AI-Powered Sound Data Analysis for Wind Turbine Blade Health

Sound Data Analysis of Wind Turbine Wind Turbine Blades by AI
At Werover, we leverage the untapped potential of sound. Our AI powered system collects and analyzes raw acoustic data from wind turbine blades to detect early stage damage before it leads to costly failures or unplanned downtime Via our advanced AI-based sound analysis, we manage to reduce maintenance and replacement costs up to 50%, minimizing downtime and maximizing energy efficiency.
Why Sound Data Matters in Blade Monitoring
Traditional blade monitoring often relies on visual inspections or vibration-based sensors mounted directly on the blade, which requires stopping the turbine for sensor installation. In contrast, acoustic signals provide deeper and often earlier insights into the internal condition of the blades. These patterns, captured during regular turbine operation, can reveal microcracks, material fatigue, and other signs of damage that are otherwise difficult to detect.

Raw Sound Data A Unique Competitive Advantage
Unlike other systems in the industry, Werover collects raw unprocessed sound data directly from the turbine environment. This allows us to work with the most authentic and detailed signals, leading to highly accurate diagnostics. We are currently the only company providing blade health monitoring based on raw acoustic data. Up to date, we have +500.000 hours of raw data sound pool.
AI-Powered Analysis for Predictive Maintenance
Our artificial intelligence and machine learning algorithms are designed to identify patterns and anomalies in complex acoustic data. These models detect subtle sound signatures associated with early blade damage. Thanks to using AI-powered sound analysis alongside machine learning algorithms, our system does not only detect damages at the earliest stage, but also categorize the damage types and identify the size of the damages. This enables operators to take action before small issues turn into critical failures, saving time, reducing maintenance & replacement costs and enhancing energy efficiency through minimized downtime.