Client Context
A renewable energy company operating 180 wind turbines across 3 wind farms. Maintenance was primarily reactive, with scheduled inspections supplementing break-fix responses.
The Challenge
Unplanned gearbox failures were the single largest cost driver — each failure costing €200K+ in parts and lost generation. Scheduled maintenance couldn’t predict which turbines needed attention, leading to either too-early replacement or unexpected failures.
Our Approach
We deployed sensor-driven predictive models analysing vibration patterns, temperature, oil quality, and power output. The system predicts gearbox failures 21 days in advance with 87% accuracy, enabling planned maintenance during low-wind periods and proactive parts procurement.
Timeline: 20 weeks
The Results
- Unplanned downtime reduced 42%
- Maintenance costs reduced 28%
- Turbine availability: 94% → 97.8%
- Gearbox failures predicted 21 days ahead with 87% accuracy
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