Flight Anomaly Detection via a Deep Hybrid Model

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...

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Published in:	Aerospace Vol. 9; no. 6; p. 329
Main Authors:	Qin, Kun, Wang, Qixin, Lu, Binbin, Sun, Huabo, Shu, Ping
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.06.2022
Subjects:	Accident investigation Accident investigations Accidents Aerospace industry Aircraft accidents & safety Aircraft landing Airport security Airports Algorithms Anomalies Aviation Civil aviation Clustering convolutional autoencoder deep hybrid model Deep learning Detectors Feature extraction flight anomaly detection Forensic engineering HDBSCAN clustering algorithm Machine learning Neural networks Object recognition Random variables Risk management Safety and security measures Time series time-feature attention China
ISSN:	2226-4310, 2226-4310
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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.
AbstractList	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.
Audience	Academic
Author	Sun, Huabo Qin, Kun Lu, Binbin Shu, Ping Wang, Qixin
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SubjectTerms	Accident investigation Accident investigations Accidents Aerospace industry Aircraft accidents & safety Aircraft landing Airport security Airports Algorithms Anomalies Aviation Civil aviation Clustering convolutional autoencoder deep hybrid model Deep learning Detectors Feature extraction flight anomaly detection Forensic engineering HDBSCAN clustering algorithm Machine learning Neural networks Object recognition Random variables Risk management Safety and security measures Time series time-feature attention
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Title	Flight Anomaly Detection via a Deep Hybrid Model
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