Time-feature attention-based convolutional auto-encoder for flight feature extraction

• Published in: Scientific reports Vol. 13; no. 1; p. 14175
• Main Authors: Wang, Qixin, Qin, Kun, Lu, Binbin, Sun, Huabo, Shu, Ping
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 30.08.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Aircraft accidents & safety; Airports; Case studies; Civil aviation; Discriminant analysis; Fatalities; Flight; Kinematics; Landing behavior; Principal components analysis; Quality assurance; Time series
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-41295-y

Quick Access Recorders (QARs) provide an important data source for Flight Operation Quality Assurance (FOQA) and flight safety. It is generally characterized by large volume, high-dimensionality and high frequency, and these features result in extreme complexities and uncertainties in its usage and comprehension. In this study, we proposed a Time-Feature Attention (TFA)-based Convolutional Auto-Encoder (TFA-CAE) network model to extract essential flight features from QAR data. As a case study, we used the QAR data landing at the Kunming Changshui International Airport and Lhasa Gonggar International Airport as the experimental data. The results show that (1) the TFA-CAE model performs the best in extracting representative flight features in comparison to some traditional or similar approaches, such as Principal Component Analysis (PCA), Convolutional Auto-Encoder (CAE), Self-Attention-based CAE (SA-CAE), Gate Recurrent Unit based Auto-Encoder (GRU-AE) and TFA-GRU-AE models; (2) flight patterns corresponding to different runways can be recognized; and (3) anomalous flights can effectively deviate from many observations. Overall, the TFA-CAE model provides a well-established technique for further usage of QAR data, such as flight risk detection or FOQA.