Real-time aviation anomaly detection and multi-label classification using deep learning on multivariate sensor data

Abstract

General aviation records a fatal accident rate of approximately one per 100,000 flight hours, with loss-of-control and stall events remaining leading preventable causes. Existing flight safety systems rely on fixed expert-defined thresholds and cannot detect complex multi-sensor anomaly patterns or identify specific event types in real time. This thesis proposes a lightweight two-stage deep learning framework for real-time aviation anomaly detection and multi-label event classification on raw flight sensor data from the NGAFID General Aviation Training Set. Stage 1 uses an ensemble of two novel Transformer architectures, the Cross-Sensor Patch Transformer (CSPT) and the Hierarchical Cross-Sensor Transformer (HiCST), each incorporating a cross-sensor multi-head attention module that explicitly models inter-sensor dependencies before temporal processing. Root Mean Square ensemble fusion achieves an anomaly-class F1-score of 0.8815, recall of 0.9076, and AUPRC of 0.9523 on a test set with a 339:1 class imbalance ratio. Stage 2 uses MHANet, which introduces per-sensor independent linear projections to classify each anomalous timestep into any combination of ten simultaneous event types, achieving a macro F1 of 0.9563 and subset accuracy of 0.9627. The complete pipeline runs in 6.08 milliseconds per sensor reading on a standard CPU, confirming real-time feasibility. Both stages outperform all established deep learning and classical machine learning baselines while using significantly fewer parameters, demonstrating that domain-aware architectural specialization consistently outperforms general-purpose approaches for aviation safety monitoring.