DUVET: Dual-View Enhanced Transformer for Anomaly Detection in Aviation Safety Monitoring

In recent years, the prevalence of modern flight data sensing systems, represented by Quick Access Recorder (QAR), has made it viable to improve flight safety by analyzing massive flight parameters. However, existing studies predominantly concentrate on predefined safety events where the expert-annotated data are highly scarce and imbalanced. In this paper, we address this issue by uncovering abnormal flight patterns from tremendous unlabeled data to discover potential and unexpected safety risks. To this end, we propose DUVET, a reconstruction-based DUal View Enhanced Transformer model for flight data anomaly detection, which plays a vital role in aviation safety monitoring. Specifically, DUVET adopts patching and channel independence techniques to reduce model parameters and improve Transformer’s computational efficiency while preserving its semantic representation capacity. Based on the observation that flight safety risks usually relate to both long-term trend and short-term patterns, we creatively propose Flight Trend-Aware Attention and Bell attention mechanisms to capture both global and local dependencies, enhancing the model reconstruction ability. We evaluate DUVET on six public real-world datasets and one QAR dataset, demonstrating that DUVET achieves state-of-the-art performance in general time series anomaly detection task. Further experiments on the QAR dataset show that DUVET meets key criteria of anomaly detection for flight safety in terms of stability, training efficiency and detection accuracy. Case study shows that DUVET can effectively uncover hidden flight safety risks.