When there is damage to an aircraft, it is critical to be able to quickly detect and diagnose the problem so that the pilot can attempt to maintain control of the aircraft and land it safely. We develop methodology for real-time classification of flight trajectories to be able to distinguish between an undamaged aircraft and five different damage scenarios. Principal components analysis allows a lower-dimensional representation of multi-dimensional trajectory information in time. Random Forests provide a computationally efficient approach with sufficient accuracy to be able to detect and classify the different scenarios in real-time. We demonstrate our approach by classifying realizations of a 45 degree bank angle generated from the Generic Transport Model flight simulator in collaboration with NASA.
Journal of Classification – Springer Journals
Published: Oct 2, 2017
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