How Acoustic AI Detects Wind Turbine Blade Damage in Real Time

Can wind turbines detect blade damage before it becomes a million-dollar failure?

Balca Yilmaz, Co-Founder and CEO of Werover, explains how acoustic monitoring, IoT, and machine learning are changing the way wind turbine blades are maintained. Instead of shutting down turbines for infrequent inspections, Werover’s system listens to the sound of spinning blades, sending real-time data to the cloud where AI identifies early signs of damage such as lightning strikes, cracks, erosion, and delamination.

Early detection can mean the difference between a short, low-cost repair and a blade replacement that can exceed $1 million and keep a turbine offline for months, especially offshore. Designed to work alongside drone and rope inspections, the technology provides operators with timely warnings that help reduce downtime, energy loss, and maintenance costs while turbines continue producing power.

A closer look at how sound, data, and AI could reshape preventive maintenance across the global wind energy sector.