Solution Energy
AI-Driven Predictive Maintenance for Wind Farms
IoT sensors and Azure Machine Learning predict wind turbine failures before they occur, so wind farm operators can plan maintenance instead of reacting to breakdowns at remote sites.
-30%Unplanned downtime
DaysOf advance failure warning
Typical outcomes for this kind of engagement. Results depend on your data and starting point.
THE CHALLENGE
Wind turbines often sit in locations that take a crew a day to reach, and a gearbox or generator failure found after the fact means lost generation plus emergency crane and logistics costs.
- Reactive maintenance on remote assets, with repair costs multiplied by access difficulty
- Lost generation for every day a turbine waits on parts or a crane
- Calendar-based servicing that over-maintains healthy turbines and misses degrading ones
OUR APPROACH
CloudGate instruments the fleet with condition-monitoring sensors feeding Azure IoT Hub and trains anomaly-detection and remaining-useful-life models in Azure Machine Learning on vibration, temperature, pitch and power-curve data.
- Condition-monitoring sensors across the turbine fleet via Azure IoT Hub
- Anomaly detection and remaining-useful-life models in Azure Machine Learning
- Site-level inference on Azure IoT Edge for resilience to connectivity loss
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