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起重机械金属结构缺陷的热成像技术研究

谢文昕 马伟 杜雪雪 倪佳敏 殷晨波

谢文昕, 马伟, 杜雪雪, 倪佳敏, 殷晨波. 起重机械金属结构缺陷的热成像技术研究[J]. 红外技术, 2022, 44(7): 741-749.
引用本文: 谢文昕, 马伟, 杜雪雪, 倪佳敏, 殷晨波. 起重机械金属结构缺陷的热成像技术研究[J]. 红外技术, 2022, 44(7): 741-749.
XIE Wenxin, MA Wei, DU Xuexue, NI Jiamin, YIN Chenbo. Thermal Imaging Technology for Metal Structure Defects of Lifting Machinery[J]. Infrared Technology , 2022, 44(7): 741-749.
Citation: XIE Wenxin, MA Wei, DU Xuexue, NI Jiamin, YIN Chenbo. Thermal Imaging Technology for Metal Structure Defects of Lifting Machinery[J]. Infrared Technology , 2022, 44(7): 741-749.

起重机械金属结构缺陷的热成像技术研究

详细信息
    作者简介:

    谢文昕(1997-),男,硕士,从事再制造无损检测研究。E-mail:xiewenxin_iacm@163.com

    通讯作者:

    殷晨波(1963-),男,教授,博士,主要研究方向为工程机械数字化创新设计与制造。E-mail:yinchenbo@njtech.edu.cn

  • 中图分类号: R445.7

Thermal Imaging Technology for Metal Structure Defects of Lifting Machinery

  • 摘要: 对起重机械金属结构裂纹缺陷的识别是红外热成像检测技术的新方向。介绍了脉冲红外热成像技术检测原理,设计了脉冲红外热成像检测系统,并根据脉冲红外热成像检测系统搭建了脉冲红外热成像检测系统实验平台。采用中值滤波和巴特沃斯低通滤波对实验中采集到的红外图像进行图像处理,并针对以上算法处理后缺陷轮廓边缘模糊的问题,提出了巴特沃斯带通滤波算法。对图像进行阈值分割、边缘检测后提取出缺陷轮廓特征,根据平板试件的实际尺寸和轮廓特征图像像素之间的换算关系,最终得到裂纹缺陷的识别精度。经过对比验证,脉冲红外热成像技术可以满足对起重机械金属结构裂纹缺陷检测的检测需求。
  • 图  1  脉冲红外热成像检测原理图

    Figure  1.  The principle of pulsed infrared thermal imaging detection

    图  2  脉冲红外热成像硬件系统框图

    Figure  2.  The diagram of pulsed infrared thermal testing hardware system

    图  3  脉冲红外热成像检测系统实验平台

    Figure  3.  Experimental platform of pulsed infrared thermal testing system

    图  4  不同脉冲强度下被检试件表面温度变化情况

    Figure  4.  Temperature field of the specimen surface after different pulsed intensities

    图  5  金属裂纹试件

    Figure  5.  Metal crack specimen

    图  6  采集的某帧红外图像

    Figure  6.  One of collected infrared thermal image

    图  7  灰度变换后的图像

    Figure  7.  Infrared image after grayscale transforming

    图  8  直方图均衡化

    Figure  8.  Histogram equalized image

    图  9  中值滤波后图像

    Figure  9.  Median filtered image

    图  10  频率域图像处理

    Figure  10.  Image processing algorithm based on frequency domain

    图  11  巴特沃斯低通滤波

    Figure  11.  Butterworth low-pass filtered image

    图  12  改进的巴特沃兹带通滤波算法处理后的图像

    Figure  12.  Infrared images aftert the improved Butterworth bandpass filtering algorithm

    图  13  改进巴特沃斯带通滤波算法处理后

    Figure  13.  The binary image after the improved Butterworth band pass filtering algorithm

    图  14  Canny算子边缘检测效果

    Figure  14.  Canny operator edge detection result

    表  1  各种滤波后图像的峰值信噪比

    Table  1.   Peak signal-to-noise ratio of various filtered images

    Test subject Median Filter SNR Butterworth Low Pass Filtering Algorithm SNR Butterworth Bandpass Filtering Algorithm SNR
    Metal specimens with crack defects 43.3848 38.9708 68.9181
    下载: 导出CSV

    表  2  裂纹缺陷的特征识别参数及其精度

    Table  2.   Identification parameters and accuracy of rack defects features

    Parameter category Parameters Parameter value
    Attributes PIXS/pixel 2692
    PIXL/pixel 192
    PIXD/pixel 18
    Actual parameters of crack defect Actual area/mm2 20
    Actual length/mm 20
    Actual width/mm 1
    Crack defect calculation parameters Area-calculation/mm2 19.88
    Length-calculation/mm 16.50
    Width-calculation/mm 1.55
    Crack defect calculation error Area-calculation error/% 0.60 %
    Length-calculation error/% 17.50 %
    Width-calculation error/% 55.00 %
    下载: 导出CSV
  • [1] 周俊光. 浅谈起重机械安全隐患及缺陷[J]. 智能城市, 2019, 5(6): 176-177. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNCS201906111.htm

    ZHOU Junguang. Talking about the hidden dangers and defects of hoisting machinery[J]. Intelligent City, 2019, 5(6): 176-177. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNCS201906111.htm
    [2] 贾文晶. 基于红外图像处理的钢轨裂纹检测研究[D]. 兰州: 兰州交通大学, 2017.

    JIA Wenjing. Research on Rail Crack Detection Based on Infrared Image Processing[D]. Lanzhou: Lanzhou Jiaotong University, 2017.
    [3] Avdelidis N P, Almond D P, Dobbinson A, et al. Aircraft composites assessment by means of transient thermal NDT[J]. Progress in Aerospace Sciences, 2004, 40(3): 143-162. doi:  10.1016/j.paerosci.2004.03.001
    [4] MIAN A, HAN X, Islam S. Fatigue damage detection in graphite/epoxy composites using sonic infrared imaging technique[J]. Composites Science & Technology, 2004, 64(5): 657-666.
    [5] ZOU H, HUANG F Z. A novel intelligent fault diagnosis method for electrical equipment using infrared thermography[J]. Infrared Physics and Technology, 2015, 73: 29-35. doi:  10.1016/j.infrared.2015.08.019
    [6] 秦雷, 刘俊岩, 龚金龙, 等. 超声红外锁相热像技术检测金属板材表面裂纹[J]. 红外与激光工程, 2013, 42(5): 1123-1130. doi:  10.3969/j.issn.1007-2276.2013.05.003

    QIN Lei, LIU Junyan, GONG Jinlong, et al. Testing surface crack defects of sheet metal with ultrasoniclock-in thermography[J]. Infrared and Laser Engineering, 2013, 42(5): 1123-1130. doi:  10.3969/j.issn.1007-2276.2013.05.003
    [7] 胡海林, 任煜文, 郭迪, 等. 基于红外热成像的物体缺陷检测方法研究[J]. 沈阳理工大学学报, 2020, 39(2): 83-89.

    HU Hailin, REN Yuwen, GUO Di, et al. Research on object defect detection method based on infrared thermal imaging[J]. Journal of Shenyang Ligong University. 2020, 39(2): 83-89.
    [8] Chatterjee K, Tuli S, Pickering S G, et al. A comparison of the pulsed, lock-in and frequency modulated thermography nondestructive evaluation techniques[J]. NDT and E International, 2011, 44(7): 655-667. doi:  10.1016/j.ndteint.2011.06.008
    [9] Moskovchenko A I, Vavilov V P, Bernegger R, et al. Detecting delaminations in semitransparent glass fiber composite by using pulsed infrared thermography[J]. Journal of Nondestructive Evaluation, 2020, 39(3): 69. doi:  10.1007/s10921-020-00717-x
    [10] Marinetti S, Vavilov V. Thermographic detection and characterization of hidden corrosion in metals: General analysis[J]. Corrosion Science, 2009, 52(3): 865-872.
    [11] Subhani S, Suresh B, Ghali VS. Orthonormal projection approach for depth-resolvable subsurface analysis in non-stationary thermal wave imaging[J]. Insight, 2016, 58(1): 42-45. doi:  10.1784/insi.2016.58.1.42
    [12] 张勇, 张金玉, 黄建祥. 基于红外热波检测理论模型的红外热像数据拟合方法[J]. 红外, 2012, 33(4): 38-41. doi:  10.3969/j.issn.1672-8785.2012.04.007

    ZHANG Yong, ZHANG Jinyu, HUANG Jianxiang. Infrared thermal imaging data fitting method based ontheoretical model of infrared thermal wave detection[J]. Infrared, 2012, 33(4): 38-41. doi:  10.3969/j.issn.1672-8785.2012.04.007
    [13] Kaur K, Mulaveesala R. Experimental investigation on noise rejection capabilities of pulse compression favourable frequency-modulated thermal wave imaging[J]. Electronics Letters, 2019, 55(6): 352. doi:  10.1049/el.2018.8047
    [14] Koltsov P P. Comparative analysis of image processing algorithms[J]. Pattern Recognition and Image Analysis, 2012, 22(1): 39. doi:  10.1134/S1054661812010245
    [15] 张德丰. 数字图像处理(MATLAB版)[M]. 北京: 人民邮电出版社, 2015.

    ZHANG Defeng. Digital Image Processing (MATLAB)[M]. Beijing: Posts & Telecom Press Co. . LTD, 2015.
    [16] 陈观应. 基于机器视觉的干电池缺陷并行检测方法研究[D]. 广州: 广东工业大学, 2016.

    CHEN Guanying. Research of Battery Defects Parallel Detecting Methods Based on Machine Vision[D]. Guangdong: Guangdong University of Technology, 2016.
    [17] Arunmuthu K, Kumar P A, Saravanan T. Image processing of radiographs of tube-to-tubesheet weld joints for enhanced detectability of defects[J]. Insight, 2008, 50(6): 298-303. doi:  10.1784/insi.2008.50.6.298
    [18] XUE J H, ZHANG Y J. Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding[J]. Pattern Recognition Letters, 2012, 33(6): 793-797. doi:  10.1016/j.patrec.2012.01.002
    [19] LEE W Y, KIM Y W, KIM S Y. Edge detection based on morphological amoebas[J]. Imaging Science Journal, 2012, 60(3): 172-183. doi:  10.1179/1743131X11Y.0000000013
    [20] 朱光忠, 黄云龙, 余世明. 边缘检测算子在汽车牌照区域检测中的应用[J]. 计算机技术与发展, 2006(3): 161-162. https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ200603056.htm

    ZHU Guangzhong, HUANG Yunlong, YU Shiming. Application of edge detection operators in regiondetection of automobile license plate[J]. Computer Technology and Development, 2006(3): 161-162. https://www.cnki.com.cn/Article/CJFDTOTAL-WJFZ200603056.htm
    [21] 杜雪雪, 殷晨波, 童欣, 等. 红外热成像技术在大型起重机械金属裂纹探伤中的应用[J]. 现代制造工程, 2021(4): 121-125. https://www.cnki.com.cn/Article/CJFDTOTAL-XXGY202104023.htm

    DU Xuexue, YIN Chenbo, TONG Xin, et al. Application of infrared thermal imaging technology in metal crackdetection of large lifting machinery[J]. Modern Manufacturing Engineering, 2021(4): 121-125. https://www.cnki.com.cn/Article/CJFDTOTAL-XXGY202104023.htm
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出版历程
  • 收稿日期:  2021-10-03
  • 修回日期:  2021-11-29
  • 刊出日期:  2022-07-20

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