Abstract

Aviation accidents during the final approach phase of transport aircraft account for nearly half of all accidents in the flight stage, with unstable approaches being a notable contributing factor. In related accident analysis research, traditional single-parameter threshold monitoring methods have shown difficulty in capturing the complex coupling relationship between kinetic energy and potential energy. This weakness results in insufficient adaptability under variable meteorological disturbances and poor risk identification. To address this limitation, this study establishes an evaluation framework based on the concepts of “energy altitude” and “balance energy, shifting the analytical focus of aircraft state variations to energy evolution. A hybrid dynamic safety boundary function is further constructed by integrating flight mechanics principles with civil aviation regulatory constraints. This boundary integrates a height attenuation mechanism, enhancing adaptability to environmental disturbances. The study adopts QAR flight data of Boeing aircraft collected at an international airport from 2015 to 2020 as the database for machine learning modeling, and selects two additional independent flight datasets under calm-air and wind-shear conditions respectively for model verification. The research results indicate that this framework provides a robust theoretical foundation for the early identification of unstable approaches and provides actionable insights for optimizing energy control strategies, thus improving flight safety under complex operational conditions. Nevertheless, the verification only relies on two groups of typical flight cases under limited meteorological conditions, which restricts the generalizability of the research conclusions. Follow-up work will expand multi-type and multi-meteorological flight samples to carry out quantitative performance evaluation and further optimize the model’s practicality under diverse operational environments.

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