On applying AI-driven flight data analysis for operational spacecraft model-based diagnostics

• Published in: Annual reviews in control Vol. 49; pp. 197 - 211
• Main Authors: Tipaldi, Massimo, Feruglio, Lorenzo, Denis, Pierre, D’Angelo, Gianni
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2020
• Subjects: Artificial intelligence; Discipline models; Model-based diagnostics; Spacecraft flight data; Spacecraft operations; Discipline models; Spacecraft flight data; Artificial intelligence; Spacecraft operations; Model-based diagnostics
• ISSN: 1367-5788
• DOI: 10.1016/j.arcontrol.2020.04.012

This paper presents new perspectives on the application of Artificial Intelligence (AI) solutions to process Spacecraft (S/C) flight data in order to augment currently used operational S/C health monitoring and diagnostics systems. It captures the growing general interest in the usage of such techniques in the Space engineering domain and applications. Jointly with the AI approach, the operational usage of S/C simulation models (referred to as “discipline models”) is also explored. During S/C development and testing activities, significant efforts are made by the discipline experts to build such models. However, using discipline-specific knowledge to support complex S/C operational activities (e.g., anomaly root cause analysis) remains a challenging task. Based on the current needs of Space Agencies and Industry and by exploiting the advances in AI-based solutions and technologies, this paper proposes an operational S/C model-based diagnostics framework, which can serve as basis for future developments. Such framework combines AI-based techniques, S/C flight data information, and discipline models. Three main needs are addressed: S/C anomaly root cause analysis, S/C prediction behavior, and discipline model refinement. Concrete operational case studies from the Project for On-Board Autonomy (PROBA) satellite family are presented to show the applicability of the proposed framework.