Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding

• Published in: SAE International journal of aerospace 2022-12, Vol.15 (2), p.219-229, Article 01-15-02-0017
• Main Authors: Bell, Victoria, Moral Arce, Ignacio, Mase, Jimiama M, Rengasamy, Divish, Rothwell, Benjamin, Figueredo, Grazziela P
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Anomalies; Anomaly detection; Autoencoder; Aviation; Commercial aircraft; Data analysis; Deep learning; Drone aircraft; Dynamic thresholding; Fault detection; Information management; LSTM; Machine learning; Methods; Military aspects; Military operations; Outliers (statistics); Performance measurement; Sensors; Technology application; Unmanned aerial vehicle; Unmanned aerial vehicles
• ISSN: 1946-3855; 1946-3901; 1946-3901
• DOI: 10.4271/01-15-02-0017

With substantial recent developments in aviation technologies, unmanned aerial vehicles (UAVs) are becoming increasingly integrated in commercial and military operations internationally. Research on the applications of aircraft data is essential in improving safety, reducing operational costs, and developing the next frontier of aerial technology. Having an outlier detection system that can accurately identify anomalous behavior in aircraft is crucial for these reasons. This article proposes a system incorporating a long short-term memory (LSTM) deep learning autoencoder-based method with a novel dynamic thresholding algorithm and weighted loss function for anomaly detection of a UAV dataset, in order to contribute to the ongoing efforts that leverage innovations in machine learning and data analysis within the aviation industry. The dynamic thresholding and weighted loss functions showed promising improvements to the standard static thresholding method, both in accuracy-related performance metrics and in speed of true fault detection.