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Reseach Article

De-noising of ToFD Signals from Austenitic Stainless Steel Welds using Stationary Wavelet Transform

by Angam Praveen, K Vijayarekha, B Venkatraman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 9
Year of Publication: 2013
Authors: Angam Praveen, K Vijayarekha, B Venkatraman
10.5120/11115-6076

Angam Praveen, K Vijayarekha, B Venkatraman . De-noising of ToFD Signals from Austenitic Stainless Steel Welds using Stationary Wavelet Transform. International Journal of Computer Applications. 66, 9 ( March 2013), 34-38. DOI=10.5120/11115-6076

@article{ 10.5120/11115-6076,
author = { Angam Praveen, K Vijayarekha, B Venkatraman },
title = { De-noising of ToFD Signals from Austenitic Stainless Steel Welds using Stationary Wavelet Transform },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 9 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number9/11115-6076/ },
doi = { 10.5120/11115-6076 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:21:57.324191+05:30
%A Angam Praveen
%A K Vijayarekha
%A B Venkatraman
%T De-noising of ToFD Signals from Austenitic Stainless Steel Welds using Stationary Wavelet Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 9
%P 34-38
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Inspection of structural materials is an important aspect in many industrial applications. Ultrasonic Non-Destructive Testing (NDT) is one of the most widely used techniques in this aspect. Conventional pulse echo and through transmission methods in Ultrasonic Testing (UT) are not reliable in detection of defects with random orientation due to its reflection principle. Time of Flight Diffraction (ToFD) method has gained more popularity in this area in the recent past. It uses diffraction energy and has more advantages in detection, sizing and positioning of the defects irrespective of type, orientation and characteristics. This technique is hampered by several of the unwanted signals arising due to ultrasonic interaction with the material grains. This noise affects the visibility of a defect signal especially when the defect size is small. Many signal processing techniques such as split spectrum processing, wavelet transform and correlation etc are available for de-noising of signals. Among this Discrete Wavelet Transform (DWT) is widely used due to its added advantage of time-frequency information simultaneously. Stationary Wavelet Transform (SWT) is a form of DWT with the main difference that it is translation invariant unlike DWT. In this paper SWT has been used for de-noising of the real time ultrasonic ToFD signals from austenitic stainless steel welds and its performance is compared with that of the DWT.

References
  1. R. Drai , F. Sellidj, M. Khelil, A. Benchaala, 'Elaboration of some signal processing algorithms in ultrasonic techniques: application to materials NDT', Ultrasonics, Algeria, 2000, vol no. 38, pp. 503-507.
  2. T. Bouden,S. Dib, K. Aissaous and M. Grimes, 'Signal Processing Methods for Materials Defects Detection', IEEE Ultrasonic Symposium, Jijel, Algeria, 2009
  3. L. Ericsson , T. Stepinski, 'Algorithms for suppressing ultrasonic backscattering from material structure', Ultrasonics, ELSEVIER, Sweden, 2002, vol. no. 40, pp. 733–734
  4. V. L. Newhouse, N. M. Bilgautay, J. Saniie, and E. S. Furgason, "Flaw-to-grain echo enhancement by split-spectrum processing", Ultrasonics, March 1982, pp. 59-68
  5. Liu Zhenqing, Ta Dean and Liu Xiao, "Phase deviation based Split Spectrum Processing for Ultrasonic testing in coarse grained materials", Roma 2000 15th WCNDT, 2000
  6. Scott E Bailey, 'Implementing SSP in TMS320c26', Texas Instruments, 1997.
  7. Zhang Ze, Ren Yueqing, 'Time–frequency Analysis of Echoes Signal in Ultrasonic Testing of Adhesion Based on Short-time Fourier Transformation', International Conference on Measuring Technology and Mechatronics Automation, Beijing, China, 2010, pp. 1023-1026.
  8. Temple J. A. G. , "Time-of-flight inspection: theory", Nulcear Energy, 1983, 22, No. 5, Oct. , pp. 335-348.
  9. Charles Worth J. P. and Temple J. A. G. , "Engineering application of ultrasonic time of flight diffraction", 2nd ed. Research Studies press Ltd. 2001.
  10. Verkooijen J. , "TOFD used to replace radiography", Insight Vol. 37. No. 6, June 1995, pp. 433-435.
  11. Tianlu Chen, Peiwen Que, Oi Zhang, and Qingkun Liu, "Ultrasonic Nondestructive Testing Accurate Sizingand Locating Technique Based on Time-of-Flight-Diffraction Method", Russian Journal of Nondestructive Testing, 2005, Shanghai, China, Vol. 41, No. 9, 57-68, pp. 594–601.
  12. Silk M. G. , "An evaluation of the performance of the TOFD techniques as a means of sizing flaws, with particular reference to flaws with curved profiles", Insight vol. 38, No. 4, April, 1996.
  13. Sony Baby, Balasubramaniam T. , Pardikar R. J. , Palaniyappan M. and Subbaratnam R. , "Time of flight diffraction(ToFD) technique for accurate sizing if cracks embedded in sub-cladding", Insight, Vol. 45, No. 9, September, 2003.
  14. M. Riahi and M. R. Abolhasany, "Substitution of the Time-of-Flight Diffraction Technique for Nondestructive Testing of Welds and Thick Layers of Steel: A Comparative Investigation", Russian Journal of Nondestructive Testing, Tehran, Iran Vol. 42, No. 12, pp. 794–801.
  15. Ogilvy J. A. and Temple J. A. G. , "Diffraction of elastic waves by cracks: Application of time of flight inspection", Ultrasonics, Nov. 1983, pp. 259-268.
  16. . C. Lázaro, J. L. San Emeterio and A. Ramos, "Noise Reduction in Ultrasonic NDT using Discrete Wavelet Transform Processing", IEEE Ultrasonic Symposium, Spain, 2002, pp. 777-780
  17. Vaclav Matz, Radislav Smid, Stanislav starman, Marcel Kreidi, "Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing", Ultrasonics, ELSEVIER, Czech Republic, 2009, Vol. 49, pp. 752-759.
  18. M. Kreidl, P. Houfek, "Reducing Ultrasonic Signal Noise by Algorithms based on Wavelet Thresholding", Acta Polytechnica, 2002, Vol. 42, No. 2.
  19. Yuan Chen, Hongwei Ma, "Application of wavelet analysis to signal de-noising in ultrasonic testing of welding flaws", 17th World Conference on Nondestructive Testing, Shanghai, China, 2008.
  20. Erdal Oruklu and Jafar Saniie, "Ultrasonic flaw detection using Discrete Wavelet Transform for NDT applications", IEEE Ultrasonic Symposium, Chicago, 2004, pp. 1054-1057.
  21. Zhen-zhu Yu, Chong Zhao, Wei Ma, "Application of the Wavelet Transform in Ultrasonic Echo Signal Processing", IEEE Computer Society, Beijing, China, 2009, pp. 576-579.
  22. Xianfeng Fan, Ming J Zuo, and Xiaodong Wang. Application of stationary wavelet transforms to ultrasonic crack detection, IEEE explore, Ottawa. 2006, pp. 1432-1436
  23. Chiseung Park , Youngseock Lee and Seon Jin Kim, 'Threshold-Varying Method of Stationary Wavelet Denoising for Ultrasonic Speckle Reduction', Materials Science Forum, Switzerland, 2004, Vols. 449-452, pp. 1153-1156.
  24. Prasanna Karpur and Orlando J. Canelones, "Split spectrum processing: a new filtering approach for improved signal-to-noise ratio enhancement of ultrasonic signals", Ultrasonics, USA, 1992, Vol. 30, No. 6, pp. 351-357.
Index Terms

Computer Science
Information Sciences

Keywords

Stationary Wavelet Transform Discrete Wavelet Transform De-noising Signal-to-Noise ratio