Predictive maintenance uses data from equipment to predict failures, so maintenance happens before breakdowns but not unnecessarily early.
Maintenance Strategies
- Reactive: fix it when it breaks — costly downtime.
- Preventive: service on a fixed schedule — wasteful if parts are replaced early.
- Predictive: service when data shows failure is approaching.
Data Sources
- Sensor readings: vibration, temperature, pressure, current, sound.
- Operating conditions and load.
- Maintenance logs and failure records.
- Equipment age and specifications.
Approaches
- Thresholds and rules on sensor values — simple and explainable.
- Anomaly detection when failures are rare or unlabelled.
- Classification: will this machine fail within the next N days?
- Remaining useful life (RUL) regression: how long until failure?
Challenges
- Few failures: well-maintained equipment rarely fails, so labelled examples are scarce.
- Messy maintenance records: free-text logs, missing dates.
- Changing conditions: new operating regimes look like anomalies.
- Sensor issues: faulty sensors can mimic equipment faults.
Measuring Value
Track avoided downtime, maintenance costs, false alarms and missed failures. Involve maintenance teams early; their knowledge is essential for features and for trusting alerts.
Start Small
Pilot on one critical asset type with good data, prove value, then expand.