为实现风电机组叶片损伤检测的高效化、智能化、便捷化,研究一种基于数字图像处理技术的风电机组叶片裂纹损伤识别以及裂纹类型判断和特征参数提取的方法。以无人机采集的风电机组叶片图像为研究对象,通过对比灰度化、滤波、阈值分割等图像处理步骤的多种算法,对形态学处理方法进行改进,首先选用平均值法对叶片图像进行灰度处理,其次使用中值滤波对图像进行去噪处理,再次使用Otsu阈值分割以实现裂纹区域的分割,然后基于改进的形态学方法提取出完善的叶片裂纹损伤区域,最后基于连通域原理完成裂纹区域的框取。基于上述算法设计风电机组叶片裂纹损伤识别系统以实现叶片裂纹图像检测的可视化处理、裂纹类型判断及裂纹特征参数提取等功能。结果表明,该系统对于风电机组叶片裂纹损伤检测具有可靠的识别精度,识别准确率为85%,实现了风电机组叶片裂纹损伤的自动识别与特征参数提取,提高了叶片裂纹损伤的检测效率。
Abstract
The method for crack damage identification, crack type judgment and feature parameter extraction of wind turbine blades based on digital image processing technology is studied in order to realize the high efficiency, intelligence and convenience of wind turbine blade damage detection. Different algorithms of the graying, filtering and threshold segmentation image processing steps are compared using blade images collected by the drone. The morphological processing method is improved. Firstly, the average value method is selected to conduct grayscale processing on the blade image. Secondly, the median filter is used to denoise the image. Thirdly, the Otsu threshold segmentation method is used to achieve the segmentation of the crack area. Then the perfect blade crack damage area is extracted based on the optimized morphological method, and finally the crack area is framed based on the connected domain principle. A wind turbine blade crack damage identification system is designed based on the above selected and improved algorithms to realize the visual processing of blade crack image detection, crack type judgment and crack feature parameter extraction. The results show that the system has reliable identification accuracy for the crack damage detection of wind turbine blades, and the identification accuracy is 85%. The designed system realizes the automatic identification and feature parameter extraction of wind turbine blade crack damage, and improves the detection efficiency of blade crack damage.
关键词
风电机组 /
叶片损伤 /
数字图像处理 /
裂纹损伤识别 /
特征提取 /
识别系统
Key words
wind turbine /
blades damage /
digital image processing /
crack damage identification /
characteristic extraction /
identification system
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] WANG W J, XUE Y, HE C K, et al.Review of the typical damage and damage-detection methods of large wind turbine blades[J]. Energies, 2022, 15(15): 5672.
[2] DU Y, ZHOU S X, JING X J, et al.Damage detection techniques for wind turbine blades: a review[J]. Mechanical systems and signal processing, 2020, 141: 106445.
[3] 陈雪峰, 郭艳婕, 许才彬, 等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程, 2020, 31(2): 175-189.
CHEN X F, GUO Y J, XU C B, et al.Review of fault diagnosis and health monitoring for wind power equipment[J]. China mechanical engineering, 2020, 31(2): 175-189.
[4] LI D S, HO S C M, SONG G B, et al. A review of damage detection methods for wind turbine blades[J]. Smart materials and structures, 2015, 24(3): 033001.
[5] DENG L W, GUO Y G, CHAI B R.Defect detection on a wind turbine blade based on digital image processing[J]. Processes, 2021, 9(8): 1452.
[6] PENG L, LIU J.Detection and analysis of large-scale WT blade surface cracks based on UAV-taken images[J]. IET image processing, 2018, 12(11): 2059-2064.
[7] ZHANG H Y, JACKMAN J.Feasibility of automatic detection of surface cracks in wind turbine blades[J]. Wind engineering, 2014, 38(6): 575-586.
[8] 毛希玮, 徐莹莹. 基于无人机的风机叶片缺陷自动检测技术[J]. 复合材料科学与工程, 2020(9): 85-89.
MAO X W, XU Y Y.Automatic defect detection of wind blade surface via uav[J]. Composites science and engineering, 2020(9): 85-89.
[9] 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.
QIU Z F, WANG S X, LI M.Defect detection of wind turbine blade based on unmanned aerial vehicle-taken images[J]. Power generation technology, 2018, 39(3): 277-285.
[10] 翟永杰, 张木柳, 乔弘, 等. 基于显著性检测的风机叶片裂纹提取研究[J]. 南方能源建设, 2016, 3(2): 136-140.
ZHAI Y J, ZHANG M L, QIAO H, et al.Research on extraction of fan blade crack based on salient region detection[J]. Southern energy construction, 2016, 3(2): 136-140.
[11] WANG L, ZHANG Z J.Automatic detection of wind turbine blade surface cracks based on UAV-taken images[J]. IEEE transactions on industrial electronics, 2017, 64(9): 7293-7303.
[12] 丛智慧. 基于巡检图像的风机桨叶故障诊断系统研究[D]. 北京: 华北电力大学, 2018.
CONG Z H.Research on fault diagnosis system of fan blade based on inspection image[D]. Beijing: North China Electric Power University, 2018.
[13] 陈建平, 侯楠, 申振腾. 风力机叶片运行状态机器视觉监测技术研究[J]. 太阳能学报, 2020, 41(12): 238-244.
CHEN J P, HOU N, SHEN Z T.Research on machine vision monitoring technology for operational status of wind turbine blade[J]. Acta energiae solaris sinica, 2020, 41(12): 238-244.
[14] HAI X A, CAO S K, CUI S B, et al.Image filter processing algorithm analysis and comparison[J]. Journal of physics: conference series, 2021, 1820(1): 012192.
[15] SUN X X, QU W. Comparison between mean filter and median filter algorithm in image denoising field[J]. Applied mechanics and materials, 2014, 644/645/646/647/648/649/650: 4112-4116.
[16] 王雪平, 张建斐, 李万润, 等. 基于机器视觉的风电叶片风沙侵蚀程度检测方法研究[J]. 太阳能学报, 2020, 41(5): 166-173.
WANG X P, ZHANG J F, LI W R, et al.Study on monitoring method of wind power blades erosion severity under wind-sand storm based on machine vision technology[J]. Acta energiae solaris sinica, 2020, 41(5): 166-173.
[17] OTSU N.A threshold selection method from gray-level histograms[J]. IEEE transactions on systems, man, and cybernetics, 1979, 9(1): 62-66.
[18] 康爽, 陈长征, 赵思雨, 等. 自适应差异多尺度形态学的风力机叶片红外图像增强研究[J]. 中国机械工程, 2021, 32(7): 786-792.
KANG S, CHEN C Z, ZHAO S Y, et al.Study on infrared image enhancement of wind turbine blades based on adaptive differential multiscale morphology(ADMM)[J]. China mechanical engineering, 2021, 32(7): 786-792.
[19] 张建斐. 基于机器视觉的风力发电机叶片表面缺陷检测研究[D]. 兰州: 兰州理工大学, 2018.
ZHANG J F.Study on detecting surface defects of wind turbine blade based on machine vision[D]. Lanzhou: Lanzhou University of Technology, 2018.
[20] 茅宇琳. 基于无人机采集图像的风机叶片多种表面缺陷检测研究[D]. 北京: 北京交通大学, 2020.
MAO Y L.Research on detection of multi-type surface defects of wind turbine blades based on images collected by UAV[D]. Beijing: Beijing Jiaotong University, 2020.
[21] 胡世创, 魏莹玉, 周唯逸, 等. 基于图像处理的风电叶片裂纹检测系统设计[J]. 可再生能源, 2018, 36(8): 1231-1237.
HU S C, WEI Y Y, ZHOU W Y, et al.Design of crack detection system based on image processing for power wind blades[J]. Renewable energy resources, 2018, 36(8): 1231-1237.
[22] 尹玉, 张永, 王健, 等. 基于热红外图像的风力机叶片损伤识别方法研究[J]. 太阳能学报, 2022, 43(2): 492-497.
YIN Y, ZHANG Y, WANG J, et al.Research on wind turbine blade damage identification method based on thermal infrared image[J]. Acta energiae solaris sinica, 2022, 43(2): 492-497.
[23] 李万润, 张建斐, 王雪平, 等. 基于图像处理技术的风电叶片表面划痕特征提取方法研究[J]. 太阳能学报, 2020, 41(12): 278-287.
LI W R, ZHANG J F, WANG X P, et al.Feature extraction method of wind turbine blade surface scratches based on image processing[J]. Acta energiae solaris sinica, 2020, 41(12): 278-287.
基金
江苏省输配电装备技术重点实验室自主科研课题（2022JSSPD07）; 中央高校基本科研业务费专项资金（B210202063）
PDF(2838 KB)
