Abstract:

In order to assess and monitor flight risks in real-time, clustering analysis was utilized to explore the abnormal patterns embedded in QAR data, and the influencing factors of abnormal flight patterns of civil aircraft were analyzed. The Euclidean distance was employed to characterize the similarity between samples of QAR parameters, establishing an abnormal flight pattern recognition model based on K-means to define the deviation degree of abnormal patterns. By considering the number of fatal accidents and the proportion of deaths in global commercial jet accidents, in conjunction with the deviation degree of abnormal patterns, the duration of abnormal patterns, flight phases, the likelihood of unexpected safety events, and the severity of consequences following unexpected safety events, a quantified assessment method for civil aviation flight risks based on QAR data was proposed. The feasibility of abnormal flight pattern recognition and risk quantification models for civil aircraft was validated through the practical QAR data of a certain airline. The results indicate that abnormal patterns are more prevalent during the cruising phase and critical moments at the transitions between flight phases. Significant differences are observed in the distribution of abnormal flight patterns and risks across different flights and flight phases. The average total risk value for flights is 166.94, with outliers exceeding 386.97. The abnormal flight risk during the takeoff roll phase is relatively low, with an average of 5.95, while the risk during the cruising phase is relatively high, with an average of 93.46.

Key words: quick access recorder(QAR), abnormal flight of civil aircraft, pattern recognition, risk assessment, cluster analysis