Client Context
A renewable energy company operating 180 wind turbines across 3 wind farms. Maintenance is primarily reactive, with scheduled inspections supplementing break-fix responses.
The Challenge
Unplanned gearbox failures are the single largest cost driver — each failure costs €200K+ in parts and lost generation. Scheduled maintenance can't predict which turbines need attention, leading to either too-early replacement or unexpected failures.
Our Approach
We deploy 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 projected to fall by 42%
- Maintenance costs expected to reduce by 28%
- Turbine availability projected to improve from 94% to 97.8%
- Gearbox failures predicted 21 days ahead with 87% accuracy
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