Towards Adaptive and Robust Unsupervised Anomaly Detection in Satellite Telemetry

The role of satellites and space systems in space exploration is of paramount importance, particularly given the growing interest in this field. The considerable financial outlay and commitment of resources that are necessary for space missions, coupled with the inherent difficulty of directly intervening in the systems in question in the event of faults or anomalies, give rise to the imperative of designing and developing highly reliable platforms. However, the harsh environment in which these systems operate means that unexpected and undesired events may occur, with the potential to have a catastrophic impact on mission objectives. To address this issue, it is of paramount importance to use telemetry data for the prompt detection of anomalies. This facilitates the implementation of corrective or mitigating actions, although this is challenging due to the remoteness of space platforms and the limited range of intervention options. A proactive approach can extend the duration of the mission, thereby preventing the loss of data and functions that are scientifically or commercially valuable. However, the intricate and multifaceted nature of telemetry data, which encompasses both sensor readings and commands, poses considerable analytical challenges, reinforcing the ongoing necessity of experts to determine system integrity. Most existing anomaly detection algorithms depend excessively on extensive hyperparameter tuning and fixed thresholds, which renders them less robust when applied to different signals or evolving scenarios. Furthermore, numerous algorithms necessitate voluminous amounts of labeled data and protracted training phases, which results in increased deployment latency and operational costs, thereby limiting their utility throughout the duration of a mission. To address these limitations, a novel framework for intelligent time-series anomaly detection (TSAD) is proposed. The solution has been designed to be plug-and-play, thereby minimizing the necessity of hyperparameter tuning and ensuring robust performance across diverse scenarios without the need for optimization. The system incorporates an adaptive thresholding mechanism that automatically adapts to evolving time-series behavior, ultimately enhancing detection accuracy. Furthermore, it requires only a statistically significant volume of unlabeled data, drastically reducing deployment latency and enabling rapid operational integration.