TY - GEN
T1 - Precursor detection of aircraft loss of control in-flight (Loc-i) and prediction of future trajectory
AU - Lee, Hyunseong
AU - Lim, Hyung Jin
AU - Parker, Paul
AU - Chattopadhyay, Aditi
N1 - Funding Information:
The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Program Manager: Dr. Koushik Datta, Project Officer: Dr. Anupa Bajwa). The support is gratefully acknowledged.
Publisher Copyright:
© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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U2 - 10.2514/6.2020-2879
DO - 10.2514/6.2020-2879
M3 - Conference contribution
AN - SCOPUS:85092778249
SN - 9781624105982
T3 - AIAA AVIATION 2020 FORUM
BT - AIAA AVIATION 2020 FORUM
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA AVIATION 2020 FORUM
Y2 - 15 June 2020 through 19 June 2020
ER -