Precursor detection of aircraft loss of control in-flight (Loc-i) and prediction of future trajectory

Hyunseong Lee, Hyung Jin Lim, Paul Parker, Aditi Chattopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Aircraft loss of control in-flight (LOC-I) is a primary contributor to fatal accidents worldwide. As air traffic increases, current aviation control systems may be unable to adequately manage severe LOC-I related issues. In this paper, a data-driven system health monitoring (SHM) technique using an autoencoder (AE) is proposed to detect aircraft LOC-I precursors in real-time and to provide aircraft system level information to air traffic controllers (ATCs) for proactive aviation safety management. An air traffic simulator is utilized to investigate aircraft flight operations and trajectories based on flight phases and flight plans. To estimate nominal flight operations, an AE model is adopted. A statistical detection baseline is defined using multivariate Gaussian distribution to detect LOC-I precursors, which are statistically uncommon operation patterns. The proposed technique is validated using a case of LOC-I scenario. The novelty of this study lies in development of a real-time, data-driven LOC-I precursor detection technique, and an interface that can connect aircraft system health information with ATCs.

Original languageEnglish (US)
Title of host publicationAIAA AVIATION 2020 FORUM
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105982
StatePublished - 2020
EventAIAA AVIATION 2020 FORUM - Virtual, Online
Duration: Jun 15 2020Jun 19 2020

Publication series

Volume1 PartF


CityVirtual, Online

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Aerospace Engineering
  • Energy Engineering and Power Technology


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