AI USE CASE
IoT-Driven Equipment Failure Prediction
Predict machine breakdowns before they happen to eliminate costly unplanned downtime.
What it is
By combining IoT sensor data with machine learning models, manufacturers can detect anomalies in equipment behaviour days or weeks before a failure occurs. Maintenance teams receive automated alerts and can schedule interventions during planned downtime windows, reducing unplanned stoppages by 30–50%. This typically translates to 10–25% lower maintenance costs and a measurable increase in overall equipment effectiveness (OEE). Plants with high asset intensity — such as continuous process or automotive lines — see the fastest payback, often within 6–12 months.
Data you need
Continuous time-series sensor data (vibration, temperature, pressure, current) from production equipment, plus historical maintenance logs with failure labels.
Required systems
- erp
- data warehouse
Why it works
- Start with two or three high-criticality machines that have rich sensor history and known failure modes.
- Involve maintenance engineers in labelling failure events and defining alert thresholds to build trust.
- Integrate alerts directly into the existing CMMS or ERP maintenance scheduling workflow.
- Run a parallel period comparing model-triggered vs. calendar maintenance outcomes before full cutover.
How this goes wrong
- Insufficient historical failure data means models cannot learn meaningful failure signatures, producing unreliable alerts.
- Poor sensor coverage or inconsistent data quality from legacy machinery undermines model accuracy.
- Maintenance teams distrust model alerts due to early false positives and revert to calendar-based maintenance.
- IoT infrastructure rollout takes far longer than expected, delaying model training and value realisation.
When NOT to do this
Do not deploy predictive maintenance on equipment that already has well-understood, low-cost failure modes and short replacement cycles — the sensor and modelling investment will never pay back.
Vendors to consider
Sources
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