Article Article
Quantitative Defect Characterization for Passive Thermography Application

This article proposes a new method for characterizing subsurface defects in high temperature wall by means of passive thermography. The method enables a fast and reliable quantitative defect characterization. Ten informative parameters have been proposed for this purpose based on temperature behavior on the outer surface wall of a petrochemical boiler. Multilayer perceptron neural network has been trained to characterize quantitatively three defect properties: thickness, length, and width of the defect. From an extensive testing of the method, it has been shown that the method is able to characterize the defect properties, which actually we believe is a new approach in passive thermography application.

References
  1. X. P. V. Maldague. Theory and Practice of Infrared Technology for Nondestructive Testing. NewYork: John Wiley & Sons, Inc. (2001).
  2. R. Heriansyah and S. A. R. Abu-Bakar. NDT&E International 42(8):729–740 (2009).
  3. M. B. Saintey and D. P. Almond. J. Phys. D: Appl. Phys. 28:2539–2546 (1995).
  4. M. B. Saintey and D. P. Almond. NDT&E International 30(5):291–295 (1997).
  5. Y. A. Plotnikov. Proceedings of the 25th Annual Review of Progress in Quantitative Nondestructive Evaluation Conference. Snowbird, Utah: Jul. 19–24, 1998; pp. 873–880.
  6. Y. A. Plotnikov and W. P. Winfree. In D. O. Thompson and D. E. Chimenti (eds.). Review of Progress in QNDE. Plenum Press, NY, USA. 19. AIP (2000).
  7. C. Maierhofer, H. Wiggenhauser, A. Brink, and M. Röllig. Infrared Physics and Technology 46:173–180 (2004).
  8. R. Chowdhury. M.Sc. Thesis, Wayne State University, USA (2004).
  9. M. Krishnapillai, R. Jones, I. H. Marshall, M. Bannister, and N. Rajic. Composite Structures. 75:241–249 (2006).
  10. D. P. Almond and S. K. Lau. J. Phys. D: Appl. Phys. 27:1063–1069 (1994).
  11. M. B. Saintey and D. P. Almond. J. Phys. D: Appl. Phys. 28:2539–2546 (1995).
  12. D. R. Croft and D. G. Lilley. Heat Transfer Calculations Using Finite Difference Equations. Applied Science Publishers, Ltd, London, UK. (1977).
  13. L. Fausett. Fundamentals of Neural Network: Architectures, Algorithms, and Applications. Prentice- Hall, Inc., Upper Saddle River, NJ, USA. (1994).
  14. M. T. Hagan, H. B. Demuth, and M. Beale. Neural Network Design. Boston, MA: PWS Publishing Company (1996).
  15. X. Maldague, Y. Largouët, and J. P. Couturier. Rev. Gén. Therm. 37:704–717 (1998).
  16. M. Maj, W. Oliferuk, and O. Wysocka. Proceedings of 9th International Conference on Quantitative Infrared Thermography (QIRT2008). Krakow, Poland (2008).
  17. J. S. R. Jang, C. T. Sun, and E. Mizutani. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ: Prentice-Hall, Inc. (1997).
  18. A. Darabi. Ph.D. Thesis, Université Laval, Canada (2000).
  19. X. P. V. Maldague. Theory and Practice of Infrared Technology for Nondestructive Testing. JohnWiley & Sons, Inc., New York, USA. (2001).
  20. N. Ludwig and P. Teruzzi. Infrared Physics and Technology 43:297–301 (2002).
  21. G. Manduchi, S. Marinetti, P. Bison, and E. Grinzato. Neural Computing and Applications 6:148–157 (1997).
Metrics
Usage Shares
Total Views
102 Page Views
Total Shares
0 Tweets
102
0 PDF Downloads
0
0 Facebook Shares
Total Usage
102