Abstract
In order to study acoustic emission (AE) signals characteristics of pitting corrosion on carbon steel, Pitting corrosion process on carbon steel in 6% ferric chloride solution was monitored by AE technology. K-mean cluster algorithm was used to classify the monitored AE signals, in which the duration, counts, amplitude, absolute energy and peak frequency were analyzed as the AE signals characteristics, and different types AE sources were identified. The results showed that there were mainly three type AE sources during carbon steel pitting corrosion process in ferric chloride solution, and the different types AE sources could be classified by cluster analysis. The research results have some certain significance for AE monitoring of pitting corrosion on carbon steel.
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