E-Pilots: A System to Predict Hard Landing during the Approach Phase of Commercial Flights

Debora Gil*, Aura Hernàndez-Sabaté, Julien Enconniere, Saryani Asmayawati, Pau Folch, Juan Borrego-Carazo, Miquel Angel Piera

*Autor correspondiente de este trabajo

Producción científica: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva

6 Citas (Scopus)

Resumen

More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event. This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.

Idioma originalInglés
Páginas (desde-hasta)7489-7503
Número de páginas15
PublicaciónIEEE Access
Volumen10
DOI
EstadoPublicada - 2022

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