AI Bearing Failure Prediction
The Problem
“Predict bearing failures before energy asset downtime”
Organizations face these key challenges:
1
Unplanned outages from sudden bearing failure causing lost generation/throughput and emergency repair premiums
2
High false-alarm rates from static vibration/temperature thresholds leading to alarm fatigue and unnecessary maintenance
3
Limited visibility into early-stage bearing defects under variable operating conditions (load, speed, starts/stops, curtailment)
Impact When Solved
Reduce forced outage hours from rotating equipment by 10–25% through earlier detection and planned interventionsLower bearing-related corrective maintenance costs by 15–30% via targeted replacements and avoided collateral damageImprove asset availability by 0.3–1.0 percentage points (plant) or increase AEP by 0.5–1.5% (wind fleet) with fewer unplanned stoppages
The Shift
Real-World Use Cases
Predictive Maintenance Framework for Wind Turbine Blade Erosion
This is like putting a smart ‘health monitor’ on wind turbine blades so you can tell when their edges are wearing down long before they fail, and schedule service at the best time instead of waiting for breakdowns.
Time-SeriesEmerging Standard
8.5
AI Condition Monitoring for Wind Turbines
This AI framework monitors wind turbines to detect any problems early, helping to prevent energy losses.
anomaly detectionemerging
8.5