New designs of large wind turbine blades have become increasingly flexible and thus need cost-efficient monitoring solutions. Hence, we investigate if aerodynamic pressure measurements from a low-cost sensing system can be used to detect structural damage. Our research is based on a wind tunnel study, emulating a simplified wind turbine blade under various conditions. We show that, when using a convolutional-neural-network-based method, structural damage can indeed be detected and its severity can be rated.