Wind Turbine Blade Surface Damage Detection based on Aerial Imagery and VGG16-RCNN Framework
This addresses early damage detection for wind energy systems to prevent malfunctions, but it is incremental as it applies existing methods to a specific domain.
The paper tackles wind turbine blade surface damage detection using aerial imagery and deep learning, achieving improved detection capabilities through image augmentation and testing models like VGG16-RCNN.
In this manuscript, an image analytics based deep learning framework for wind turbine blade surface damage detection is proposed. Turbine blade(s) which carry approximately one-third of a turbine weight are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. The surface damage detection of wind turbine blade requires a large dataset so as to detect a type of damage at an early stage. Turbine blade images are captured via aerial imagery. Upon inspection, it is found that the image dataset was limited and hence image augmentation is applied to improve blade image dataset. The approach is modeled as a multi-class supervised learning problem and deep learning methods like Convolutional neural network (CNN), VGG16-RCNN and AlexNet are tested for determining the potential capability of turbine blade surface damage.