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Defect identification of wind turbine blades based on defect semantic features with transfer feature extractor

机译:基于缺陷语义特征的传递特征提取器识别风电叶片缺陷

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摘要

The monitoring of the status of the wind turbine blades is significant for the wind generation system and currently mainly dependent on manual visual inspections. The variance of the blade defects and the lack of the blade defect images make the defect identification of the wind turbine blades challenging. This paper proposes a defect identification method of wind turbine blades based on defect semantic features with transfer feature extractor. A deep convolutional neural network (DCNN) is built and is trained on the ImageNet Large Scale Visual Recognition Challenge dataset. The deep hierarchical features of the training blade images are extracted by the trained DCNN and fed into a classifier. By training on the labeled blade images, the first n layers of the trained DCNN is selected as the transfer feature extractor to extract the defect semantic features and the defect classifier is also obtained. The blade images can be diagnosed by the defect classifier based on the defect semantic features. The experiments are conducted on a real dataset of wind turbine blade images. The experimental results demonstrate the high learning ability of the proposed method from the small samples and its effectiveness for the defect identification of wind turbine blades. (C) 2019 Elsevier B.V. All rights reserved.
机译:风力涡轮机叶片状态的监视对于风力发电系统非常重要,目前主要取决于人工目视检查。叶片缺陷的变化和叶片缺陷图像的缺乏使得对风力涡轮机叶片的缺陷识别具有挑战性。提出了一种基于缺陷语义特征的传递特征提取器的风机叶片缺陷识别方法。建立了深度卷积神经网络(DCNN),并在ImageNet大规模视觉识别挑战数据集上对其进行了训练。训练叶片图像的深层次特征由训练后的DCNN提取,并输入到分类器中。通过对标记的叶片图像进行训练,选择训练后的DCNN的前n层作为传递特征提取器,以提取缺陷的语义特征,并获得缺陷分类器。叶片图像可以由缺陷分类器基于缺陷语义特征来诊断。实验是在风力涡轮机叶片图像的真实数据集上进行的。实验结果表明,该方法从小样本中就具有很高的学习能力,并且对风力发电机叶片的缺陷识别有效。 (C)2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2020年第1期|1-9|共9页
  • 作者

  • 作者单位

    Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China;

    Natl Univ Singapore Fac Engn Dept Elect & Comp Engn Singapore 117580 Singapore|Natl Univ Singapore Adv Robot Ctr Singapore 117580 Singapore;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Defect identification; Defect semantic feature; Transfer feature extractor; Wind turbine blades;

    机译:缺陷识别;缺陷语义特征;转移特征提取器;风力涡轮机叶片;

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