AI USE CASE
Vibration Analysis for Rotating Equipment
Detect bearing faults and misalignment in rotating machinery before costly failures occur.
What it is
Machine learning models trained on vibration sensor data continuously monitor rotating equipment to identify early signs of bearing degradation, misalignment, and imbalance. Plants typically see unplanned downtime reduced by 30–50% and maintenance costs cut by 20–35% compared to time-based schedules. Early fault detection can extend equipment life by months and prevent catastrophic failures that cost tens of thousands of euros in emergency repairs and lost production. The system provides maintenance teams with actionable alerts ranked by fault severity and estimated remaining useful life.
Data you need
Historical and real-time vibration sensor data (accelerometers) from rotating equipment, ideally with labeled failure events or at minimum normal operating baselines.
Required systems
- erp
- data warehouse
Why it works
- Involve experienced vibration analysts to validate model outputs and tune fault thresholds during the pilot phase.
- Ensure sensors are properly mounted and calibrated before any data collection begins.
- Start with the most critical or highest-cost equipment to demonstrate ROI quickly and build buy-in.
- Integrate alerts directly into the existing maintenance workflow or CMMS to ensure follow-through.
How this goes wrong
- Insufficient sensor coverage or poor sensor placement leads to missed fault signatures and false negatives.
- Lack of labeled failure history makes model training unreliable and requires long baseline collection periods.
- Alert fatigue from poorly calibrated thresholds causes maintenance teams to ignore warnings.
- Integration between the ML platform and the CMMS/ERP is neglected, so actionable alerts never reach the right people.
When NOT to do this
Do not deploy this if your plant has fewer than 20 rotating assets or lacks the engineering bandwidth to act on maintenance alerts — the ROI will not justify the sensor infrastructure and integration investment.
Vendors to consider
Sources
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