Journal article
Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Wind Turbine Structures and Component Design, Department of Wind Energy, Technical University of Denmark3
Department of Wind Energy, Technical University of Denmark4
EasyInspect ApS5
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark6
Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages.
Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades.
We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision.
Language: | English |
---|---|
Publisher: | MDPI AG |
Year: | 2019 |
Pages: | 676 |
ISSN: | 19961073 |
Types: | Journal article |
DOI: | 10.3390/en12040676 |
ORCIDs: | Shihavuddin, ASM , Chen, Xiao , Christensen, Anders Nymark , Riis, Nicolai Andre Brogaard , Branner, Kim , Dahl, Anders Bjorholm and Paulsen, Rasmus Reinhold |