Institute of Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Ali Jafarabadi
Institute of Structural Engineering, ETH Zürich, Stefano-Franscini-Platz 5, 8093 Zürich, Switzerland
Empa, Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland
Xudong Jian
Future Resilient Systems, Singapore-ETH Centre, Singapore, 138602 Singapore
Max von Danwitz
German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Rathausallee 12, 53757 Sankt Augustin, Germany
Alexander Popp
German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Rathausallee 12, 53757 Sankt Augustin, Germany
Institute for Mathematics and Computer-Based Simulation (IMCS), University of the Bundeswehr Munich, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany
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.
New designs of large wind turbine blades have become increasingly flexible and thus need...