Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism

Abstract

With the rapid development of the aviation industry, it is particularly important to ensure the safe flight of aircraft. How to find potential hazards in the process of aircraft flight has always been one of the important topics of civil aviation research. At present, the Quick Access Recorder (QAR) is the most widely used equipment to store the data recorded on aircraft. QAR data contain a lot of valuable and unexplored information, which records the true status of the aircraft in detail. Therefore, finding abnormal data from QAR data lays an important foundation for obtaining the cause of abnormality and providing a guarantee for flight. In this paper, in order to discover the abnormal information in the QAR data, we applied a VAE-LSTM model with a multihead self-attention mechanism. Compared to the VAE and LSTM models alone, our model performs much better in anomaly detection and prediction, detecting all types of anomalies. We conducted extensive experiments on real-world QAR data sets to prove the efficiency and accuracy of our proposed neural network model. The experimental results proved that our proposed model can outperform state-of-the-art models under different experimental settings.

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

“…After that, affiliated scholars made continuous improvements based on LSTM. For example, Rong et al [25] applied a LSTM model for variational autoencoder model with a multi-headed selfattentive mechanism to detect abnormal patterns. Liu et al [26] proposed several deep learning models to perform health monitoring of aircraft systems, among which the LSTM-AE obtained promising results.…”