Datamining turbofan engine performance to improve fuel efficiency

Snecma as major engine manufacturer is often asked by its airline customers to help them improve their efficiency in the operation of the engines. The main concern is fuel consumption but also a long-term expectancy about the engine cost during its full life. For example, this includes maintenance frequency and shop costs. Snecma built a new laboratory for data analysis who's mission is to find numeric models able to take most of the manufacturer knowledge into account when observing large amounts of data coming back from the aircrafts' operations. In the next five years, we need to be able to collect more than one gigabyte of data per flight and per engine. This becomes huge when looking at the big fleet of CFM engines, which already accounts for almost 30000 turbofans, with a new flight beginning every 2.5 seconds. This flow of data continuously increases and should be used to help our customers improve their operations. As manufacturer, we also need to help our maintenance teams improve the reliability of the systems and optimize the shop operations with all information available in the data and even the design of future engines. In this paper, we outline our data laboratory, the way we take care of all sources of information including operations, shops and also production, integration, tests and external observations like weather, airports data, 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 the same as the ones we implemented in our prognostic and health-management (PHM) system. They are used on ground stations to compile models that may eventually be reworked to create new embedded health indicators.