A method for handling aircraft data, includes the steps of receiving an input aircraft model and/or operational data; comparing the model and/or data with reference data previously obtained by machine learning performed on large collections of data of recorded flights, wherein the reference data defines safe boundaries for one or more aircraft models and/or operations data. Developments describe “big data” aspects, comprising aircraft or flight data of a plurality of airlines about a plurality of aircraft, engines, flight contexts, aircraft configurations and meteorological data; the handling of calculations about performances, weight and balance, fuel, crew duty times; etc., the use of machine deep learning (unsupervised pre-training); comparisons against a superset of data generated out from the collected large collections of data, with or without human intervention; automated validation tests of aircraft models and associated data. Hardware and software aspects are described.
MACHINE LEARNING ON BIG DATA IN AVIONICS
2020-04-16
Patent
Electronic Resource
English