This paper discusses the feasibility of using machine learning methods, including deep model architectures, for the prediction of near future hazardous energy states (i.e., stall, overspeed, high and fast, low and slow, unstable approaches). Aircraft state prediction and specifically energy state prediction is an important step in providing the flight crew with visual and aural cues to improve their Aircraft State Awareness (ASA). Lack of ASA has been identified as one of the leading contributing factors in commercial aviation accidents, thus improving ASA has the potential to enhance aviation safety. In previous research, various Predictive Alerting of Energy (PAE) methods for flight crew information management and decision support (IMDS) were developed and tested with data from a NASA flight simulator study (AIME-l) in which eleven commercial airline crews (22 pilots) completed more than 230 flights. The previously tested aircraft state prediction methods included predictor stages of: (i) sequential stochastic filters, (ii) batch estimators and (iii) fast-time 3DOF model simulations. Successful predictions of stall, overspeed and high-fast/low-slow conditions were generated with these methods for time horizons ranging up to 300s. The paper discusses the use of machine learning techniques for energy state prediction, and considers its fundamental safety implications and algorithmic limitations, such as a lack of off-nominal training data, while also examining performance characteristics and providing insight into the underlying structure of the algorithms used.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Feasibility of Machine Learning Methods for Predictive Alerting of the Energy State for Aircraft


    Beteiligte:


    Erscheinungsdatum :

    2018-09-01


    Format / Umfang :

    1313566 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Aircraft Mode and Energy-State Prediction, Assessment, and Alerting

    Shish, Kimberlee / Kaneshige, John / Acosta, Diana et al. | AIAA | 2016



    Trajectory Prediction and Alerting for Aircraft Mode and Energy State Awareness

    Shish, Kimberlee H. / Kaneshige, John / Acosta, Diana M. et al. | AIAA | 2015