Wang Qixin, Qin Kun, Lu Binbin, Sun Huabo, Shu Ping
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
Engineering and Technical Research Center of Civil Aviation Safety Analysis and Prevention, China Academy of Civil Aviation Science and Technology, Beijing, 100028, China.
Sci Rep. 2023 Aug 30;13(1):14175. 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.
快速访问记录器（QAR）为飞行运行质量保证（FOQA）和飞行安全提供了重要的数据源。它通常具有数据量巨大、维度高和频率高的特点，这些特点导致其使用和理解极具复杂性和不确定性。在本研究中，我们提出了一种基于时间特征注意力（TFA）的卷积自动编码器（TFA-CAE）网络模型，用于从QAR数据中提取关键飞行特征。作为案例研究，我们将在昆明长水国际机场和拉萨贡嘎国际机场降落的QAR数据用作实验数据。结果表明：（1）与一些传统或类似方法（如主成分分析（PCA）、卷积自动编码器（CAE）、基于自注意力的CAE（SA-CAE）、基于门控循环单元的自动编码器（GRU-AE）和TFA-GRU-AE模型）相比，TFA-CAE模型在提取代表性飞行特征方面表现最佳；（2）可以识别对应于不同跑道的飞行模式；（3）异常航班能够有效偏离许多观测值。总体而言，TFA-CAE模型为QAR数据的进一步应用（如飞行风险检测或FOQA）提供了一种成熟的技术。
