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Two different types of fuel sources, namely conventional diesel and GTL diesel, were used to conduct a study of their effects on engine emissions and performance. Varying loads were applied to obtain concise data when contrasting the aforementioned fuels. Key parameters such as net power output, torque, engine speed and efficiency were measured. The engine and the dynamometer were operated via an automated closed-loop control system. On-road study found that the volumetric fuel consumption of GTL diesel was higher by up to 3.3% when compared to conventional diesel; there were drastic reductions in the levels of regulated emissions when using GTL by 36% for CO, 4.2% for CO2, 47% for THC (total hydrocarbons) and 35% for NOx, compared to conventional diesel.

In this paper, wavelets as signal processing tools are used to analyze the acceleration data acquired at the cylinder head for the detection and characterization of combustion malfunctions in multi-cylinder industrial engines. The objectives were to collect data on 1) normal operations, and 2) operations with a deactivated cylinder to simulate a faulty condition. Wavelet packet and local discriminatory basis algorithm are used to select wavelets that can recognize different conditions. It is shown that the wavelet packet provides a useful data analysis structure for extracting features that are capable of detecting the combustion malfunction of one cylinder in a 12-cylinder engine. Feature extraction is followed by a classification that uses a neural network for the fault identification phase.