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Analysis of Electrochemical Noise for Corrosion Type Identification by Use of Global Recurrence Plots and Texture Analysis

Kok, T.L. and Aldrich, C. (2019) Analysis of Electrochemical Noise for Corrosion Type Identification by Use of Global Recurrence Plots and Texture Analysis. [["eprint_typename_conference\_item" not defined]]

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

A classification framework was developed to identify different types of corrosion in aqueous systems by use of electrochemical noise. Segments of electrochemical noise signals generated by uniform corrosion, pitting corrosion and passivation were analysed in a sliding window. Unthresholded recurrence plots were derived from each segment based on Euclidean distance measures of similarity. Multivariate image analysis was used to extract features from the recurrence plots and these features were used as predictors in a classifier trained to identify the corrosion types. The classification models showed satisfactory prediction accuracy and could potentially be applied in real-time corrosion monitoring systems. The length of the sliding window is a critical parameter in the proposed framework and its effect on classification performance was investigated as well. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Item Type:["eprint_typename_conference\_item" not defined]
Impact Factor:cited By 0
Uncontrolled Keywords:Corrosion; Multivariant analysis; Neural networks; Pitting; Real time systems; Textures, Classification framework; Classification performance; Electrochemical noise; Euclidean distance measure; Multivariate image analysis; Real time corrosion monitoring; Recurrence plot; Texture analysis, Electrochemical corrosion
ID Code:23627
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 08:09
Last Modified:19 Aug 2021 08:09

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