Why predictive maintenance beats run-to-failure — the numbers

Run-to-failure feels cheap because the costs hide elsewhere: in expedited freight for spares, in collateral damage to adjacent equipment, and in unplanned downtime that always arrives at the worst moment. Predictive maintenance moves those costs back into view — and then removes most of them.

What anomaly detection actually sees

Vibration, temperature and pressure signatures drift long before a bearing seizes or a seal lets go. Trained on your own historian data, anomaly detection flags the drift weeks ahead — enough time to order the part at list price and schedule the swap into a planned window.

The numbers that matter

• ✓ Unplanned downtime: typically −30 to −50% in the first two years
• ✓ Spare parts logistics: list price instead of express surcharges
• ✓ TAR scope: fewer surprises opened up during the shutdown itself

The often-overlooked benefit is the third one: every failure you predict is a work package you can plan, which shrinks the unknown scope that makes turnarounds overrun.

„Every failure you can see coming is a failure you can schedule.“

PlantPilot Engineering

PlantPilot connects live sensor data to the digital twin, so a flagged anomaly carries its drawings, parts and history with it. The path from alarm to executed work order is one screen, not five systems.

PlantPilot Editorial Team
Insights on digital plant operation, maintenance & energy

Get in contact