Unsupervised anomaly detection for underwater gliders using generative adversarial networks

An effective anomaly detection system is critical for marine autonomous systems operating in complex and dynamic marine environments to reduce operational costs and achieve concurrent large-scale fleet deployments. However, developing an automated fault detection system remains challenging for several reasons including limited data transmission via satellite services. Currently, most anomaly detection for marine autonomous systems, such as underwater gliders, rely on intensive analysis by pilots. This study proposes an unsupervised anomaly detection system using bidirectional generative adversarial networks guided by assistive hints for marine autonomous systems with time series data collected by multiple sensors. In this study, the anomaly detection system for a fleet of underwater gliders is trained on two healthy deployment datasets and tested on other nine deployment datasets collected by a selection of vehicles operating in a range of locations and environmental conditions. The system is successfully applied to detect anomalies in the nine test deployments, which include several different types of anomalies as well as healthy behaviour. Also, a sensitivity study of the data decimation settings suggests the proposed system is robust for Near Real-Time anomaly detection for underwater gliders.