The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
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L'article a utilisé Alexnet, un cadre d'apprentissage en profondeur, pour diagnostiquer automatiquement les dommages causés aux surfaces des pales des éoliennes. Les images originales des surfaces des pales d’un générateur d’énergie éolienne ont été capturées par des visions industrielles d’un drone (véhicule aérien sans pilote) à 4 rotors. Tout d'abord, un Alexnet à 8 couches, comprenant au total 21 sous-couches fonctionnelles, est construit et paramétré. Deuxièmement, l'Alexnet a été formé avec 10000 6 images, puis testé avec 350 images à 99.001 tours. Enfin, les statistiques des tests du réseau montrent que la précision moyenne du diagnostic des dommages par Alexnet est d'environ 20 %. Nous avons également formé et testé un réseau neuronal BP (Back Propagation) traditionnel, doté d'une couche d'entrée de 5 neurones, d'une couche cachée de 1 neurones et d'une couche de sortie de 19.424 neurone, avec les mêmes données d'image. La précision moyenne du diagnostic des dommages du réseau neuronal BP est inférieure de XNUMX % à celle d'Alexnet. Ce point montre qu'il est possible d'appliquer l'acquisition d'images d'UAV et le classificateur d'apprentissage profond pour diagnostiquer automatiquement les dommages causés aux pales d'éoliennes en service.
Xiao-Yi ZHAO
Inner Mongolia University of Technology,Inner Mongolia Key Laboratory of Mechanical and Electrical Control
Chao-Yi DONG
Inner Mongolia University of Technology,Inner Mongolia Key Laboratory of Mechanical and Electrical Control
Peng ZHOU
Inner Mongolia University of Technology,Inner Mongolia Key Laboratory of Mechanical and Electrical Control
Mei-Jia ZHU
Inner Mongolia University of Technology,Inner Mongolia Key Laboratory of Mechanical and Electrical Control
Jing-Wen REN
Inner Mongolia University of Technology,Inner Mongolia Key Laboratory of Mechanical and Electrical Control
Xiao-Yan CHEN
Inner Mongolia University of Technology,Inner Mongolia Key Laboratory of Mechanical and Electrical Control
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Xiao-Yi ZHAO, Chao-Yi DONG, Peng ZHOU, Mei-Jia ZHU, Jing-Wen REN, Xiao-Yan CHEN, "Detecting Surface Defects of Wind Tubine Blades Using an Alexnet Deep Learning Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 12, pp. 1817-1824, December 2019, doi: 10.1587/transfun.E102.A.1817.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1817/_p
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@ARTICLE{e102-a_12_1817,
author={Xiao-Yi ZHAO, Chao-Yi DONG, Peng ZHOU, Mei-Jia ZHU, Jing-Wen REN, Xiao-Yan CHEN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Detecting Surface Defects of Wind Tubine Blades Using an Alexnet Deep Learning Algorithm},
year={2019},
volume={E102-A},
number={12},
pages={1817-1824},
abstract={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.},
keywords={},
doi={10.1587/transfun.E102.A.1817},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Detecting Surface Defects of Wind Tubine Blades Using an Alexnet Deep Learning Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1817
EP - 1824
AU - Xiao-Yi ZHAO
AU - Chao-Yi DONG
AU - Peng ZHOU
AU - Mei-Jia ZHU
AU - Jing-Wen REN
AU - Xiao-Yan CHEN
PY - 2019
DO - 10.1587/transfun.E102.A.1817
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2019
AB - 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.
ER -