Detection of precursors to aviation safety incidents due to human factors

Research output: Contribution to conferencePaperpeer-review

12 Scopus citations

Abstract

In this paper, we study the problem of anomaly detection with application to aviation systems. We proposed a framework for detecting precursors to aviation safety incidents due to human factors based on Hidden Semi-Markov Models (HSMM). We investigate HSMMs due to their inherent ability to model durations in addition to model latent state transitions based on the observed pilots actions. Empirical evaluation on synthetic data and flight simulator data show that HSMMs perform favorably compared to many other existing anomaly detection algorithms.

Original languageEnglish (US)
Pages407-412
Number of pages6
DOIs
StatePublished - Jan 1 2013
Event2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013 - Dallas, TX, United States
Duration: Dec 7 2013Dec 10 2013

Other

Other2013 13th IEEE International Conference on Data Mining Workshops, ICDMW 2013
CountryUnited States
CityDallas, TX
Period12/7/1312/10/13

Keywords

  • Anomaly detection
  • Aviation safety
  • Data mining
  • Hidden markov model

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