AI in Predictive Maintenance for Wind Energy

Wind turbines are exposed to some of the harshest environments on the planet. Over time, leading-edge erosion, lightning strikes, and fatigue cracks can compromise structural integrity and aerodynamic efficiency. Traditional reactive maintenance often results in costly downtime and emergency repairs.

The Power of Predictive Analytics

Predictive maintenance utilizes data to predict when a component is likely to fail, allowing operators to schedule repairs before a catastrophic failure occurs. In the wind industry, this is driven by high-quality visual data collected by autonomous drones and analyzing it with Computer Vision (CV) models.

AI algorithms can identify micro-cracks and erosion patterns that are invisible to the naked eye from the ground. By tracking the progression of these defects over time across multiple inspections, operators can build a specific degradation curve for each blade.

• Reduce O&M costs by up to 30%
• Extend the lifespan of turbine blades
• Optimize annual energy production (AEP)

From Data to Action

Indavian's platform doesn't just find defects; it categorizes them by severity (1-5 scale) and recommends specific repair actions. This closes the loop between inspection and maintenance, ensuring that data directly drives operational decisions.