBF neural network model
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Bizon K. 1, Continillo G. 1, Mancaruso E. 2, Vaglieco B.M. 2 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- SAE Paper 2011-24-0161 (ISSN 0148-7191).
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1) Università del Sannio, Benevento; 2) Istituto Motori, CNR, Napoli. (literal)
- Titolo
- Reconstruction of in-cylinder pressure in a Diesel engine from vibration signal using a RBF neural network model (literal)
- Abstract
- This study aims at building an efficient and robust radial
basis function (RBF) artificial neural network (ANN), 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. The RBF network is trained
using measurements from different engine operating
conditions. Training data are composed of time series from
the accelerometer and corresponding measured in-cylinder
pressure signals. The RBF network is then validated using
data not included in training and the results show good
correspondence between measured and reconstructed
pressure signal. Various network parameters are used to
optimize the network quality. The accuracy of the predicted
pressure signals is analyzed in terms of mean square error and
of a number of parameters, such as maximum pressure, peak
location, and mass burned fraction (MBF). Robustness is
sought with respect to changes in the engine parameters as
well as with respect to changes in the nature of the fuel. The
encouraging results indicate that the prediction model based
on RBF neural network can be incorporated in the design of
fuel-independent real-time control of diesel engines. (literal)
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