Causal analysis of flight exceedance events, e.g. hard-landing, is a key task for modern
airlines performing Flight Operation Quality Assurance (FOQA) programs. The main
objective of the program is to learn from experience: detect early signs of major
problems and correct them before accidents occur. It has been found that flare
operation would greatly influence the landing performance. According to the finding,
we proposed a deep learning approach to assist airlines performing causal analysis for
hard landing events. Experimental results confirm that compared with the other stateof-the-art techniques, the proposed approach provides a more reliable results. The
technique can be the basis of developing advanced models for further revealing the
relationships between pilot operations and flight exceedance events.
關聯:
Journal of Information Science and Engineering 37(6), p.1405-1418
