Electrochemical Noise Analysis of Pitting Corrosion of Type 304L Stainless Steel

Abstract

Electrochemical current and potential noise were simultaneously acquired from Type 304L stainless steel (UNS S30403) in 0.05 M ferric chloride (FeCl 3 ) using a three-electrode configuration . Power spectral, statistical, and wavelet analyses have been used to know the uniqueness of the parameters proposed for the identification of various types of corrosion processes. Roll-off slopes derived from power spectral analysis and statistical parameters such as standard deviation, localization index, and kurtosis corroborated with pitting as the corrosion mechanism. Energy distribution plots (EDP) obtained from wavelet analysis of current noise was found to be useful to derive mechanistic information on the progress of corrosion. Discrete wavelet transform was used to decompose the signals into a D 1 , D 2 , D 3 …D 8 , S 8 set of coefficients. The EDP showed that the contribution from the medium time scale crystal, D 5 , prevailed over the smaller time scale crystals and larger time scale crystals during the initial stages of immersion. With an increase in the time of immersion, the energy deposition on the larger time scale crystals increased and the maximum energy was concentrated on the D 8 crystals, indicating that the dominant process occurring on the specimen surface was stable pitting. The results of the investigation are detailed in the paper.

Alternative to Nitric Acid Passivation of 15-5 and 17-4PH Stainless Steel Using Electrochemical Techniques

“…The use of LI to determine corrosion types has been the subject of many discussions among investigators on the data treatment and interpretations using LI [29,30,38,[57][58][59]. Since the mean of the noise data (detrended) would be negligible, the standard deviation and root mean square current noise would converge to the same value and, hence, the LI evaluated from the data would be unity, irrespective of the corrosion type.…”

“…The EN data can be analyzed by several methods. Perhaps the most commonly used are those related to frequency domain (power density spectral or spectral analysis), time domain (statistical methods as skewness, kurtosis, localization index (LI), and the variation of signal amplitude with time) and time-frequency domains [29,30]. Suresh and Mudali [31] studied the corrosion of UNS S30403 stainless steel in 0.05 M ferric chloride (FeCl 3 ) by spectral, statistical, and wavelet methods to deduce the corrosion mechanism.…”

The reassigned pseudo Wigner–Ville transform in electrochemical noise analysis

“…Medium timescales are reported to be dominant in the case of metastable pitting. 75 The peaks in D3-D5 crystals appeared as the result of the contribution of frequencies in the range from 10 −1 to 1 Hz. Similar observations of the main corrosion events contributing to the experimental signal were acquired by both RSPWD and DWT.…”

Correlated pitting stages of 304 stainless steel with recurrence quantification analysis of electrochemical noise

“…Suresh et al obtained similar results when 304 SS was exposed to 0.05 M FeCl 3 solution . They found the wavelet energy mainly concentrated on d5 at the initial stage and with further immersion the wavelet energy was occupied by d8 corresponding to the stable pitting …”