Advertisement

Russian Journal of Nondestructive Testing

, Volume 55, Issue 5, pp 363–368 | Cite as

Discrete Wavelet Transform based Denoising of TOFD Signals of Austenitic Stainless Steel Weld at Elevated Temperature

  • S. LalithakumariEmail author
  • R. PandianEmail author
ACOUSTIC METHODS
  • 32 Downloads

Abstract—Time of flight Diffraction technique is an advanced ultrasonic NDE methods, adopted in weld integrity testing. During the Time of flight Diffraction (TOFD) inspection of the stainless steel welds at high temperature, grain scattering noise is developed. In Time of flight Diffraction testing, the quality of the signal plays a dominant role in characterizing the defects. Hence, signal denoising is an essential prerequisite for the successful application of Time of flight Diffraction testing. In this work, one austenitic stainless steel weldment was artificially induced with slag defect. At high temperatures, the Time of flight Diffraction testing has been conducted on the weld piece and the resultant signals are applied over the proposed algorithms. Various combinations of wavelets, decomposition levels with different thresholding levels are applied to select an optimum denoising method. The evaluation of wavelet based denoising is achieved by calculating the Signal to Noise Ratio (SNR). Results show that the noises can be suppressed well and Signal to Noise Ratio is improved.

Keywords: symlet coiflet soft thresholding hard thresholding SNR and TOFD 

Notes

ACKNOWLEDGMENTS

The author wishes to thank Dr. B. SheelaRani, Director-Research, Sathyabama Institute of Science and Technology and Dr. B. Venkatraman, Scientists of Indira Gandhi Center for Atomic Research, Kalpakkam, Government of India for the technical support provided by them.

REFERENCES

  1. 1.
    Subbaratnam, R., Abraham, S.T., Menaka, M., Venkatraman, B., and Raj, B., Time of flight diffraction testing of austenitic stainless steel weldments at elevated temperatures, Mater. Eval., 2008, vol. 66, pp. 332–337.Google Scholar
  2. 2.
    Pardikar, R.J., Sony Baby, and Balasubramanian, T., Ultrasonic study for the flaw detectability in ferritic butt welds at high temperatures, Natl. Semin. ISNT, Indian Nondestr. Test., 2002.Google Scholar
  3. 3.
    Verkooijen, J. and McLay, A., Advances with the time of flight diffraction technique, MAT & TEST, 2005, vol. 5.Google Scholar
  4. 4.
    Drai, R., Sellidj, F., Khelil, M., and Benchaala, A., Elaboration of some signal processing algorithms in ultrasonic techniques: appl to materials NDT, Ultrasonics, Elsevier, 2000, vol. 38, pp. 503–507.CrossRefGoogle Scholar
  5. 5.
    Qi Tian and Bilgutay, N.M., Statistical analysis of split spectrum processing for multiple target detection, IEEE Trans. Ultrason. Eng., 1998, vol. 45, no. 1, pp. 251–256.CrossRefGoogle Scholar
  6. 6.
    Legendre, S., Goyette, J., and Massicotte, D., Ultrasonic NDE of composite material structures using wavelet coefficients, NDT & E Int., 2001, vol. 34, pp. 31–37.CrossRefGoogle Scholar
  7. 7.
    Pandian, R. and Vigneswaran, T., Dr., Adaptive wavelet packet basis selection for zero tree image coding, Int. J. Signal Imaging Syst. Eng., 2016, vol. 9, no. 6.Google Scholar
  8. 8.
    Udo Schlengermann, The European TOFD Standard Draft ENV 583-6 and The British TOFD Standard BS 7706, 1993.Google Scholar
  9. 9.
    Shyamal Mondal and Sattar, T., Dr., An overview of ToFD method and its mathematical model, NDT.net, April, 2000.Google Scholar
  10. 10.
    Hong-Wei, M.A. and Wang Bin, Application of wavelet rransform to signal de-noising in ultrasonic testing, NDT, 2004, vol. 26, pp. 68–71.Google Scholar
  11. 11.
    Zhou Wei, Advanced Technology of Wavelet Analysis Based on MATLAB, Xi’an: Xi Dian Univ., 2006.Google Scholar
  12. 12.
    Wang, Y., Chen, S.J., and Liu, S.J., Best wavelet basis for wavelet transforms in acoustic emission signals of concrete damage process, Russ. J. Nondestr. Test., 2016, vol. 52, no. 3, pp. 125–133.CrossRefGoogle Scholar
  13. 13.
    Yong Han and Guo-Guang Chen, Maximum kurtosis principle for the parameter selection of Gabor wavelet and its application to ultrasonic signal processing, Russ. J. Nondestr. Test., 2009, vol. 45, no. 6, pp. 436–443.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.Department of EIE, Sathyabama Institute of Science and TechnologyChennaiIndia

Personalised recommendations