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In order to meet the stricter and stricter emission regulations, cleaner combustion concepts for Diesel engines are being progressively introduced. These new combustion approaches often requires closed loop control systems with real time information about combustion quality. The most important parameter for the evaluation of combustion quality in internal combustion engines is the in-cylinder pressure, but its direct measurement is very expensive and involves an intrusive approach to the cylinder. Previous researches demonstrated the direct relationship existing between in-cylinder pressure and engine block vibration signal and several authors tried to reconstruct the pressure cycle on the basis of information coming from accelerometers mounted on engine block. This paper proposes a method, based on the analysis of the engine vibration signal, for the diagnosis of combustion process in a Diesel engine.

Increasing demands on emissions reduction and efficiency encouraged a progressive introduction of cleaner combustion concepts. "Advanced" diesel combustions offer a high potential for simultaneous reduction of both NOx and soot within the engine through high inlet charge dilution and mixture homogenization. However, the potential benefits of these combustions in terms of emissions are counterbalanced by their high sensitivity to in-cylinder thermodynamic conditions. This sensitivity makes the engines require closed loop combustion control with real-time information about combustion quality. The parameter widely considered as the most important for the evaluation of the combustion quality in internal combustion engines is the cylinder pressure. However, this kind of measure involves an intrusive approach to the cylinder, expensive sensors and a special mounting process.

The present study proposes the use of a MLP neural network to model the relationship between the engine crankshaft speed and parameters derived from the in-cylinder pressure cycle. This allows to have an indirect measure of cylinder pressure permitting a real time evaluation of combustion quality. The structure of the model and the training procedure is outlined in the paper. The application of the model is demonstrated on a single-cylinder engine with data from a wide range of speed and load. Results confirm that a good estimation of combustion pressure parameters can be obtained by means of a suitable processing of crankshaft speed signal.

Electronic engine controls based on non-intrusive diagnostics can significantly help in complying with the stricter and stricter regulations on pollutants emissions and fuel consumption. The aim of this paper is the use of a low-cost linear capacitive accelerometer placed on the engine block for non-intrusive diagnosis of combustion process in spark ignition engines. In particular, good correspondences between the engine block vibrations and the combustion pressure signal were obtained. The angular position of pressure peak evaluated by accelerometer data can be used in a closed-loop control system for real time control of spark advance.