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Abstract

Aircraft maneuver partition, which dividing flight data into meaningful maneuvers, is an essential preprocess method for health monitoring, flight simulation and flying quality evaluating. Maneuver partition usually needs flight testing and manual interpretation, which is time-consuming, higher cost, and lower versatility. In this paper, a non-supervised automatic method of aircraft maneuver partition (NSAM) is proposed by using data mining without any priori knowledge: Select 6 parameters, height, speed, angle of pitch, angle of bank, angle of yaw, and normal overload; Extract action parts according to the trends of the normal overload, the main parameters; Use the iterative self-organized data analysis algorithm (ISODATA) and divide action parts by numeric features of parameters into classes that represent maneuvers. Applying the NSAM into the small-scale and large-scale data respectively has the results that at least 89% of the maneuvers can be recognized and classified correctly. It indicates that the NSAM is effective and meets the requirements of engineering accuracy.

Keywords

Maneuver partition data mining non-supervised learning trend recognition ISODATA clustering

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A non-supervised automatic method of aircraft maneuver partition

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