This work describes a detector for the loss of control condition of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. First, we demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is approaching a flight state change by measuring a shift in the sampling distributions of the CVAE latent space. The sampling distributions for flight observations that are approaching envelope limits are localized to external areas of the latent space. We provide an analysis of its applicability to flight data from NASA’s dynamically-scaled generic transport model (GTM) aircraft.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Loss of Control Detection for Commercial Transport Aircraft Using Conditional Variational Autoencoders


    Contributors:
    N. H. Campbell (author) / J. Grauer (author) / I. Gregory (author)

    Publication date :

    2020


    Size :

    30 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




    Loss of Control Detection for Commercial Transport Aircraft Using Conditional Variational Autoencoders

    Campbell, Newton H. / Grauer, Jared A. / Gregory, Irene M. | AIAA | 2021


    LOSS OF CONTROL DETECTION FOR COMMERCIAL TRANSPORT AIRCRAFT USING CONDITIONAL VARIATIONAL AUTOENCODERS

    Campbell, Newton H. / Grauer, Jared A. / Gregory, Irene M. | TIBKAT | 2021




    Route choice set generation using variational autoencoders

    Yao, Rui / Bekhor, Shlomo | TIBKAT | 2022