Laboratory-Scale Identification of Corrosion Mechanisms by a Pattern Recognition System Based on Electrochemical Noise Measurements

Uniform corrosion, pitting corrosion and passivation of 304 stainless steel were promoted and pattern recognition system (PRS) based on electrochemical noise (EN) measurements was employed to classify these corrosion mechanisms in laboratory-scale. The EN signals were analyzed using statistical, shot noise, wavelet and recurrence quantification analysis. It was found that the PRS designed by combining these EN parameters has a discriminant accuracy of 99.7%, which is superior to those designed by solely statistical parameters (92.4%), and the parameters selected by principle component analysis (97.8%). The application of the PRS above is evaluated by considering environmental factors and material types.