  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.
