AI Bearing Failure Prediction
The Problem
“Predict bearing failures before energy asset downtime”
Organizations face these key challenges:
Unplanned outages from sudden bearing failure causing lost generation/throughput and emergency repair premiums
High false-alarm rates from static vibration/temperature thresholds leading to alarm fatigue and unnecessary maintenance
Limited visibility into early-stage bearing defects under variable operating conditions (load, speed, starts/stops, curtailment)
Impact When Solved
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.
AI Condition Monitoring for Wind Turbines
This AI framework monitors wind turbines to detect any problems early, helping to prevent energy losses.