Elsevier

Procedia CIRP

Volume 81, 2019, Pages 1166-1170
Procedia CIRP

Automatic Optical Surface Inspection of Wind Turbine Rotor Blades using Convolutional Neural Networks

https://doi.org/10.1016/j.procir.2019.03.286Get rights and content
Under a Creative Commons license
open access

Abstract

The operation of wind turbines includes the regular surface inspection of their rotor blades. This leads to considerable downtimes and expenses due to the manual inspection process. A possible solution is the automation of this process by using drones or robots. In this article, we present a key component for such an approach by automating the visual surface inspection with convolutional neural networks (CNN). We provide insights into CNN model selection based on available hardware and training data. We further show that all CNN models reach over 96 % median classification accuracy with the best model, ResNet50, reaching 97.4 %.

Keywords

Convolutional neural network
Deep learning
Optical surface Inspection
Wind turbine rotor blade

Cited by (0)