Satellites play a crucial role in the global communications system, while being subjected to the harsh space environment that often causes component degradation. Early failure detection is important to ensure that the critical
services they provide are not interrupted. This work proposes a forecasting-based anomaly detection method, where a Graph Convolutional Network (GCN) is leveraged to extract relevant information from time series. The proposed anomaly detection model reaches an F-Score of 89% on the open-source SMAP & MSL dataset, which is the best score achieved on this dataset to the best of the authors’ knowledge. Furthermore, the proposed model is deployed on the space-grade AMD-Xilinx Versal AI Core series and its performance measures as well as occupied hardware resources are demonstrated.