For decades maintenance followed two approaches: run-to-failure, fixing the machine after it stops, and preventive, with interventions at fixed intervals even when the component is still in good condition.
What data changes
Predictive maintenance uses data measured on machines (vibration, temperature, power draw, operating hours) to understand when a component is degrading and schedule the intervention at the right time.
The benefits
- Fewer unplanned stoppages.
- Spare parts replaced when really needed, not before.
- Interventions scheduled during low-production periods.
- A data history that also helps improve processes.
Where to start
There is no need to put sensors on everything at once: start with the most critical machines, measure a few meaningful parameters and grow the system as the data proves its value.
