Classification of Corrosion Detected by Acoustic Emission

Abstract

This paper presents an Acoustic Emission (AE) to detect pitting corrosion in stainless steel. The AE signals were analyzed to reveal the correlation between AE parameters and severity levels of pitting corrosion in austenitic stainless steel 304 (SS304). In this work, the corrosion severity is graded roughly into five levels based on the depth of corrosion. Relationships between a number of time-domain AE parameters and the corrosion severity were first studied and key parameters identified. The corrosion severity was also categorized into three stages: initial, propagation and final stages based on the source mechanisms of the AE signals. We identified these stages from the frequency-domain characteristic of the AE signal and the visual characteristic of the corroded pits in each level of corrosion severity. A number of measures were employed to quantify such characteristics and the source mechanisms hypothesized. To demonstrate the usefulness of such parameters, a feed-forward neural network was used to classify the corrosion severity. Preprocessing and verification techniques were provided to facilitate and to maintain the generalization capability of the network. The classification performance is excellent and demonstrates that the AE technique and a neural network can be efficiently used to detect and monitor the occurrence of corrosion as well as to classify the corrosion severity.