AI USE CASE
IoT-Driven Equipment Failure Prediction
Predict machine breakdowns before they happen to eliminate costly unplanned downtime.
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Run the diagnostic →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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