The Discrete Wavelet Transform and Its Application for Noise Removal in Localized Corrosion Measurements

The present work discusses the problem of induced external electrical noise as well as its removal from the electrical potential obtained from Scanning Vibrating Electrode Technique (SVET) in the pitting corrosion process of aluminum alloy A96061 in 3.5% NaCl. An accessible and efficient solution of this problem is presented with the use of virtual instrumentation (VI), embedded systems, and the discrete wavelet transform (DWT). The DWT is a computational algorithm for digital processing that allows obtaining electrical noise with Signal to Noise Ratio (SNR) superior to those obtained with Lock-In Amplifier equipment. The results show that DWT and the threshold method are efficient and powerful alternatives to carry out electrical measurements of potential signals from localized corrosion processes measured by SVET.

[1]  Hu Meng,et al.  Virtual Instrument for Determining Rate Constant of Second-Order Reaction by pX Based on LabVIEW 8.0 , 2009, Journal of automated methods & management in chemistry.

[2]  K. Khoshelham,et al.  EVALUATION OF WAVELET DENOISING METHODS FOR SMALL-SCALE JOINT ROUGHNESS ESTIMATION USING TERRESTRIAL LASER SCANNING , 2015 .

[3]  Bingsheng Wu,et al.  Wavelet Denoising and Its Implementation in LabVIEW , 2009, 2009 2nd International Congress on Image and Signal Processing.

[4]  Benjamin Valdez,et al.  LabVIEW 2010 Computer Vision Platform Based Virtual Instrument and Its Application for Pitting Corrosion Study , 2013, Journal of analytical methods in chemistry.

[5]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[6]  M. Jafarian,et al.  Application of wavelet entropy in analysis of electrochemical noise for corrosion type identification , 2014 .

[7]  Julian A. Wharton,et al.  Wavelet analysis of electrochemical noise measurements during corrosion of austenitic and superduplex stainless steels in chloride media , 2003 .

[8]  Luis E. Suarez,et al.  Aplicaciones De La Transformada Ondícula (“Wavelet”) En Ingeniería Estructural. , 2007 .

[9]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[10]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[11]  R. Akid,et al.  Scanning vibrating reference electrode technique: a calibration study to evaluate the optimum operating parameters for maximum signal detection of point source activity , 2004 .

[12]  H. Isaacs,et al.  Current and Potential Transients during Localized Corrosion of Stainless Steel , 1985 .

[13]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[14]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[15]  Hong Men,et al.  Wavelet Analysis and Its Application in the Field of Microbial Corrosion , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[16]  Qi-Jun Wu,et al.  The Design of a Chemical Virtual Instrument Based on LabVIEW for Determining Temperatures and Pressures , 2007, Journal of automated methods & management in chemistry.