Computing for Predictive Maintenance

Using sensor data and models to forecast when a component will need service, so maintenance is scheduled by condition rather than by calendar.

Three maintenance strategies

Reactive maintenance fixes things after they break, risking unplanned downtime. Preventive maintenance services on a fixed schedule, which is safe but wastes life on parts that were still healthy. Predictive maintenance uses condition data to estimate remaining useful life and service each component when it actually needs it, capturing more of its life while avoiding failure.

How it works

• Collect condition signals: vibration, temperature, acoustic emission, electrical signatures
• Extract features that correlate with degradation
• Model the degradation trajectory toward a failure threshold
• Estimate remaining useful life with uncertainty
• Schedule service inside the safe window before failure

Remaining useful life

The central output is remaining useful life: how long until a component crosses a failure threshold, expressed as a distribution rather than a single number. A point estimate invites overconfidence; a distribution lets planners choose how much margin to keep. The estimate updates as new data arrive, tightening as the component nears the threshold.

def remaining_life(trend, current, threshold):
    # trend: degradation per unit time (with uncertainty upstream)
    if trend <= 0: return float('inf')
    return (threshold - current) / trend

The neutron environment

In a fusion plant, some degradation is driven by neutron damage, which is hard to sense directly and must be inferred from exposure models rather than a simple wear signal. Predictive maintenance for those components leans on neutronics-based damage estimates (see Materials Degradation) as much as on live sensors.

Kronos framing

During design, predictive-maintenance models are built and validated against simulated and analog data so the plant is instrumented for condition monitoring from the start. This is analysis and planning; it carries no cost figures.