基于无人机图像的风力发电机叶片缺陷识别

仇梓峰; 王爽心; 李蒙

doi:10.12096/j.2096-4528.pgt.2018.043

欢迎来到能源电力期刊网！

English Version 读者登录期刊登录

您当前的位置：

首页 >

文章列表页 >

基于无人机图像的风力发电机叶片缺陷识别

新能源 | 更新时间：2025-06-06

- 基于无人机图像的风力发电机叶片缺陷识别
- Defect Detection of Wind Turbine Blade Based on Unmanned Aerial Vehicle-taken Images
- 发电技术 2018年39卷第3期页码：277-285
- 作者机构：
  
  北京交通大学机械与电子控制工程学院, 北京市海淀区 100044
- 作者简介：
  
  [ "仇梓峰(1993), 男, 硕士研究生, 研究方向为基于无人机的图像检测, qiuzifeng@bjtu.edu.cn" ]
  [ "王爽心(1965), 女, 教授, 博士生导师, 从事电力系统智能控制、过程建模与优化等方面的研究, shxwang1@bjtu.edu.cn" ]
  [ "李蒙(1989), 男, 博士研究生, 从事故障诊断、容错控制及智能优化算法研究, 16116351@bjtu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金项目(50776005);国家自然科学基金项目(51577008)
- DOI：10.12096/j.2096-4528.pgt.2018.043
  中图分类号：
- 收稿：2018-04-20，
  
  纸质出版：2018-06-28
- 稿件说明：
移动端阅览
仇梓峰, 王爽心, 李蒙. 基于无人机图像的风力发电机叶片缺陷识别[J]. 发电技术, 2018,39(3):277-285.

Zifeng QIU, Shuangxin WANG, Meng LI. Defect Detection of Wind Turbine Blade Based on Unmanned Aerial Vehicle-taken Images[J]. Power Generation Technology, 2018, 39(3): 277-285.
仇梓峰, 王爽心, 李蒙. 基于无人机图像的风力发电机叶片缺陷识别[J]. 发电技术, 2018,39(3):277-285. DOI： 10.12096/j.2096-4528.pgt.2018.043.

Zifeng QIU, Shuangxin WANG, Meng LI. Defect Detection of Wind Turbine Blade Based on Unmanned Aerial Vehicle-taken Images[J]. Power Generation Technology, 2018, 39(3): 277-285. DOI： 10.12096/j.2096-4528.pgt.2018.043.

摘要

针对风力发电机叶片人工检测低效，缺陷诊断难的问题，提出一种基于无人机与图像处理的风力发电机叶片缺陷识别方法。通过Halcon 12与Visual Studio 2015的联合开发，实现图像处理流程、检测结果输出以及缺陷回放等功能，包括相机标定、通过快速自适应加权中值滤波处理图像、动态阈值分割叶片图像缺陷特征，利用区域处理识别裂纹和砂眼等缺陷，并对缺陷进行分类与测量以及输出对叶片质量的分析报告等，实现风力发电机叶片表面缺陷的自动检测功能。通过实例验证了该方法在风力发电机叶片表面缺陷检测中的较高精确性与算法稳定性。

Abstract

Aiming at the problems of inefficient manual detection of wind turbine (WT) blades and difficult diagnosis of defects

a method of defects detection for WT blades based on unmanned aerial vehicle (UAV) and image processing is proposed. Through the joint development of Halcon 12 and Visual Studio 2015

the functions of image processing flow

test result output and defects playback are realized

including camera calibration

image processing through fast adaptive weighted median filtering and dynamic threshold segmentation. Through the regional processing to identify defects such as cracks and trachoma

the defects are classified and measured

and the analysis report of WT blades quality can be output to finally realize the automatic detection function of the surface defects of the WT blades. The high accuracy and stability of the algorithm of this method in detecting WT blades surface defects are demonstrated experimentally.

关键词

Keywords

references

Yang B, Sun D.Testing, inspecting and monitoring technologies for wind turbine blades:A survey[J].Renewable&Sustainable Energy Reviews, 2013, 22(8):515-526.

Márquez F P G, Tobias A M, Pérez J M P, et al.Condition monitoring of wind turbines:Techniques and methods[J].Renewable Energy, 2012, 46(5):169-178.

Duttonl A G, Blanch M J, Vionis P. Acoustic emission condition monitoring of a wind turbine rotor blades: laboratory certification testing to large scale in-service deploymen[C]//2003 American Institute of Aeronauties and Astronauties Conference. Copenhagen, 2003.

Ruan J, Ho S C M, Patil D, et al. Structural health monitoring of wind turbine blade using piezoceremic based active sensing and impedance sensing[C]//IEEE, International Conference on Networking, Sensing and Control. IEEE, 2014: 661-666.

Walle G, Abuhamad M, Toma E.Defect indications in sono-thermography in relation to defect location and structure[J].Seminars in Roentgenology, 2004:1-6.

Amirat Y, Benbouzid M E H, Al-Ahmar E, et al.A brief status on condition monitoring and fault diagnosis in wind energy conversion systems[J].Renewable&Sustainable Energy Reviews, 2009, 13(9):2629-2636.

Tsai C S, Hsieh C T, Huang S J.Enhancement of damage-detection of wind turbine blades via CWT-based approaches[J].IEEE Transactions on Energy Conversion, 2006, 21(3):776-781.

Pitchford C, Grisso B L, Inman D J.Impedance-based structural health monitoring of wind turbine blades[J].Proceedings of SPIE-The International Society for Optical Engineering, 2007, 6532:1-11.

Lee J K, Park J Y, Oh K Y, et al.Transformation algorithm of wind turbine blade moment signals for blade condition monitoring[J].Renewable Energy, 2014, 79(1):209-218.

Ozbek M, Meng F, Rixen D J.Challenges in testing and monitoring the in-operation vibration characteristics of wind turbines[J].Mechanical Systems&Signal Processing, 2013, 41(1-2):649-666.

Neeru N, Kaur L. An Experimental Analysis on Removal of Salt and Pepper Noise from Digital Images[C]//International Conference on Smart Trends for Information Technology and Computer Communications. Springer Singapore, 2016: 434-441.

Tong X, Guo J, Ling Y, et al. A new image-based method for concrete bridge bottom crack detection[C]//International Conference on Image Analysis and Signal Processing. IEEE, 2011: 568-571.

Hutchinson T C, Chen Z Q.Improved Image Analysis for Evaluating Concrete Damage[J].Journal of Computing in Civil Engineering, 2006, 20(3):210-216.

Yamaguchi T, Nakamura S, Hashimoto S. An efficient crack detection method using percolation-based image processing[C]//Industrial Electronics and Applications, 2008. Iciea 2008. IEEE Conference on. IEEE, 2008: 1875-1880. http: //ieeexplore. ieee. org/xpl/topAccessedArticles. jsp?punumber=4570851 .

Mishnaevsky L.Composite materials for wind energy applications:micromechanical modeling and future directions[J].Computational Mechanics, 2012, 50(2):195-207.

张柱, 张莹, 闫璠, 等.基于HALCON的全景摄像机标定及应用研究[J].计算机工程与应用, 2016, 52(10):241-246.

Ricolfeviala C, Sanchezsalmeron A J.Lens distortion models evaluation[J].Applied Optics, 2010, 49(30):5914-5928.

于春和, 祁乐阳.基于HALCON的双目摄像机标定[J].电子设计工程, 2017, 25(19):190-193.

于彬, 祁文军.极端环境载荷对复合材料风机叶片特性影响分析[J].机械科学与技术, 2016, 35(7):1133-1138.

Aum J, Kim J H, Jeong J.Effective speckle noise suppression in optical coherence tomography images using nonlocal means denoising filter with double Gaussian anisotropic kernels[J].Applied Optics, 2015, 54(13):13-14.

姚薇, 钱玲玲.矿山遥感图像自适应加权改进中值滤波算法[J].金属矿山, 2016, V45(4):101-105.

Girard M J, Tun T A, Husain R, et al.Lamina cribrosa visibility using optical coherence tomography:comparison of devices and effects of image enhancement techniques[J].Investigative Ophtha-lmology&Visual Science, 2015, 56(2):865-874.

Du W, Tian X, Sun Y. A dynamic threshold segmentation algorithm for anterior chamber OCT images based on wavelet transform[C]//International Congress on Image and Signal Processing. IEEE, 2013: 279-282.

刘南艳, 刘菁, 厍向阳, 等.光照不均条件下的钢管图像分割算法研究[J].激光杂志, 2016, 37(3):46-49.

Seppke B, Bähnisch C, Benthien J T, et al. A concurrent skeleton-based approach for the characterization of wood fibers with sub-pixel precision for fiber board production[C]//International Conference on MASS Data Analysis of Images and Signals, 2015: 1-15.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于无人机巡检的输电线路绝缘子及其异物检测算法

一种基于图像的燃气轮机叶型参数测量方法