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

Abstract

Verbatim from NASA NTRS. Not paraphrased, not summarized.

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.

Authors

• Newton H Campbell Science Applications International Corporation (United States)
• Jared Grauer Langley Research Center
• Irene Gregory Langley Research Center

Keywords

• Loss of control
• Conditional variational autoencoders
• Generic transport model