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Reconstruction of In-Cylinder Pressure in a Diesel Engine from Vibration Signal Using a RBF Neural Network Model
Technical Paper
2011-24-0161
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
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.
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Citation
Bizon, K., Continillo, G., Mancaruso, E., and Vaglieco, B., "Reconstruction of In-Cylinder Pressure in a Diesel Engine from Vibration Signal Using a RBF Neural Network Model," SAE Technical Paper 2011-24-0161, 2011, https://doi.org/10.4271/2011-24-0161.Also In
References
- Taglialatela, F. Cesario, N. Porto, M. Merola, S. et al. “Use of Accelerometers for Spark Advance Control of SI Engines,” SAE Int. J. Engines 2 1 971 981 2009 10.4271/2009-01-1019
- Chiavola, O. Chiatti, G. Arnone, L. Manelli, S. “Combustion Characterization in Diesel Engine via Block Vibration Analysis,” SAE Technical Paper 2010-01-0168 2010 10.4271/2010-01-0168
- Taglialatela, F. Cesario, N. Lavorgna, M. Merola, S.S. et al. “Use of Engine Crankshaft Speed for Determination of Cylinder Pressure Parameters,” SAE Technical Paper 2009-24-0108 2009 10.4271/2009-24-0108
- Gu, F. Jacob, P. J. Ball, A. D. “A RBF neural network model for cylinder pressure reconstruction in internal combustion engines” IEE Colloquium on Modeling and Signal Processing for Fault Diagnosis 1996
- Saraswati, S. Chand, S. “Reconstruction of cylinder pressure for SI engine using recurrent neural networks” Neural Computing and Applications 19 935 944 2010
- Johnsson, R. “Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signal” Mechanical Systems and Signal Processing 20 1923 1940 2006
- Leonhardt, S. Ludwig, C. Schwarz, R. “Real-time supervision for Diesel engine injection” Control Engineering Practice 3 1003 1010 1995
- Wang, J. Zhang, Y. Xiong, Q. Ding, X. “NOx Prediction by Cylinder Pressure Based on RBF Neural Network in diesel Engine” International Conference on Measuring Technology and Mechatronics Automation 2010
- Yoon, M. Lee, K. Sunwoo, M. “A method for combustion phasing control using cylinder pressure measurement in a CRDI diesel engine” Mechatronics 17 469 479 2007
- Gao, Y. Randall, R. B. “Reconstruction of diesel engine cylinder pressure using a time domain smoothing technique” Mechanical Systems and Signal Processing 13 709 722 1999
- Moro, D. Cavina, Ponti, F. “In-cylinder pressure reconstruction based on instantaneous engine speed signal” Journal of Engineering for Gas Turbines and Power 124 220 225 2002
- Antoni, J. Daniere, J. Gullet, F. “Effective vibration analysis of IC engines using cyclostationarity. Part II - new results on the reconstruction of the cylinder pressures” Journal of Sound and Vibrations 257 839 856 2002
- Broomhead, D. S. Lowe, D. “Multivariable functional interpolation and adaptive networks” Complex Systems 2 321 355 1988
- Harpham, C. Dawson, C. W. “The effect of different basis functions on a radial basis function network for time series prediction: A comparative study” Neurocomputing 69 2161 2170
- Luo, F.-L. Unbehauen, R. “Applied Neural Network for Signal Processing” Cambridge University Press 1998
- Arsie, I. Marotta, F. Pianese, C. Rizzo, G. “Information Based Selection of Neural Networks Training Data for S.I. Engine Mapping,” SAE Technical Paper 2001-01-0561 2001 10.4271/2001-01-0561
- Taglialatela-Scafati, F. Cesario, N. Lavorgna, M. Mancaruso, E. et al. “Diagnosis and Control of Advanced Diesel Combustions using Engine Vibration Signal,” SAE Technical Paper 2011-01-1414 2011 10.4271/2011-01-1414