Automatic Optical Surface Inspection of Wind Turbine Rotor Blades using Convolutional Neural Networks

被引：26

作者：

Denhof, Dimitri ^{[1
]}

Staar, Benjamin ^{[1
]}

Luetjen, Michael ^{[1
]}

Freitag, Michael ^{[1
,2
]}

机构：

[1] Univ Bremen, BIBA Bremer Inst Prod & Logist GmbH, Hsch Ring 20, D-28359 Bremen, Germany

[2] Univ Bremen, Fac Prod, Engn, Bibliothekstr 1, D-28359 Bremen, Germany

来源：

52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS) | 2019年 / 81卷

关键词：

Convolutional neural network; Deep learning; Optical surface Inspection; Wind turbine rotor blade; ARCHITECTURES;

D O I：

10.1016/j.procir.2019.03.286

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The operation of wind turbines includes the regular surface inspection of their rotor blades. This leads to considerable downtimes and expenses due to the manual inspection process. A possible solution is the automation of this process by using drones or robots. In this article, we present a key component for such an approach by automating the visual surface inspection with convolutional neural networks (CNN). We provide insights into CNN model selection based on available hardware and training data. We further show that all CNN models reach over 96 % median classification accuracy with the best model, ResNet50, reaching 97.4 %. (C) 2019 The Authors. Published by Elsevier Ltd.

引用

页码：1166 / 1170

页数：5

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