Abstract
In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.
Funder
National Natural Science Foundation of China
Key Laboratory of National Geographic Census and Monitoring, Ministry of Nature Resources
Reference59 articles.
1. Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder
2. Aerodynamic modeling and parameter estimation from QAR data of an airplane approaching a high-altitude airport;Qing;Chin. J. Aeronaut.,2012
3. Outlier detection: A survey;Chandola;ACM Comput. Surv.,2007
4. Identification of Outliers;Hawkins,1980
5. Multiple kernel learning, conic duality, and the SMO algorithm;Bach;Proceedings of the Twenty-First International Conference on Machine Learning,2004
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献