Multiclass Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model

The identification of precursors to safety incidents in aviation data is a crucial task, yet extremely challenging. The main approach in practice leverages domain expertise to define expected tolerances in system behavior and flags exceedances from such safety margins. However, this approach is incapable of identifying unknown risks and vulnerabilities. Various machine-learning approaches have been investigated and deployed to identify anomalies, with the great challenge of procuring enough labeled data to achieve reliable and accurate performance. This paper presents an explainable deep semi-supervised model for anomaly detection in aviation, building upon recent advancements described in the machine-learning literature. The proposed model combines feature engineering and classification in feature space, while leveraging all available data (labeled and unlabeled). Our approach is validated with case studies of anomaly detection during the takeoff and landing phases of commercial aircraft. Our model outperforms the state-of-the-art supervised anomaly-detection model, reaching significantly higher accuracy and fewer false alarms, even if only small proportion of data in the training set is labeled.