Wind Turbine Blade Surface Damage Detection based on Aerial Imagery and VGG16-RCNN Framework

TL;DR

This paper presents a deep learning framework using aerial imagery and VGG16-RCNN for early detection of surface damage on wind turbine blades, aiming to improve maintenance and prevent system failures.

Contribution

It introduces a novel application of VGG16-RCNN with image augmentation for wind turbine blade damage detection from aerial images.

Findings

VGG16-RCNN outperformed other CNN models in damage detection accuracy.

Image augmentation improved dataset diversity and model performance.

The framework enables early damage detection to enhance wind turbine maintenance.

Abstract

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.