Maintenance usually follows one of three strategies. Reactive: fix it when it breaks. Preventive: service on a fixed schedule. Predictive: service when the data says failure is approaching. Predictive maintenance reduces both breakdowns and unnecessary servicing.
The data you need
| Signal | What it can reveal |
|---|---|
| Vibration | Bearing wear, imbalance, misalignment |
| Temperature | Overheating motors, friction, cooling issues |
| Current / power draw | Mechanical load changes, motor faults |
| Cycle counts and run hours | Usage-based service needs |
The roadmap
- Instrument critical machines with sensors and an edge gateway.
- Monitor live data with threshold alerts.
- Collect history including failure events and maintenance records.
- Model: train machine-learning models to spot patterns before failure.
- Act: create maintenance requests in your ERP automatically.
Be realistic
Models need history, including some failures, to learn from. Start with threshold alerts, which deliver value immediately, and add prediction as data accumulates.