Structural Damage Evaluation of Carbon Steel Based on Dynamic Scanning Eddy Current Infrared Thermography
Tu Yanxin1, Mei Hongwei1, Liu Lishuai2, Shen Zekai1, Wang Liming1
1. Guangdong Engineering Technology Research Centre of Power Equipment Reliability in Complicated Coastal Environments Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China; 2. Key Laboratory of Pressure Systems and Safety of Ministry of Education East China University of Science and Technology Shanghai 200237 China
Abstract：As a common metal, carbon steel has been utilized widely in the power industry. Nevertheless, faults such as fatigue, cracks, and delamination are unavoidable in carbon steel during the process of manufacture and use, and these defects can even lead to the fracture failure of equipment. Detecting structural defects in carbon steel in a timely manner is crucial for ensuring the safety of power industrial systems. Compared to traditional non-destructive testing techniques, dynamic scanning eddy current infrared thermography (DSECT) has application potential for the inspection of carbon steel materials in the power industry. For the practical needs of structural damage detection, the principle of DSECT and related algorithms are explored, as well as the applicability of the technology for detecting faults in carbon steel. Firstly, the thermal wave propagation model of DSECT is derived based on Fourier heat conduction. Secondly, an algorithm for thermographic signal reconstruction (TSR) is proposed, in which the thermal wave signal is fitted in the logarithmic coordinate system to eliminate the noise of the thermal wave signal. Thirdly, an experimental study is carried out to verify the effectiveness of the TSR algorithm by detecting artificial defective carbon steel specimens using DSECT. Finally, the frequency of the minimum phase is extracted by the fast Fourier transform for the purpose of quantitatively detecting the defect depth, and the defect depth is estimated quantitatively by using the linear relationship between the defect depth and the inverse of the square root of the frequency of the minimum phase. In a word, DSCET overcomes the drawbacks of traditional non-destructive testing, and the related algorithm enhances imaging and defect depth measurement. Experimental test results on artificial defect samples show that the coil arrival order matches the peak temperature arrival order. The temperature at the detection point remains largely constant while the coil does not reach the detection point, increases rapidly as the coil leaves the detection point, and peaks as the coil leaves the effective heating region. The TSR-based thermogram sequence algorithm is used to reconstruct the original experimental data and the results show that, the thermophysical change process in the reconstructed thermogram sequence conforms to the thermal wave propagation law; the reconstruction algorithm can eliminate coils from the field of view and achieve uniform heating of the tested specimen. In addition, the minimum phase in the reconstructed thermogram is linearly proportional to the frequency, and the frequency domain properties are correlated with defect depth. The defect depth can be fitted with the square root inverse of the frequency of the minimum phase as a linear relationship, and the goodness of fit is 0.991 2. Results show that the DSECT approach and related algorithms proposed can depict faults in two-dimensional imaging more simply and clearly, and also offer quantitative measures of defect depth. The key conclusions are as follows. (1)DSECT's dynamic coil scanning allows for a single, wide-range specimen detection, overcoming the single-coil excitation method's limitations. (2)By using TSR algorithm, a smooth reconstruction of the thermal wave signal can be accomplished and the thermal wave signal noise is eliminated. (3)The TSR-based reconstruction method of the thermogram sequence eliminates the coils in the thermogram field of view and achieves uniform heating of the specimen in the thermogram. (4)Minimum phase is the effective feature for quantitative detection, and the defect depth can be quantitatively estimated using the linear relationship between the defect depth and the inverse of the square root of the minimum phase frequency.
涂彦昕, 梅红伟, 刘立帅, 沈泽锴, 王黎明. 基于动态扫描涡流热成像技术的碳钢结构损伤检测[J]. 电工技术学报, 2023, 38(11): 2999-3008.
Tu Yanxin, Mei Hongwei, Liu Lishuai, Shen Zekai, Wang Liming. Structural Damage Evaluation of Carbon Steel Based on Dynamic Scanning Eddy Current Infrared Thermography. Transactions of China Electrotechnical Society, 2023, 38(11): 2999-3008.
[1] 董聪. 现代结构系统可靠性理论及其应用[M]. 北京: 科学出版社, 2001. [2] Wang Wenzhi, Huang Songling, Zhao Wei.A 3-D imaging method for local shallow defects on the surface of ferromagnetic materials[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-9. [3] 刘素贞, 杨圣哲, 袁路航, 等. 含微裂纹铝材的电磁超声Lamb波混频非线性检测及量化分析[J]. 电工技术学报, 2021, 36(17): 3639-3648. Liu Suzhen, Yang Shengzhe, Yuan Luhang, et al.Nonlinear detection and quantitative analysis of electromagnetic ultrasonic Lamb wave mixing for aluminum with micro crack[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3639-3648. [4] Lu Wenquan, Zhang Shuguang, Zhang Wei, et al.Imaging of structure evolution in solidifying Al-Bi immiscible alloys by synchrotron radiography[J]. Journal of Materials Science & Technology, 2016, 32(12): 1321-1325. [5] 段志荣, 解社娟, 李丽娟, 等. 基于磁力传动式阵列涡流探头的管道缺陷检测[J]. 电工技术学报, 2020, 35(22): 4627-4635. Duan Zhirong, Xie Shejuan, Li Lijuan, et al.Detection of defects in pipeline structures based on magnetic transmission eddy current array probe[J]. Transactions of China Electrotechnical Society, 2020, 35(22): 4627-4635. [6] 黄松岭, 彭丽莎, 赵伟, 等. 缺陷漏磁成像技术综述[J]. 电工技术学报, 2016, 31(20): 55-63. Huang Songling, Peng Lisha, Zhao Wei, et al.Overview of defect magnetic flux leakage imaging technology[J]. Transactions of China Electrotechnical Society, 2016, 31(20): 55-63. [7] 刘素贞, 田钰霖, 张闯, 等. 铝合金拉伸塑性变形的非线性电磁超声检测[J]. 电工技术学报, 2020, 35(15): 3153-3160. Liu Suzhen, Tian Yulin, Zhang Chuang, et al.Nonlinear electromagnetic acoustic detection of aluminum alloys with tensile plastic deformation[J]. Transactions of China Electrotechnical Society, 2020, 35(15): 3153-3160. [8] 张元良, 张洪潮, 赵嘉旭, 等. 高端机械装备再制造无损检测综述[J]. 机械工程学报, 2013, 49(7): 80-90. Zhang Yuanliang, Zhang Hongchao, Zhao Jiaxu, et al.Review of non-destructive testing for remanufacturing of high-end equipment[J]. Journal of Mechanical Engineering, 2013, 49(7): 80-90. [9] He Yunze, Tian Guiyun, Pan Mengchun, et al.Eddy current pulsed phase thermography and feature extraction[J]. Applied Physics Letters, 2013, 103(8): 084104. [10] Tu Yanxin, Guo Chenjun, Wang Liming, et al.Eddy Current pulsed thermography for steel corrosion characterization[C]//2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Beijing, China, 2020: 1-4. [11] 严俨, 刘怡, 朱俊臻, 等. 基于电涡流脉冲热成像偏度特征的钢轨自然裂纹量化评估[J]. 机械工程学报, 2021, 57(18): 75-85. Yan Yan, Liu Yi, Zhu Junzhen, et al.Quantification of rolling contact fatigue cracks in rails based on eddy current pulsed thermography with skewness feature[J]. Journal of Mechanical Engineering, 2021, 57(18): 75-85. [12] Yi Q, Tian G Y, Malekmohammadi H, et al.New features for delamination depth evaluation in carbon fiber reinforced plastic materials using eddy current pulse-compression thermography[J]. NDT & E International, 2019, 102: 264-273. [13] Oswald-Tranta B, Sorger M.Scanning pulse phase thermography with line heating[J]. Quantitative InfraRed Thermography Journal, 2012, 9(2): 103-122. [14] Usamentiaga R, Mokhtari Y, Ibarra-Castanedo C, et al.Automated dynamic inspection using active infrared thermography[J]. IEEE Transactions on Industrial Informatics, 2018, 14(12): 5648-5657. [15] He Yunze, Yang Ruizhen, Wu Xuan, et al.Dynamic scanning electromagnetic infrared thermographic analysis based on blind source separation for industrial metallic damage evaluation[J]. IEEE Transactions on Industrial Informatics, 2018, 14(12): 5610-5619. [16] Wu Jianbo, Zhu Junzhen, Xu Zhaoyuan, et al.A DC-biased scanning induction thermographic system for characterizing surface cracks in ferromagnetic components[J]. IEEE/ASME Transactions on Mechatronics, 2021, 26(5): 2782-2790. [17] Vrana J, Goldammer M, Baumann J, et al.Mechanisms and models for crack detection with induction thermography[J]. AIP Conference Proceedings, 2008, 975(1): 475-482. [18] Mandelis A, Nicolaides L, Chen Yan.Structure and the reflectionless/refractionless nature of parabolic diffusion-wave fields[J]. Physical Review Letters, 2001, 87(2): 020801. [19] 王黎明, 刘立帅, 梅红伟, 等. 基于脉冲红外热波技术的支柱瓷绝缘子无损检测方法[J]. 中国电机工程学报, 2017, 37(24): 7359-7366, 7450. Wang Liming, Liu Lishuai, Mei Hongwei, et al.Nondestructive testing method of post porcelain insulator based on pulsed infrared thermal wave technology[J]. Proceedings of the CSEE, 2017, 37(24): 7359-7366, 7450. [20] He Min, Zheng Wenpei, Li Weijun, et al.Research on temperature distribution law around crack using the moving mode of eddy current thermography[J]. Infrared Physics & Technology, 2019, 102: 102993. [21] 涂彦昕, 刘立帅, 郭晨鋆, 等. 基于电磁感应热成像技术的复合绝缘子内部导通性缺陷检测方法[J]. 中国电机工程学报, 2021, 41(7): 2565-2576. Tu Yanxin, Liu Lishuai, Guo Chenjun, et al.Active electromagnetic induction thermography for conductive defects detection on composite insulators[J]. Proceedings of the CSEE, 2021, 41(7): 2565-2576. [22] 郭晨鋆, 涂彦昕, 刘立帅, 等. 复合支柱绝缘子界面热波模型及其界面缺陷无损检测方法[J]. 中国电机工程学报, 2021, 41(20): 7160-7169. Guo Chenjun, Tu Yanxin, Liu Lishuai, et al.Interfacial thermal wave model of composite post insulator and its nondestructive detection method on interfacial defects[J]. Proceedings of the CSEE, 2021, 41(20): 7160-7169. [23] Liu Lishuai, Guo Chenjun, Tu Yanxin, et al.Differential evolution fitting-based optical step-phase thermography for micrometer thickness measurement of atmospheric corrosion layer[J]. IEEE Transactions on Industrial Informatics, 2020, 16(8): 5213-5222.
2023, Vol. 38 
