  Since the dawn of mankind's introduction to powered flights, there have been
multiple incidents which can be attributed to aircraft stalls. Most modern-day
aircraft are equipped with advanced warning systems to warn the pilots about a
potential stall, so that pilots may adopt the necessary recovery measures. But
these warnings often have a short window before the aircraft actually enters a
stall and require the pilots to act promptly to prevent it. In this paper, we
propose a deep learning based approach to predict an Impending stall, well in
advance, even before the stall-warning is triggered. We leverage the
capabilities of long short-term memory (LSTM) recurrent neural networks (RNN)
and propose a novel approach to predict potential stalls from the sequential
in-flight sensor data. Three different neural network architectures were
explored. The neural network models, trained on 26400 seconds of simulator
flight data are able to predict a potential stall with > 95% accuracy,
approximately 10 seconds in advance of the stall-warning trigger. This can
significantly augment the Pilot's preparedness to handle an unexpected stall
and will add an additional layer of safety to the traditional stall warning
systems.
