基于无人机图像的风力发电机叶片缺陷识别

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

发电技术 ›› 2018, Vol. 39 ›› Issue (3): 277-285.DOI: 10.12096/j.2096-4528.pgt.2018.043

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()

北京交通大学机械与电子控制工程学院, 北京市海淀区 100044

收稿日期:2018-04-20 出版日期:2018-06-30 发布日期:2018-07-27
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
基金资助:
国家自然科学基金项目(50776005);国家自然科学基金项目(51577008)

Defect Detection of Wind Turbine Blade Based on Unmanned Aerial Vehicle-taken Images

Zifeng QIU(),Shuangxin WANG(),Meng LI()

School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China

Received:2018-04-20 Published:2018-06-30 Online:2018-07-27
Supported by:
National Natural Science Foundation of China(50776005);National Natural Science Foundation of China(51577008)

摘要/Abstract

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。

关键词: 风力发电机, 缺陷检测, 无人机, 图像处理

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.

Key words: wind turbine, defects detection, unmanned aerial vehicle, image processing

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.

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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

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