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Machine operators rely on intuition and experience to evaluate vehicle performance. As we increasingly turn to automation, it is important to automatically evaluate sensor data and system performance. Fuzzy logic allows us to take advantage of domain knowledge to evaluate data and to describe a system linguistically. In this paper, two automated fuzzy evaluation systems are described. In the first, a fuzzy quality module evaluates output from a simulated noisy sensor. In the second system, a fuzzy quality module evaluates the output from a machine vision system. Results from both systems indicate that fuzzy logic was able to accurately categorize the output in support of machinery decision making for automated control.

The use of substances other than petroleum based fuels for power sources is not a new concept. Prior to the advent of petroleum fueled vehicles numerous other substances were used to create mobile sources of power. As the world's petroleum supply dwindles, alternative fuel sources are sought after to replace petroleum fuels. Many industries are particularly interested in the development of renewable fuel sources, or biologically derived fuel sources, which includes ethanol. The use of No. 2 diesel as well as many alternative fuels in compression ignition engines result in injector coking. Injector coking can severely limit engine performance by limiting the amount of fuel delivered to the combustion chamber and altering the spray pattern. Injector tip coking is also one of the most sensitive measures of diesel fuel quality [1]. A machine vision system was implemented to quantify injector coking accumulation when a compression ignition engine was fueled with oxydiesel.