Self-organising maps for comparing flying performance using different inceptors - Nichanian, Arthur; Li, Wen-Chin; Korek, Wojciech Tomasz; Wang, Yifan; Chan, Wesley Tsz-Kin (Springer, 2024)
This paper addresses a new data analysis method which is suitable to cluster flight data and complement current exceedance-based flight data monitoring programmes within an airline. The data used for this study consists of 296 simulated approaches from 4.5 NM to 1 NM to the runway threshold, flown by 74 participants (both pilots and non-pilots) with either a conventional sidestick or a gamepad in the future flight simulator at Cranfield University. It was clustered and analysed with the use of Kohonen’s Self-Organising Maps (SOM) algorithm. The results demonstrate that SOM can be a meaningful indicator for safety analysts to accurately cluster both optimal and less-optimal flying performance. This methodology can therefore complement current deviation-based flight data analyses by highlighting day-to-day as well as exceptionally good performance, bridging the cap of current analyses with safety-II principles.
Keywords: data analysis; human-machine interactions