Journal of Nondestructive Evaluation

, Volume 31, Issue 4, pp 383–392 | Cite as

Application of the Naive Bayes Classifier to Defect Characterization Using Active Thermography

  • Sebastian Dudzik


In the paper, a method for characterization of defects using active thermography and naive Bayes classifier is presented. Experimental investigations of the test sample are conducted with the stepped heating method. Two classifiers—with the parametric and non-parametric estimation of the features density distributions—are considered. Simulations are performed for three datasets representing three phases of the heat process occurring in the investigated sample.


Active thermography Nondestructive evaluation Naive Bayes classification Defect characterization 


  1. 1.
    Minkina, W., Dudzik, S.: Infrared Thermography—Errors and Uncertainties. Wiley, Chichester (2009) Google Scholar
  2. 2.
    Dudzik, S.: Investigations of a heat exchanger using infrared thermography and artificial neural networks. Sens. Actuators A Phys. 166, 149–156 (2011) CrossRefGoogle Scholar
  3. 3.
    Gryś, S., Minkina, W.: Filtered thermal contrast—error analysis. In: Proc. 10th International Conference on Quantitative Infrared Thermography (QIRT’2010), 27–30 July 2010, Quebec, Canada, pp. 495–502 (2010) Google Scholar
  4. 4.
    Maldague, X.P.: Theory and Practice of Infrared Technology for Nondestructive Testing. Wiley, New York (2001) Google Scholar
  5. 5.
    Vallerand, S., Maldague, X.P.: Defect characterization in pulsed thermography: a statistical method compared with Kohonen and Perceptron neural networks. NDT E Int. 33, 307–315 (2000) CrossRefGoogle Scholar
  6. 6.
    Breitenstein, O., Warta, W., Langenkamp, M.: Lock-in Thermography—Basics and Applications to Functional Diagnostics of Electronic Components. Springer, Berlin (2010) Google Scholar
  7. 7.
    Dudzik, S.: A simple method for defect area detection using active thermography. Opto-Electron. Rev. 17(4), 338–344 (2009) CrossRefGoogle Scholar
  8. 8.
    Maldague, X.P., Galmiche, F., Ziadi, A.: Advances in pulsed phase thermography. Infrared Phys. Technol. 43, 175–181 (2002) CrossRefGoogle Scholar
  9. 9.
    Avdelidis, N.P., Hawtin, B.C., Almond, D.P.: Transient thermography in the assessment of defects of aircraft composites. NDT E Int. 36, 433–439 (2003) CrossRefGoogle Scholar
  10. 10.
    Gleiter, A., Spiessberger, C., Busse, G.: Phase angle thermography for depth resolved characterization. In: Proc. 9th International Conference on Quantitative Infrared Thermography (QiRT), 2–5 July 2008, Cracow, Poland, pp. 435–441 (2008) Google Scholar
  11. 11.
    Zöcke, C., Langmeier, A., Stößbel, R., Arnold, W.: Reconstruction of the defect shape from lock-in thermography phase images. Quant. Infrared Thermogr. J. 6, 63–78 (2009) CrossRefGoogle Scholar
  12. 12.
    Alifanow, OM: Inverse Heat Transfer Problems. Springer, Berlin (1994) CrossRefGoogle Scholar
  13. 13.
    Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis. Chapman & Hall/CRC, London (2004) MATHGoogle Scholar
  14. 14.
    Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Prentice Hall, New York (1994) MATHGoogle Scholar
  15. 15.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990) MATHGoogle Scholar
  16. 16.
    van der Heijden, F., Duin, R., de Ridder, D., Tax, D.M.J.: Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. Wiley, New York (2004) MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Electrical EngineeringCzestochowa University of TechnologyCzestochowaPoland

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