Condition Monitoring · RENEWABLES
Wind turbines that flag failure before they take the farm offline.
Gearboxes, bearings and blades wear continuously under load — and fixed-interval inspection programmes either miss the early signal or service healthy assets at cost. The XMPro AO Platform fuses IoT-sensor telemetry with ML to predict component failures across the farm and surface ranked maintenance actions before downtime arrives.
What's getting in the way today.
Wind turbines run continuously in harsh conditions, and the maintenance pressures compound across three fronts:
Early signs of wear missed
Gearboxes, bearings and blades degrade quietly. By the time the failure is visible, the cost has multiplied.
Inefficient maintenance schedules
Fixed-interval programmes either over-service healthy turbines or miss real degradation already in progress — both eat margin.
Unplanned downtime
Unexpected failures hit hard: lost generation, emergency crew mobilisation, and warranty exposure across the fleet.
Wind Turbine Predictive Maintenance — how it works.
A unified picture of every turbine in the farm — fed by the sensors already on the asset, ranked by predicted remaining useful life, and tied to maintenance scheduling and operator workflow.
The platform integrates IoT-sensor telemetry continuously across every turbine — vibration, temperature and acoustics from gearboxes, rotors and blades, alongside wind speed and direction. ML models combine binary classification (will this turbine fail) with regression for remaining useful life, surfacing specific failure modes such as gearbox oil degradation, rotor temperature anomalies and blade erosion with confidence scoring. A digital twin lets reliability engineers test interventions virtually. Threshold breaches generate ranked recommendations with event data, parts list and crew assignment, feeding directly into work-order creation.
*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.
Predicted condition, planned maintenance
Failure modes surface days ahead of stoppage, so crews and parts move into planned outage windows instead of emergency response.
Longer asset life
Condition-based intervention extends the useful life of gearboxes, bearings and blades — the components with the highest replacement cost.
Higher availability
Continuous monitoring captures degradation that scheduled inspection misses, lifting fleet-level generation availability.
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.
