留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于卤素灯激励的红外热成像裂纹无损检测研究

金玫秀 朱士虎 王通 庄飞飞

金玫秀, 朱士虎, 王通, 庄飞飞. 基于卤素灯激励的红外热成像裂纹无损检测研究[J]. 红外技术, 2022, 44(4): 421-427.
引用本文: 金玫秀, 朱士虎, 王通, 庄飞飞. 基于卤素灯激励的红外热成像裂纹无损检测研究[J]. 红外技术, 2022, 44(4): 421-427.
JIN Meixiu, ZHU Shihu, WANG Tong, ZHUANG Feifei. Nondestructive Crack Testing via Infrared Thermal Imaging Using Halogen Lamp Excitation[J]. Infrared Technology , 2022, 44(4): 421-427.
Citation: JIN Meixiu, ZHU Shihu, WANG Tong, ZHUANG Feifei. Nondestructive Crack Testing via Infrared Thermal Imaging Using Halogen Lamp Excitation[J]. Infrared Technology , 2022, 44(4): 421-427.

基于卤素灯激励的红外热成像裂纹无损检测研究

基金项目: 

江苏省研究生科研与实践创新计划项目 SJCX20_0906

详细信息
    作者简介:

    金玫秀(1999-),女,江苏徐州人,硕士研究生,研究方向:传感器检测与图像处理。E-mail:519392925@qq.com

    通讯作者:

    朱士虎(1970-),男,江苏淮安人,副教授,硕士生导师,研究方向:信息处理与电路设计。E-mail:zshoo@126.com

  • 中图分类号: TB33

Nondestructive Crack Testing via Infrared Thermal Imaging Using Halogen Lamp Excitation

  • 摘要: 钢轨安全状态的监测对保证列车的安全运行至关重要,针对钢轨裂纹的检测,本文阐述了几种不同的裂纹检测技术。重点分析了红外热成像检测技术在钢轨裂纹检测中的应用,该检测技术包括外激励加热、红外图像采集以及图像处理三部分。本文将常用激励方式进行了介绍和对比,详细阐述了卤素灯作为激励在裂纹检测中的应用;其次,搭建了基于卤素灯激励的红外热成像检测实验平台;然后,对采集到的红外图像进行增强处理,并提出改进图像处理算法;最后,本文对该技术未来的应用前景做出展望。
  • 图  1  卤素灯激励红外热成像检测原理图

    Figure  1.  Schematic diagram of halogen lamp excited infrared thermal imaging detection

    图  2  热波图像序列

    Figure  2.  Thermal wave image sequence

    图  3  图像增强结果

    Figure  3.  Image enhancement results

    图  4  图像处理算法的直方图对比

    Figure  4.  Histogram comparison of image processing algorithms

    图  5  Canny边缘检测

    Figure  5.  Canny edge detection

    表  1  红外检测常用激励方式

    Table  1.   Common excitation methods of infrared detection

    Excitation modes Advantages Disadvantages Scope of application
    Ultrasonic excitation It is not limited by the shape of the tested object, has the characteristics of selective heating for closed crack defects, and only produces temperature rise in the crack defect area. It belongs to internal excitation and can detect internal micro cracks[13] The excitation effect is greatly affected by the coupling effect and excitation direction, and the mechanical wave vibration may damage the internal interface of the material Defects such as closed cracks on the surface or sub surface of parts with complex shape
    Laser excitation High energy density, high-intensity energy input to tiny areas Low efficiency and small single excitation area; high energy will lead to thermal stress on the local surface of the material Defect detection of small parts or small areas
    Halogen lamp excitation It can operate at a higher temperature, with larger excitation area and higher efficiency. In addition, the halogen lamp has the advantages of low cost, long service life, good seismic resistance and easy heat control The detection depth is shallow Rapid detection of surface defects in large areas
    Pulse excitation It can quickly obtain the original thermal image, and is not sensitive to uneven illumination. Simultaneously, there is no need for reference points Due to the uneven distribution of heat flow on the surface of the test piece, and greatly affected by the reflectivity of the surface of the test piece and the surrounding environmental noise, it is difficult to accurately judge the defects according to the original thermal image of the surface of the test piece [14] Can be used for composite material with defects inside
    Electromagnetic excitation It is not limited by the shape of the detection image, does not produce mechanical vibration, and will not damage the internal structure of the material Affected by the surface skin effect of induced current, the excitation depth is shallow Surface and subsurface defect detection of with high-conductivity materials
    下载: 导出CSV
  • [1] 郭火明, 王文健, 刘腾飞, 等. 重载铁路钢轨损伤行为分析[J]. 中国机械工程, 2014, 25(2): 267-272. doi:  10.3969/j.issn.1004-132X.2014.02.025

    GUO Huoming, WANG Wenjian, LIU Tengfei, et al. Analysis of Damage Behavior of Heavy-haul Railway Rails[J]. China Mechanical Engineering, 2014, 25(2): 267-272. doi:  10.3969/j.issn.1004-132X.2014.02.025
    [2] 田贵云, 高斌, 高运来, 等. 铁路钢轨缺陷伤损巡检与监测技术综述[J]. 仪器仪表学报, 2016, 37(8): 1763-1780. doi:  10.3969/j.issn.0254-3087.2016.08.008

    TIAN Guiyun, GAO Bin, GAO Yunlai, et al. Review of railway rail defect non-destructive testing and monitoring[J]. Chinese Journal of Scientific Instrument, 2016, 37(8): 1763-1780. doi:  10.3969/j.issn.0254-3087.2016.08.008
    [3] Kim G, Seo M K, Kim Y I, et al. Development of phased array ultrasonic system for detecting rail cracks[J]. Sensors and Actuators A Physical, 2020, 311: 112086. doi:  10.1016/j.sna.2020.112086
    [4] JIANG Yi, WANG Haitao, CHEN Shuai, et al. Visual quantitative detection of rail surface crack based on laser ultrasonic technology[J]. Optik, 2021, 237: 166732. doi:  10.1016/j.ijleo.2021.166732
    [5] 李浩然, 高斌, 张喜源, 等. 电磁热多物理耦合成像检测方法研究[J]. 中国测试, 2020, 46(12): 99-104. doi:  10.11857/j.issn.1674-5124.2020090090

    LI Haoran, GAO Bin, ZHANG Xiyuan, Research on imaging detection method of thermo-electromagnetic multi-physical coupling effects[J]. China Measurement & Test, 2020, 46(12): 99-104. doi:  10.11857/j.issn.1674-5124.2020090090
    [6] YUAN F, YU Y, LIU B, et al. Investigation on velocity effect in pulsed eddy current technique for detection cracks in ferromagnetic material [C]//IEEE Transactions on Magnetics, 2020, 56(9): 3012341.
    [7] 杨理践, 耿浩, 高松巍. 基于多级磁化的高速漏磁检测技术研究[J]. 仪器仪表学报, 2018, 39(6): 148-156. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201806019.htm

    YANG Lijian, GENG Hao, GAO Songwei. Study on high-speed magnetic flux leakage testing technology based on multistage magnetization[J]. Chinese Journal of Scientific Instrument, 2018, 39(6): 148-156. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201806019.htm
    [8] XU Changhang, XIE Jing, CHEN Guoming, et al. An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface[J]. Infrared Physics and Technology, 2014, 67: 266-272. doi:  10.1016/j.infrared.2014.08.002
    [9] 郑凯, 江海军, 陈力. 红外热波无损检测技术的研究现状与进展[J]. 红外技术, 2018, 40(5): 401-411. http://hwjs.nvir.cn/article/id/hwjs201805001

    ZHENG Kai, JIANG Haijun. CHEN Li. Infrared thermography NDT and its development[J]. Infrared Technology, 2018, 40(5): 401-411 http://hwjs.nvir.cn/article/id/hwjs201805001
    [10] YUAN L, ZHU X, HONG K. Detection of material surface cracks by infrared non-destructive testing[C]//2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan), 2020: DOI: 10.1109/PHM-Jinan48558.2020.00114.
    [11] YANG J, WANG W, LIN G, et al. Infrared thermal imaging-based crack detection using deep learning[J]. IEEE Access, 2019, 7: 182060-182077. doi:  10.1109/ACCESS.2019.2958264
    [12] 沈功田, 王尊祥. 红外检测技术的研究与发展现状[J]. 无损检测, 2020, 42(4): 1-9, 14. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC202004003.htm

    SHEN Gongtian, WANG Zunxiang, Progress of infrared testing technology[J]. Nondestructive Testing, 2020, 42(4): 1-9, 14. https://www.cnki.com.cn/Article/CJFDTOTAL-WSJC202004003.htm
    [13] 徐欢, 殷晨波, 李向东, 等. 超声红外检测中裂纹微观界面生热的数值模拟[J]. 南京工业大学学报: 自然科学版, 2019, 41(4): 493-500 doi:  10.3969/j.issn.1671-7627.2019.04.015

    XU Huan, YIN Chenbo, LI Xiangdong, et al. Numerical simulation of the heat generated by the microcosmic interface of cracks in ultrasonic infrared detection[J]. Journal of Nanjing Tech University: Natural Science Edition, 2019, 41(4): 493-500. doi:  10.3969/j.issn.1671-7627.2019.04.015
    [14] CHI Wubu, ZHAO Bo, LIU Tao, et al. Infrared thermal imaging detection of debonding defects in carbon fiber reinforced polymer based on pulsed thermal wave excitation[J]. Thermal Science, 2020, 24(6B): 3887 - 3892.
    [15] 顾桂梅, 贾文晶. 钢轨轨底裂纹红外热波无损检测数值模拟分析[J]. 红外技术, 2018, 40(3): 294-299. http://hwjs.nvir.cn/article/id/hwjs201803016

    GU Guimei, JIA Wenjing. Numerical simulation analysis of infrared thermal wave nondestructive testing of rail bottom crack[J]. Infrared Technology, 2018, 40(3): 294-299. http://hwjs.nvir.cn/article/id/hwjs201803016
    [16] 李玉杰, 李科, 钟安彪, 等. 卤素灯加热红外成像检测技术仿真研究[J]. 激光与红外, 2016, 46(12): 1477-1480. doi:  10.3969/j.issn.1001-5078.2016.12.008

    LI Yujie, LI Ke, ZHONG Anbiao, et al. Simulation research of infrared image detection technology for halogen lamp heating[J]. Laser & Infrared, 2016, 46(12): 1477-1480. doi:  10.3969/j.issn.1001-5078.2016.12.008
    [17] ZHOU Zhenggan, HE Pengfei, ZHAO Hanxue, et al. Detection of skin desoldering defect in Ti-alloy honeycomb structure using lock-in infrared thermography test[J]. Journal of Beijing University of Aeronautics and Astronautics. 2016, 42(9): 1795-1802. https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD202103011.htm
    [18] 黄涛. 基于红外热波技术的钢轨疲劳裂纹深度定量检测研究[D]. 兰州: 兰州交通大学, 2015.

    HUANG Tao. Rail Fatigue Crack Depth Quantitative Detection Based on Infrared Thermal Wave Technology[D]. Lanzhou: Lanzhou Jiaotong University, 2015.
    [19] 李科, 钟安彪, 李玉杰, 等. 基于热风激励的红外成像检测技术研究[J]. 激光与红外, 2016, 46(7): 823-826. doi:  10.3969/j.issn.1001-5078.2016.07.010

    LI Ke, ZHONG Anbiao, LI Yujie. Research on infrared imaging detection based on hot wind heating[J]. Laser & Infrared, 2016, 46(7): 823-826. doi:  10.3969/j.issn.1001-5078.2016.07.010
    [20] 王加, 周永康, 李泽民, 等. 非制冷红外图像降噪算法综述[J]. 红外技术, 2021, 43(6): 557-565. http://hwjs.nvir.cn/article/id/380dcf6e-de3d-4411-ab70-e246d5c8ea27

    WANG Jia, ZHOU Yongkang, LI Zemin, et al. A survey of uncooled infrared image denoising algorithms[J]. Infrared Technology, 2021, 43(6): 557-565 http://hwjs.nvir.cn/article/id/380dcf6e-de3d-4411-ab70-e246d5c8ea27
    [21] 王浩, 张叶, 沈宏海, 等. 图像增强算法综述[J]. 中国光学, 2017, 10(4): 438-448. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGA201704005.htm

    WANG Hao, ZHANG Ye, SHEN Honghai, et al. Review of image enhancement algorithms[J]. Chinese Optics, 2017, 10(4): 438-448. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGA201704005.htm
    [22] 李贤阳, 阳建中, 杨竣辉, 等. 基于改进的直方图均衡化与边缘保持平滑滤波的红外图像增强算法[J]. 计算机应用与软件, 2019, 36(3): 96-103. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903020.htm

    LI Xianyang, YANG Jianzhong, YANG Junhui, et al. Infrared image enhancement algorithm based on improved histogram equalization and edge preserving smooth filtering[J]. Computer Applications and Software, 2019, 36(3): 96-103. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903020.htm
    [23] 陈明, 谭涛. 基于形态学和高斯滤波的图像快速去雾算法[J]. 计算机应用与软件, 2019, 36(12): 209-213. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201912034.htm

    CHEN Ming, TAN Tao. A fast image denoising algorithm based on morphology and Gaussian filter[J]. Computer Applications and Software, 2019, 36(12): 209-213. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201912034.htm
    [24] 宋人杰, 刘超, 王保军. 一种自适应的Canny边缘检测算法[J]. 南京邮电大学学报: 自然科学版, 2018, 38(3): 72-76. https://www.cnki.com.cn/Article/CJFDTOTAL-NJYD201803012.htm

    SONG Renjie, LIU Chao, WANG Baojun. Adaptive Canny edge detection algorithm[J]. Journal of Nanjing University of Posts and Telecommunications: Natural Science Edition. 2018, 38(3): 72-76. https://www.cnki.com.cn/Article/CJFDTOTAL-NJYD201803012.htm
  • 加载中
图(5) / 表(1)
计量
  • 文章访问数:  117
  • HTML全文浏览量:  21
  • PDF下载量:  29
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-08-08
  • 修回日期:  2021-11-19
  • 刊出日期:  2022-04-20

目录

    /

    返回文章
    返回