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Photonic Sensors

, Volume 9, Issue 2, pp 142–150 | Cite as

Inspection Detectability Improvement for Metal Defects Detected by Pulsed Infrared Thermography

  • Zhengwei YangEmail author
  • Guangjie Kou
  • Yin Li
  • Gan Tian
  • Wei Zhang
  • Jietang Zhu
Open Access
Regular
  • 111 Downloads

Abstract

Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thermography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.

Keywords

Pulsed infrared thermography metal defects detectability improvement piecewise fitting differential processing 

Notes

Acknowledgment

This work is supported by the National Natural Science Foundation of China (Grant Nos. 51575516 and 51605481) and Xi’an Science and Technology Project (Grant No. 2017089CG/RC052 HJKC001).

References

  1. [1]
    T. Sakagami, Y. Izumi, and S. Kubo, “Application of infrared thermography to structural integrity evaluation of steel bridges,” Journal of Modern Optics, 2010, 57(18): 1738–1746.ADSCrossRefzbMATHGoogle Scholar
  2. [2]
    V. P. Vavilov, E. Grinzato, P. G. Bison, S. Marinetti, and M. J. Bales, “Surface transient temperature inversion for hidden corrosion characterization: theory and applications,” International Journal of Heat and Mass Transfer, 1996, 39(2): 355–371.CrossRefGoogle Scholar
  3. [3]
    E. Grinzato, V. Vavilov, P. G. Bison, and S. Marinetti, “Hidden corrosion detection in thick metallic components by transient IR thermography,” Infrared Physics &Technology, 2007, 49: 234–238.ADSCrossRefGoogle Scholar
  4. [4]
    X. P. Maldague, Theory and practice of infrared technology for nondestructive testing. Manhattan, USA: Wiley Interscience, 2001: 1–704.Google Scholar
  5. [5]
    M. Pilla, M. Klein, X. Maldague, and A. Salerno, “New absolute contrast for pulsed thermography,” Quantitative Infrared Thermography Journal, 2002: 004: 53–58.Google Scholar
  6. [6]
    H. D. Benitez, X. Maldague, C. I. Castanedo, H. L. Correa, A. Bendada, and E. F. C. Bravo, “Modified differential absolute contrast using thermal quadrupoles for the nondestructive testing of finite thickness specimens by infrared thermography,” in Proceeding of 2006 Canadian Conference on Electrical and Computer Engineering, Ottawa, Ont., Canada, 2007, pp. 1039–1042.Google Scholar
  7. [7]
    X. W. Guo, W. Shao, G. P. Guo, and Y. Liu, “Image processing algorithms for uneven heating in infrared thermgorahic NDT,” Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(11): 1204–1207.Google Scholar
  8. [8]
    R. Usamentiaga, P. Venegas, J. Guerediaga, L. Vega, and I. Lopez, “Automatic detection of impact damage in carbon fiber composites using active thermography,” Infrared Physics &Technology, 2013, 58(5): 36–46.ADSCrossRefGoogle Scholar
  9. [9]
    Q. J. Tang, C. W. Bu, Y. L. Liu, L. T. Qi, and Z. Y. Yu, “A new signal processing algorithm of pulsed infrared thermography,” Infrared Physics &Technology, 2015, 68: 173–178.ADSCrossRefGoogle Scholar
  10. [10]
    D. D. Wang, W. Zhang, Z. W. Yang, and G. Tian, “Image enhancement of thermal waving inspection based on independence component analysis,” Science Technology and Engineering, 2013, 13(2): 512–515.Google Scholar
  11. [11]
    T. Liu, W. Zhang, and S. Z. Yan, “A novel image enhancement algorithm based on stationary wavelet transform for infrared thermography to the de-bonding defect in solid rocket motors,” Mechanical Systems and Signal Processing, 2015, 62–63: 366–380.CrossRefGoogle Scholar
  12. [12]
    W. Zhang, F. H. Cai, B. M. Ma, and Z. W. Yang, “Quantitative analysis of infrared thermal image defect based on mathematical morphology,” Nondestructive Testing, 2009, 31(8): 596–599.Google Scholar

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://doi.org/creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Zhengwei Yang
    • 1
    • 2
    Email author
  • Guangjie Kou
    • 1
  • Yin Li
    • 1
  • Gan Tian
    • 1
  • Wei Zhang
    • 1
  • Jietang Zhu
    • 1
  1. 1.Rocket Force University of EngineeringXi’anChina
  2. 2.School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina

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