Title: Stall prediction and identification for civil aircraft based on a long short-term memory model

Authors: Long Xi

Addresses: Chinese Flight Test Establishment, Xi'an, 710089, China

Abstract: Stall is a major contributor to aircraft safety incidents. Most civil aircraft lack stall prediction, and existing warnings afford little reaction time, limiting pilots' ability to avert accidents. This paper proposes a deep learning-based stall identification approach. We first analyse flight-performance parameters for a representative civil aircraft and define identification methods of incipient stall. We then conduct cruise-phase stall simulations across 28 operating points for the same type and build a long short-term memory (LSTM) model to forecast the resulting time-series signals. Finally, we train and evaluate the model under varied train/test strategies and compare alternatives. Results show the proposed LSTM reliably anticipates stall and supports timely recovery guidance. The framework demonstrates that data-driven sequence modelling can extend crew reaction time and improve safety, offering a practical path toward onboard stall prediction systems for civil aviation. Performance remains robust under realistic noise and sensor latencies.

Keywords: aircraft stall; LSTM; long short-term memory; state prediction; flight safety.

DOI: 10.1504/IJVSMT.2026.153214

International Journal of Vehicle Systems Modelling and Testing, 2026 Vol.20 No.2, pp.224 - 243

Received: 21 Sep 2025
Accepted: 10 Nov 2025
Published online: 29 Apr 2026 *

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