AI & Analytics
Predictive maintenance connected to work execution
Predict remaining useful life for critical components, then connect the prediction to asset context, parts, schedules, O&M guidance, Field closeout, and evidence-backed maintenance history.
Predictive maintenance proof
A prediction only matters when it changes the maintenance plan.
TwinEdge connects RUL models to asset context, work history, parts readiness, O&M guidance, scheduling, Field closeout, capital exposure, and compliance evidence so predictive maintenance becomes execution.
RUL
Remaining life
Health, confidence, and failure-mode context for planning.
Parts
Readiness
Recommended work can include inventory and procurement context.
Field
Execution
Technicians receive context, procedures, and closeout evidence.
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 improves the decision loop by shifting work from emergency reaction to planned, evidence-backed execution.
Reactive
Preventive
Predictive (TwinEdge)
Core Capabilities
Plannable Lead Time
Estimate failure risk early enough to order parts, schedule crews, and reduce operational disruption when data supports prediction.
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.
Move from failure prediction to planned maintenance action.
Use condition, RUL, failure-mode context, parts readiness, and crew windows to plan maintenance before risk becomes emergency work.