Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring

L Li, RJ Hansman, R Palacios, R Welsch - Transportation Research Part C …, 2016 - Elsevier
Safety is key to civil aviation. To further improve its already respectable safety records, the
airline industry is transitioning towards a proactive approach which anticipates and mitigates …

Challenges and opportunities in flight data mining: A review of the state of the art

A Gavrilovski, H Jimenez, DN Mavris, AH Rao… - AIAA Infotech …, 2016 - arc.aiaa.org
Incident and accident rates of rotorcraft and fixed-wing general aviation operations are
considerably higher than those of commercial aviation. Efforts to improve the safety record of …

Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft

H Lee, G Li, A Rai, A Chattopadhyay - Advanced Engineering Informatics, 2020 - Elsevier
The development of an automated health monitoring framework is critical for aviation system
safety, especially considering the expected increase in air traffic over the next decade …

[HTML][HTML] Toward automated instructor pilots in legacy air force systems: Physiology-based flight difficulty classification via machine learning

WN Caballero, N Gaw, PR Jenkins… - Expert Systems with …, 2023 - Elsevier
Abstract The United States Air Force (USAF) is struggling to train enough pilots to meet
operational requirements. Technology has advanced rapidly over the last 70 years but …

A data analytics framework for anomaly detection in flight operations

LC e Silva, MCR Murça - Journal of Air Transport Management, 2023 - Elsevier
In the air transport system, there has been a continuous effort to develop policies, tools, and
methodologies that increase and standardize safety levels across the entire commercial …

[图书][B] Competency-based education in aviation: Exploring alternate training pathways

SK Kearns, TJ Mavin, S Hodge - 2017 - taylorfrancis.com
Whether a trainee is studying air traffic control, piloting, maintenance engineering, or cabin
crew, they must complete a set number of training'hours' before being licensed or certified …

[PDF][PDF] Flight data monitoring (FDM) unknown hazards detection during approach phase using clustering techniques and AutoEncoders

A Fernández, D Martınez, P Hernández… - Proceedings of the …, 2019 - researchgate.net
Airlines safety departments analyse aircraft data recorded on-board (FDM) to inspect safety
occurrences. This activity relies on human experts to create a rule-based system that detects …

Detecting high-risk anomalies in aircraft dynamics through entropic analysis of time series data

E Juarez Garcia, C Stephens, NJ Napoli - AIAA AVIATION 2022 Forum, 2022 - arc.aiaa.org
View Video Presentation: https://doi. org/10.2514/6.2022-3257. vid Despite recent efforts to
move away from traditional threshold exceedance detection methods for aircraft state …

[PDF][PDF] Forecasting unstable approaches with boosting frameworks and lstm networks

D Martınez, A Fernández, P Hernández… - 9th SESAR Innovation …, 2019 - sesarju.eu
This paper presents a machine learning algorithm trained to predict unstable approach
events. Predictive modeling for unstable approaches (UA) forecasting needs a precursors …

Unsupervised flight phase recognition with flight data clustering based on GMM

D Liu, N Xiao, Y Zhang, X Peng - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Currently, with the rapid development of the aviation industry, researchers are paying more
attention to the improvement of aviation safety. Aviation safety mainly includes flight safety …