Characterization of corrosion processes by current noise wavelet-based fractal and correlation analysis

Abstract

Electrochemical noise data in the presence of pitting, general corrosion and passivity were analyzed using the discrete wavelet transform. The registered current noise was decomposed into a set of band-limited details, which contain information about corrosion events occurring at a determined time-scale. It has been observed that the signal variance and variances of details depend on the intensity of processes. Distribution of the signal energy among different details was characteristic for the particular type of corrosion. The characterization of corrosion processes on the basis of in the wavelet domain calculated Hurst parameter H and fractal dimension, D, of electrochemical noise signals has been established. It is concluded that general corrosion is a stationary random process with a weak persistence and D = 2.14, whereas pitting corrosion is a non-stationary process with a long memory effect and D = 1.07. Passivity is a non-stationary process near to the Brownian motion with D = 1.56. The persistence features of electrochemical noise signals were explained also by correlation coefficients calculated between signals obtained by discrete wavelet multiresolution decomposition.