Detecting Surface Defects of Wind Tubine Blades Using an Alexnet Deep Learning Algorithm

Xiao-Yi ZHAO  Chao-Yi DONG  Peng ZHOU  Mei-Jia ZHU  Jing-Wen REN  Xiao-Yan CHEN  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.12   pp.1817-1824
Publication Date: 2019/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.1817
Type of Manuscript: Special Section PAPER (Special Section on Smart Multimedia & Communication Systems)
Category: Machine Learning
deep learning,  Alexnet,  4-rotor UAV,  BP neural network,  wind power generator blade,  damage diagnosis,  

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The paper employed an Alexnet, which is a deep learning framework, to automatically diagnose the damages of wind power generator blade surfaces. The original images of wind power generator blade surfaces were captured by machine visions of a 4-rotor UAV (unmanned aerial vehicle). Firstly, an 8-layer Alexnet, totally including 21 functional sub-layers, is constructed and parameterized. Secondly, the Alexnet was trained with 10000 images and then was tested by 6-turn 350 images. Finally, the statistic of network tests shows that the average accuracy of damage diagnosis by Alexnet is about 99.001%. We also trained and tested a traditional BP (Back Propagation) neural network, which have 20-neuron input layer, 5-neuron hidden layer, and 1-neuron output layer, with the same image data. The average accuracy of damage diagnosis of BP neural network is 19.424% lower than that of Alexnet. The point shows that it is feasible to apply the UAV image acquisition and the deep learning classifier to diagnose the damages of wind turbine blades in service automatically.