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Aircraft atypical approach detection using functional principal component analysis

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  • Jarry, Gabriel
  • Delahaye, Daniel
  • Nicol, Florence
  • Feron, Eric

Abstract

In this paper, a post-operational detection method based on functional principal component analysis and clustering is presented and compared with regard to designed operational criteria. The methodology computes an atypical scoring on a sliding window. It enables not only to detect but also to localize where trajectories deviate statistically from the others. The algorithm is applied to the total energy management, estimated from ground-based data, during approach and landing. The detected atypical flights show non-nominal energy behaviors such as glide interceptions from above or high speed approaches. This promising methodology could help to enhance flight data analysis and safety, highlighting non-monitored behaviors.

Suggested Citation

  • Jarry, Gabriel & Delahaye, Daniel & Nicol, Florence & Feron, Eric, 2020. "Aircraft atypical approach detection using functional principal component analysis," Journal of Air Transport Management, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:jaitra:v:84:y:2020:i:c:s0969699719303266
    DOI: 10.1016/j.jairtraman.2020.101787
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    1. Dauxois, J. & Pousse, A. & Romain, Y., 1982. "Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference," Journal of Multivariate Analysis, Elsevier, vol. 12(1), pages 136-154, March.
    2. Fangrong Yan & Xiao Lin & Ruosha Li & Xuelin Huang, 2018. "Functional principal components analysis on moving time windows of longitudinal data: dynamic prediction of times to event," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(4), pages 961-978, August.
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    Cited by:

    1. Jardines, Aniel & Soler, Manuel & García-Heras, Javier, 2021. "Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning," Journal of Air Transport Management, Elsevier, vol. 95(C).
    2. Zhou, Yu & Kou, Gang & Guo, Zhen-Zhu & Xiao, Hui, 2023. "Availability analysis of shared bikes using abnormal trip data," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Archimbaud, Aurore & Boulfani, Fériel & Gendre, Xavier & Nordhausen, Klaus & Ruiz-Gazen, Anne & Virta, Joni, 2021. "ICS for multivariate functional anomaly detection with applications to predictive maintenance and quality control," TSE Working Papers 21-1182, Toulouse School of Economics (TSE), revised Mar 2022.

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