Automatic indexation of turbofan data to identify anomalous behaviors

Abstract

How can we tell if a flight is normal or abnormal? In Safran Aircraft Engines, we are interested in the engine behavior. Some data are collected at low frequency between 1Hz up to 66Hz. These data are mainly measurements acquired from engines sensors, information coming from the aircraft that are needed to control the propulsion system and results of online computations for monitoring and maintenance. Hence, a flight appears as a big multivariate temporal signal. But it is not just a simple temporal observation, this signal is structured and may be decomposed in standard phases like start, taxi, take-off, climb, cruise, descent, reverse and taxi again. Moreover, during each standard phase there may be stabilized regimes and transient phases, the stabilized parts are easy to understand and to model mathematically. There are mainly four stabilized regimes: Slow ground speed, normal cruise speed, slow descent and climb. The transient regimes are more complex as they depend a lot on the command issued by the pilot, but we identify two classes of transient phases: Accelerations and decelerations. Depending on the flight plan, the airport ground geography, the day time, season and meteorology those phases may appears randomly at different instants during the journey. All of this complexity makes the comparison of different flights very difficult. Our goal in this work is to give a definition of an abnormal fight based on a new kind of metric that we build to compare those multivariate temporal series two by two.