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Proceedings Paper

Applying data mining techniques to detect abnormal flight characteristics
Author(s): H. Emre Aslaner; Cagri Unal; Cem Iyigun
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Paper Abstract

This paper targets to highlight flight safety issues by applying data mining techniques to recorded flight data and proactively detecting abnormalities in certain flight phases. For this purpose, a result oriented method is offered which facilitates the process of post flight data analysis. In the first part of the study, a common time period of flight is defined and critical flight parameters are selected to be analyzed. Then the similarities of the flight parameters in time series basis are calculated for each flight by using Dynamic Time Warping (DTW) method. In the second part, hierarchical clustering technique is applied to the aggregate data matrix which is comprised of all the flights to be studied in terms of similarities among chosen parameters. Consequently, proximity levels among flight phases are determined. In the final part, an algorithm is constructed to distinguish outliers from clusters and classify them as suspicious flights.

Paper Details

Date Published: 12 May 2016
PDF: 18 pages
Proc. SPIE 9850, Machine Intelligence and Bio-inspired Computation: Theory and Applications X, 985009 (12 May 2016); doi: 10.1117/12.2224061
Show Author Affiliations
H. Emre Aslaner, Middle East Technical Univ. (Turkey)
Cagri Unal, Turkish Aerospace Industries, Inc. (Turkey)
Cem Iyigun, Middle East Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 9850:
Machine Intelligence and Bio-inspired Computation: Theory and Applications X
Misty Blowers; Jonathan Williams; Russell D. Hall, Editor(s)

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