Koch, AndreasAndreasKochKrstova, AlisaAlisaKrstovaHegwein, FlorianFlorianHegweinCastro De Lera, MarioMarioCastro De LeraAles, FilippoFilippoAlesPetry, MichaelMichaelPetryAli, RashidRashidAliMallah, MaenMaenMallahHili, LaurentLaurentHiliGhiglione, MaxMaxGhiglioneWerner, MartinMartinWerner2024-05-272024-05-272023https://publica.fraunhofer.de/handle/publica/46882410.23919/EDHPC59100.2023.103959672-s2.0-85184850193Within the scope of an ESA funded activity, Airbus Defence and Space GmbH completed a research and development study in order to provide a novel dataset to ESA and develop a flight-ready system for on-board anomaly detection. This work includes the extraction of satellite telemetry data, the identification of anomalies, the development of machine learning models and the flight-ready system and finally the deployment of the machine learning algorithms via hardware acceleration. We present the benchmarking results of three accelerated ML algorithms from within the final flight-ready system.enanomaly detectionco-processorFPGAMachine learningon-board AIPUSconference paper