Predictive Maintenance
Instead of fixing parts on a fixed schedule or after they fail, predictive maintenance acts when data shows a part is trending toward failure.
Three maintenance philosophies
Reactive maintenance fixes things after they break, which is unacceptable in a plant where downtime and safety are coupled. Preventive maintenance replaces parts on a schedule, which wastes life on healthy parts and can still miss early failures. Predictive maintenance uses condition data to intervene at the right moment for each specific component.
How it works
- Condition sensors stream data on temperature, vibration, strain, and dose.
- Models learn the normal signature of each component and detect drift.
- Remaining-useful-life estimates convert drift into an actionable timeline.
- Maintenance is scheduled to intervene before the failure, not after.
Why fusion needs it
Components near the plasma face intense neutron and heat loads. The first wall, divertor, and magnets degrade in ways that are hard to inspect while the plant runs. A model that infers degradation from operational data extends useful life safely and prevents surprises.
Neutron-dose bookkeeping
For the Hyperion breeder, 14 MeV neutrons accumulate damage in structural materials. The twin tracks integrated dose per component and links it to materials qualification data, so replacement timing is grounded in measured material behavior, not a guess.
Fleet effect
A degradation pattern seen on one unit updates the maintenance model for every unit through fleet learning, so later units inherit the operating experience of earlier ones.
Honesty
Remaining-life estimates carry uncertainty, and that uncertainty drives conservative margins until operating data narrows it. This ties into safety and licensing.