Reconstructing cylinder pressure from vibration signals based on radial basis function networks

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.