Predictive maintenance in steel plants: a practical guide
Short answerPredictive maintenance uses equipment history and sensor data, such as vibration, temperature and running hours, to estimate which machines are likely to fail soon, so maintenance can be planned before a breakdown. In a steel plant, start with a few critical assets, make sure breakdowns and repairs are recorded consistently, then add sensor data and AI risk scoring.
Preventive vs predictive
| Approach | Trigger | Weakness |
|---|---|---|
| Reactive | After a breakdown | Unplanned downtime |
| Preventive | Fixed time or usage | Some work done too early or too late |
| Predictive | Estimated failure risk | Needs reliable data |
Where to start
- Pick 10 to 20 critical assets whose failure stops production
- Record every breakdown with cause, duration and parts
- Add running hours and available sensor readings
- Review risk scores weekly with the maintenance team
- Expand to more assets once the process works
Common pitfalls
- Starting with too many assets
- Inconsistent breakdown records
- Ignoring the maintenance team's judgement
- No link between maintenance and spares stock
Frequently asked
Do we need sensors for predictive maintenance?
Sensors improve accuracy, but you can begin with breakdown history and running hours and add sensors to critical assets over time.
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