Data mining turbofan engine performance to improve fuel efficiency

The main concern for airlines is fuel consumption but also a long-term expectancy about the engine cost during its full life. This includes maintenance frequency and shop costs. In the next five years, Snecma, as engine manufacturer, needs to be able to collect more than one gigabyte of data per flight and per engine. This becomes huge as the flow of data continuously increases and should be used to help our customers improve their operations. We also need to help our maintenance teams improve the reliability of the systems; optimize the shop operations with all information available in the data, and even the design of future engines. In this paper, we outline the way we take care of information sources including operations, shops, etc. As an example, we present the statistics we get from the analysis of the aircraft fuel consumption during climb. To be able to interpret the flight data we need to get rid of all external conditions that may bias the data. The algorithms we use for this process are almost the same ones we implemented in our prognostic and health-management system.