AI & Analytics
Predictive Maintenance
ML models predict remaining useful life for critical components -- bearings, seals, motors, and impellers. Know what will fail, when it will fail, and what to do about it.
Prediction Models
Purpose-built models for the components that fail most often.
| Component | Model | Accuracy | Lead Time | Sensor Inputs |
|---|---|---|---|---|
| Bearing Wear | XGBoost + Vibration FFT | 92% | 14-30 days | Vibration (X/Y/Z), temperature, load, speed |
| Seal Failure | LSTM Sequence Model | 88% | 7-21 days | Pressure differential, flow rate, temperature trend |
| Motor Insulation | Random Forest | 90% | 30-60 days | Current imbalance, winding temperature, run hours |
| Impeller Degradation | Physics-Informed NN | 85% | 21-45 days | Head-flow deviation, vibration spectrum, efficiency drop |
| Belt/Coupling Wear | Gradient Boosting | 91% | 10-20 days | Vibration 1x/2x harmonics, alignment offset, temperature |
Maintenance Strategy ROI
Predictive maintenance cuts total cost of ownership by 55% compared to reactive.
Reactive
Preventive
Predictive (TwinEdge)
Core Capabilities
Days of Lead Time
Predict failures 7-60 days in advance. Enough time to order parts, schedule crews, and minimize production impact.
RUL Estimation
Remaining Useful Life displayed as days, confidence interval, and health score. Track degradation trajectory over time.
Failure Mode ID
Models identify the specific failure mode -- bearing inner race, seal face wear, insulation breakdown -- not just "something is wrong."
Maintenance Windows
Algorithm recommends optimal maintenance windows that balance remaining life, production schedules, and crew availability.
Predict Failures Before They Cost You
Cut maintenance costs by 55%, eliminate unplanned downtime, and extend equipment life with ML-driven predictions.