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Semantic Scholar · Article (Journal of Aerospace Information Systems)

Analysis of Flight Data Using Clustering Techniques for Detecting Abnormal Operations

Published 2015-09-27 From Journal of Aerospace Information Systems 5 authors
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Attribution

This is the abstract and citation. Full text lives at Semantic Scholar — we link out rather than host. All credit to the authors and Journal of Aerospace Information Systems.

Abstract

Verbatim from Semantic Scholar. Not paraphrased, not summarized.

The new method, enabled by data from the flight data recorder, applies clustering techniques to detect abnormal flights of unique data patterns and can support domain experts in detecting anomalies and associated risks from routine airline operations.

Authors

  • Lishuai Li
  • Santanu Das
  • R. Hansman
  • Rafael Palacios
  • A. Srivastava

Keywords

  • Engineering
  • Computer Science

Citation: Lishuai Li, Santanu Das, R. Hansman , et al. (2015). Analysis of Flight Data Using Clustering Techniques for Detecting Abnormal Operations. Journal of Aerospace Information Systems. Semantic Scholar ID c64e0d2dc8d2be7db9dc76d8537226783879c116. https://doi.org/10.2514/1.I010329 ↗