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.

Thermal Imaging Technology for Metal Structure Defects of Lifting Machinery

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  • Received Date: October 02, 2021
  • Revised Date: November 28, 2021
  • The identification of cracks in the metal structure of lifting machinery is a new direction for infrared thermal imaging detection technology. In this study, the detection principle of pulsed infrared thermal imaging was introduced, and a pulsed infrared thermal imaging detection system was designed; the experimental platform was constructed on the basis of these. Median filtering and Butterworth low-pass filtering were used to process the images collected in the experiment. To address the problem of blurring at the edges of the defects after processing the above algorithms, a Butterworth band-pass filtering algorithm was proposed. After threshold segmentation and edge detection, the defect contour feature was extracted, and using the conversion relationship between the actual size of the flat specimen and the contour feature image pixels, the recognition accuracy of the crack defect was finally obtained. The comparison and verification demonstrated that the pulsed infrared thermal imaging technology can meet the requirements of crack defect detection in crane metal structures.
  • [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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