Rate the Article: Wind Turbine Damage Detection through Convolutional Neuronal Network, IJSR, Call for Papers, Online Journal
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Downloads: 136 | Views: 371

Research Paper | Computer Science & Engineering | Mexico | Volume 8 Issue 4, April 2019 | Rating: 6.2 / 10


Wind Turbine Damage Detection through Convolutional Neuronal Network

Jose Eduardo Castillo Morales, Perfecto Malaquas Quintero Flores, Jose Crispn Hernandez Hernandez, Alberto Reyes Ballesteros, Edmundo Bonilla Huerta


Abstract: the wind turbines now a days are alternative means to obtain renewable energy, nonetheless, due to the geometry and the environment they are usually affected by different factors such as corrosion caused by the contact with the saltines of the liquids or fractures caused by torsion generated by the wind. Because of this, they require constant monitoring to avoid damaging the integrity of a whole park. This article presents the implementation of a convolutional neuronal network to detect and classify the damages in the external structure of a wind turbine, using different algorithms like image processing filters in-between the layers, MaxPooling, Kernel, SoftMax y Backpropagation.


Keywords: convolutional neuronal network, wind turbines, image processing, damages


Edition: Volume 8 Issue 4, April 2019,


Pages: 79 - 796



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