An interpretable precursor-driven hierarchical model for predictive aircraft safety

Predicting high-risk anomalous events in flight is crucial for ensuring in-time aviation safety and reducing potential incidents. This paper proposes a precursor-driven hierarchical predictive model for early warnings and actionable insights before incidents occur. The model uses an unsupervised learning network to construct latent event sequences from discrete variables, guiding a weakly supervised learning network for feature extraction from continuous variables. This hierarchical fusion captures the influence of discrete control variables on continuous flight states, enhancing its prediction performance of anomalous events. Guided by event sequences, the model can detect different anomalous patterns through identified precursors, thus providing a comprehensive understanding of events with interpretation. Quantitative evaluations further support the model's rationale in interpretation, encompassing self-explanation and post-hoc analysis. A real case study on unstable approach events, using data from enhanced flight recorders, validates the model's effectiveness in prediction and interpretation from precursors. The study explains imminent unstable approaches and offers an in-depth analysis of error cases, providing insights for model refinement and risk analysis, contributing to ongoing improvement in aviation safety.