Inspection Detectability Improvement for Metal Defects Detected by Pulsed Infrared Thermography
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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.
KeywordsPulsed infrared thermography metal defects detectability improvement piecewise fitting differential processing
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).
- X. P. Maldague, Theory and practice of infrared technology for nondestructive testing. Manhattan, USA: Wiley Interscience, 2001: 1–704.Google Scholar
- 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
- 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
- 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
- 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
- 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
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