The OPS-SAT benchmark for detecting anomalies in satellite telemetry

Detecting anomalous events in satellite telemetry is a critical task in space operations. It is time-consuming, error-prone and human dependent, thus automated data-driven algorithms have been emerging at a steady pace. However, there are no available datasets of real satellite telemetry with annotations to verify anomaly detection models. We address this gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetries acquired on board OPS-SAT-a CubeSat mission, operated by the European Space Agency. The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms. They were evaluated using the training-test dataset split introduced in this work, and we suggest a set of quality metrics which should be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging techniques in an unbiased and fully transparent way.