Data Mining Classification Techniques Applied to Analyze the Impact of Ambient Conditions on Aero Engine Performance - A Case Study Using Xlminer

Efficiency and thrust output of an Aero Engine have a huge bearing and vary drastically based on the ambient conditions at airports. The amount of these variations greatly affects thrust, fuel consumption, surge margins, temperature management across the cross section of the gas turbine fluid flow path. Data mining techniques have been used in the aeronautical industry for many years and have been known to be effective. In order to solve problems such as, this study concentrates on building a hybrid methodology, combining data mining techniques such as association rules and classification trees for creating patterns to aid in better performance management of aero engines. The methodology is applied to real-world parameters data collected from Forecast Systems Laboratory Radiosonde Database and the results are evaluated by comparing them with other techniques. The performance of the aero engine and in turn the airplane is accounted by analyzing various parameters like Pressure, Temperature, Wind direction and Wind Speed at different altitudes of the aircraft as it takes off and lands at the airports. This methodology is expected to help Aeronautical Engineers to make a faster and more accurate prediction of the aero engines and aero planes performance.