About the project
To develop reliable satellites “quickly and affordably”, small satellite missions accept higher risk, learning from failures across generations with risk assessment relying on individual team experience. This makes it almost impossible to achieve a general risk framework for this class of satellite. This research will apply Artificial Intelligence to analyse past small satellite missions to develop a global risk assessment framework.
Traditional satellite development prioritises ultra-high reliability, resulting in long timelines and high costs. In contrast, small satellites, particularly CubeSats, embrace a risk-tolerant approach, enabling rapid deployment and learning from failure. However, mission assurance (MA) remains a critical challenge, especially for first-time developers.
The central research question of this project is: How can we build reliable satellites quickly and affordably? This project aims to develop an AI-powered global risk assessment framework using data from over 2,500 past small satellite missions. By leveraging publicly available satellite design and in-orbit malfunction data, including non-English sources, the research will create a comprehensive failure analysis and risk prioritisation database. This will support cost-effective decision-making and improve reliability for future small satellite missions.
The specific research objectives are:
- Develop a database and classification (Failures and successes) of CubeSat missions, based on defined reliability metrics, e.g stakeholders, mission type, mission success, development philosophy etc.
- Identify risks (malfunctions, failures) that actually occurred in orbit and their possible effects on small satellite missions.
- Develop and validate a novel CubeSat reliability assessment model, considering the interdependency of unique constraints, possible failure modes, and space environment attributes, among others.
- Validate the proposed framework through simulation and experimental testing, demonstrating improved accuracy in reliability predictions and increased resilience against mission-critical failures.
Standout features:
- access to cutting-edge research facilities at the University of Southampton
- potential collaboration and placement with the small satellite industry
- training in AI, space systems engineering, and satellite mission design
- contribution to space sustainability and global satellite standards.
