NASA HUNCH
AI predictive maintenance for lunar rovers.
Spot issues before they become failures.
Through NASA HUNCH, I participate in a student software team exploring AI-based predictive maintenance for lunar rovers. Our project asks how a rover might identify equipment or system issues early enough for a mission team to respond before they cause a failure.
On the Moon, access to repair facilities, replacement parts and hands-on support is limited. Detecting a developing issue earlier could help teams plan maintenance and protect valuable mission time.
This is a software question as much as an aerospace one: an early warning has to give the people responsible for a vehicle useful context for deciding what to investigate.
Explore AI as an early-warning tool.
The project explores whether AI could recognize patterns in rover equipment or system behavior that point to a potential problem. The aim is to give mission teams useful notice so they can investigate and decide what action to take.
Early-stage student software work.
I contribute as a member of the student software team, helping explore the problem and the proposed software direction. The project is in an early stage, and substantial technical implementation has not yet been completed. For now, the focus is defining a useful problem before committing to a technical solution.