| Summary: | The corrosion of carbon steel in aqueous media resulting in uniform corrosion, pitting corrosion and passivation was investigated on a laboratory scale. Recurrence quantification analysis was applied to short segments of electrochemical current noise measurements. These segments were converted to recurrence variables, which could be used as reliable predictors in a multilayer perceptron neural network model to identify the type of corrosion. In addition, an automated corrosion monitoring scheme is proposed, based on the principal component scores of the recurrence variables. This approach used the uniform corrosion measurements as reference data and could differentiate between uniform and non-uniform corrosion.
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