New Concepts Of Automated Anomaly Detection In Space Operations Through ML-Based Techniques

Carlo Ciancarelli*, Francesco Corallo, Salvatore Cognetta, Eleonora Mariotti, Livia Manovi, Mauro Mangia, Alex Marchioni, Riccardo Rovatti, Fabio Pareschi, Gianluca Setti

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review


Spacecraft health management is a key component to ensure the safety and mission operation life of a satellite complex system. The health monitoring task is pursued exploiting telemetry data, collected using various sensor reading fromonboard devices, that can be analyzed to retrieve and early detect anomalies which can lead to critical failures. The traditional monitoring methods, based on simple threshold checks, are now facing with lots of difficulties the increased complexity of the spacecraft, requiring updated and intelligent systems based on data-driven approaches. In this paper we propose different ML-based methods that contribute to the generation of an intelligent anomaly detector, that can face up the numerous telemetry data. Finally we focus on how to optimize and implement t he developed models on constrained hardware, representative of spacecraft processors.

Original languageEnglish (US)
StatePublished - 2022
Event73rd International Astronautical Congress, IAC 2022 - Paris, France
Duration: Sep 18 2022Sep 22 2022


Conference73rd International Astronautical Congress, IAC 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.


  • Health Monitoring
  • Machine Learning
  • Spacecraft

ASJC Scopus subject areas

  • Aerospace Engineering
  • Astronomy and Astrophysics
  • Space and Planetary Science


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