Image-based damage recognition of wind turbine blades

Abstract

The wind turbine blades are the key part of converting wind energy into electrical energy. Currently the fault diagnosis of blades is mainly dependent on manual visual inspections. In this paper, an image based fault diagnosis method for wind turbine blades is proposed. The blade damage recognition is realized by two-stage learning. The first learning stage is deep feature extractor learning stage. A deep convolutional neural network (ConvNet) is built and trained on the ILSVRC dataset, owing to the lack of the blade damage images. The trained ConvNet without the last two layer is extracted as the feature extractor of the blade images. The second learning stage is pattern learning stage. Deep features of the training blade images are extracted and used to train a classifier to identify the damage type of the blades. The damage identification of the wind blades can be realized by the combination of the deep feature extractor and the classifier. The wind turbine blade images in the experiment were taken at a real wind power station. Three conventional feature extraction methods, which are Histogram of Oriented Gradient, Scale Invariant Feature Transform and Texture, are adopted to compared with the proposed method. The effectiveness of the proposed approach is validated through experiments, and the results show that for the damage recognition of wind turbine blades.