An artificial neural network (ANN) has been trained to monitor the electrochemical signals produced by electrodes of stainless steel during the initiation stage of localized corrosion. This exploratory study used changes in the current time series to monitor the onset of corrosion and determine whether the form of corrosion was pitting or crevice corrosion. A multilayer feedforward perceptron network was trained by classical back-propagation, using 50 training files of real data, 25 each of pitting and crevice current/time spectra, the network learned to accurately identify corrosion onset in 98% of the files in 30000 training episodes, and reported no misclassification. The neural network showed 90% accuracy in determining corrosion onset in 39 additional data files used for testing. The network had greater accuracy in correctly classifying pitting corrosion than for crevice corrosion.
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