Condition Monitoring · RENEWABLES
Wind turbines that capture the wind they actually have.
Wind turbines operate across constantly shifting environmental conditions, and fixed operating settings leave energy on the table or load on the gearbox. The XMPro AO Platform monitors wind, turbine and gearbox telemetry continuously, predicts component degradation, and surfaces ranked recommendations to tune blade pitch, yaw and rotation speed — across every turbine in the farm.
What's getting in the way today.
Wind turbine economics depend on extracting maximum energy from every unit of wind without over-stressing the asset. Three pressures compound:
Performance under varying conditions
Wind speed, direction and turbulence shift continuously — static operating settings either leave yield on the table or stress components unnecessarily.
Wear and tear management
Blade, gearbox and bearing degradation accumulates quietly. Without continuous monitoring, the cost shows up as unplanned maintenance and lost generation.
Energy yield versus asset life
Pushing performance and protecting component life pull in different directions — and the right trade-off changes by hour, by site and by season.
Wind Turbine Performance Optimisation — how it works.
A live picture of every turbine on the farm — fed by the sensors already on the asset, with predicted failure modes ranked by yield and reliability impact, and operating recommendations tied to current wind conditions.
The platform integrates wind speed and direction, turbine rotation, yaw error, blade pitch, gearbox temperature and vibration, oil viscosity and weather-forecast data continuously across the farm. ML models predict optimal turbine settings for current and forecast wind conditions, surface specific failure modes (gearbox oil viscosity issues, blade-edge erosion, bearing wear, yaw misalignment) with confidence scoring, and estimate remaining useful life per component. A digital twin lets operations engineers simulate setting changes virtually before applying them. Threshold breaches generate ranked recommendations with event data — wind direction, yaw, power output, blade damage area — and create work requests with special instructions. Interactive 3D turbine views highlight defect locations for the field crew.
*Illustrative dashboards from the platform. Layout, signals and decision points are scoped per site.
Scope this for your operation.
Tell us about your fleet, your control maturity and the lever that matters most. We’ll map this use case to your starting point.
What this looks like in operation.
More yield from the same wind
Blade pitch, yaw and rotation tune continuously to current conditions, capturing energy that fixed settings leave in the air.
Extended component life
Operating recommendations balance yield against gearbox and bearing stress, pushing back the cost of major component replacement.
Right-sized maintenance
Predicted degradation surfaces in time to plan around grid commitments rather than scrambling around an unplanned outage.
Built for these industries.
Other solutions you might explore.
Not a concept. In production.
XMPro is deployed at Tier 1 global operators across asset-intensive and mission-critical industries — delivering measurable results across predictive maintenance, process optimisation and operational intelligence.
Now pushing the frontier.
MAGS agents are achieving what no other industrial platform has demonstrated — sustained autonomous operations at enterprise scale.
