From calendar schedules to condition-based servicing
Traditional maintenance for industrial robots is often scheduled: replace this belt every six months, inspect this gearbox every thousand hours of operation, regardless of how the specific unit has actually been used. This is safe but wasteful in both directions. A robot run gently in a low-load application gets serviced too often, wasting labor and parts. A robot run hard in a demanding application might fail well before its scheduled service date, causing exactly the unplanned downtime the schedule was meant to prevent. The fixed calendar has no way to know the difference, because it isn't looking at the robot's actual accumulated condition at all.
A digital twin changes this by making the robot's actual operational history and current condition the basis for the maintenance decision. Because the twin continuously accumulates real sensor data (vibration signatures, temperature trends, motor current draw, cycle counts, load history) it can build a model of how a specific component in a specific robot is actually wearing, not how components wear on average. That model can then be run forward: given the accumulated wear pattern and typical degradation curves, when is this particular bearing or belt or joint likely to fail?