Descripción del proyecto
Técnicas de aprendizaje automático para medir el rendimiento de las aeronaves
Las operaciones actuales de las aerolíneas se basan en el sistema de gestión de vuelos (FMS, por sus siglas en inglés) para planificar y gestionar las trayectorias de los vuelos. Sin embargo, el FMS utiliza un único modelo de rendimiento del fabricante para cada tipo de aeronave y se basa en las previsiones meteorológicas previas al vuelo. Este enfoque carece de precisión y no proporciona mediciones exactas del rendimiento de la aeronave. Para resolver este problema, el proyecto PERF-AI, financiado con fondos europeos, tiene como objetivo emplear técnicas de aprendizaje automático en los datos de vuelo. De este modo, puede medir con precisión el rendimiento real de la aeronave durante toda su vida útil. El proyecto identificará algoritmos de aprendizaje automático adecuados, evaluará su precisión para el análisis de datos de vuelo y desarrollará modelos matemáticos para optimizar trayectorias de vuelo reales.
Objetivo
PERF-AI will apply Machine Learning techniques on flight data (parametric & non-parametric approaches) to accurately measure actual aircraft performance throughout its lifecycle.
Within current airline operations, both at flight preparation (on-ground) & at flight management (in-air) levels, the trajectory is first planned, then managed by the Flight Management System (FMS) using a single manufacturer’s performance model that is the same for every aircraft of the same type, & also on weather forecast that is computed long before the flight. It induces a lack of accuracy during the planning phase with a flight route pre-established at specific altitudes & speeds to optimize fuel burn, from take-off to landing using aircraft performances that are not those of the real aircraft. Also, the actual flight will usually shift from the original plan because of Air Traffic Control (ATC) constraints, adverse weather, wind changes & tactical re-routing, without possibility for the flight crew, either using the FMS or through connected services to tactically recompute the trajectory in order to continuously optimize the flight path. This is in particular due to the limitations of the performance databases that the current systems are using.
Hence, PERF-AI is focusing on identifying adequate machine learning algorithms, testing their accuracy & capability to perform flight data statistical analysis & developing mathematical models to optimize real flight trajectories with respect to the actual aircraft performance, thus, minimizing fuel consumption throughout the flight.
The consortium consists of Safety-Line (FR) & INRIA (FR), having full expertise at Aircraft Performance & Data Science, hence, able to fully propose, test & validate different statistical models that will allow to accurately solve some optimization challenges & implement them in an operational environment.
PERF-AI total grant request to the CSJU is 568 550€ with total project duration of 24 months.
Ámbito científico
- natural sciencesearth and related environmental sciencesatmospheric sciencesmeteorology
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- engineering and technologyenvironmental engineeringenergy and fuels
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
Régimen de financiación
CS2-IA - Innovation actionCoordinador
75015 Paris
Francia
Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.