Wind Turbine Predictive Maintenance
AI models fuse SCADA, vibration, weather, and inspection data to predict wind turbine component failures before they occur, from blades and gearboxes to generators. By enabling condition-based maintenance scheduling and asset optimization across onshore and offshore fleets, this reduces unplanned downtime, extends asset life, and maximizes energy yield and ROI for wind operators.
The Problem
“You’re finding turbine failures too late—downtime and emergency repairs are killing AEP.”
Organizations face these key challenges:
SCADA alarms trigger only after performance has already degraded or damage has progressed.
Maintenance planning is reactive: parts, cranes/vessels, and crews are mobilized last-minute—especially painful offshore.
Condition data is fragmented across SCADA, CMS/vibration, weather, and inspections, so no one trusts a single “source of truth.”
High variance in decisions between sites/technicians leads to inconsistent maintenance quality and repeated failures.