LSTM recurrent neural network assisted aircraft stall prediction for enhanced situational awareness

TL;DR

This paper presents a deep learning approach using LSTM neural networks to predict aircraft stalls at least 10 seconds before traditional warnings, enhancing safety and pilot preparedness.

Contribution

It introduces a novel LSTM-based method for early stall prediction from in-flight sensor data, outperforming existing warning systems.

Findings

Achieved over 95% prediction accuracy

Predicted stalls approximately 10 seconds before warnings

Utilized 26,400 seconds of simulator flight data

Abstract

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.