Researchers have developed a new method for detecting damage in large wind turbine blades using aerodynamic pressure measurements. This approach utilizes a novel, non-intrusive sensing system called Aerosense, which was previously explored by Franz et al. in 2025. A convolutional neural network was trained on pressure data from an experimental airfoil to identify and classify structural damage, demonstrating effective real-time detection and quantification. The current study enhances this pipeline by integrating physics-based insights and explainable machine learning to improve the transparency and robustness of the damage detection system. AI
IMPACT This research could lead to more reliable and cost-effective structural health monitoring for wind turbines, improving operational efficiency and safety.
RANK_REASON The cluster contains an academic paper detailing a new research methodology and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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