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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Veröffentlicht in:Aerospace Jg. 9; H. 6; S. 329
Hauptverfasser: Qin, Kun, Wang, Qixin, Lu, Binbin, Sun, Huabo, Shu, Ping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2022
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace9060329