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The engine manufacturers have to meet more and more stringent regulations on both noise and exhaust emissions in the future requiring extensive optimisation of all parameters of the engine. Soft modelling with statistically designed experiment (SDE) and Multivariate Analysis (MVA) can be an aid in this work. The analyses are based on cylinder pressure data, engine data and exhaust emissions data, with all measurements performed simultaneously. The effects of changes in fuel quality, load, speed, and injection timing on combustion noise and exhaust emissions were investigated using SDE and MVA. The engine was a modified 6 cylinder, direct injection diesel engine running on ethanol. The paper demonstrates the applicability of experimental design and multivariate methods for modelling and minimisation of noise and exhaust emissions.

Wavelet transform analysis is capable of revealing aspects of data that other techniques miss such aspects are trends, and discontinuities in higher derivates. This method can often compress or de-noise a signal without any appreciable degradation and it provides accurate information on the localization of energy content in time and frequency [1]. The Wavelet transform method has been applied to the analysis of combustion noise and piston slap in order to identify the effects on the engine structure vibration. The experimental results showed that combustion noise and piston slap were successfully detected from the acceleration signals picked up by external transducers in the outer wall of an in-line, 6 cylinder diesel engine.