SYMPOSIA PAPER Published: 01 January 1996

The Identification of Pitting and Crevice Corrosion Spectra in Electrochemical Noise Using an Artificial Neural Network


An artificial neural network has been developed to identify the onset and classify the type of localized corrosion from electrochemical noise spectra. The multilayer feedforward (MLF) network was trained by classical back-propagation to identify corrosion from the characteristics of the initial current ramp. Using 50 training files and 39 test files taken from measurements on Type 304 stainless steel in a dilute chloride electrolyte, the network accurately detected and classified 96% of the data and reported no misclassifications. Experiments with high levels of adventitious noise superimposed on the original data have been carried out to examine the noise tolerance of the network.

Author Information

Barton, TF
Industrial Research Ltd., Auckland, New Zealand
Tuck, DL
Industrial Research Ltd., Auckland, New Zealand
Wells, DB
Industrial Research Ltd., Auckland, New Zealand
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Developed by Committee: G01
Pages: 157–169
DOI: 10.1520/STP37958S
ISBN-EB: 978-0-8031-5562-6
ISBN-13: 978-0-8031-2032-7