A novel method for identifying corrosion types and transitions based on Adaboost and electrochemical noise
Anti-Corrosion Methods and Materials
ISSN: 0003-5599
Article publication date: 18 January 2023
Issue publication date: 9 February 2023
Abstract
Purpose
The purpose of this paper is to identify corrosion types and corrosion transitions by a novel electrochemical noise analysis method based on Adaboost.
Design/methodology/approach
The corrosion behavior of Q235 steel was investigated in typical passivation, uniform corrosion and pitting solution by electrochemical noise. Nine feature parameters were extracted from the electrochemical noise data based on statistical analysis and shot noise theory. The feature parameters were analysis by Adaboost to train model and identify corrosion types. The trained Adaboost model was used to identify corrosion type transitions.
Findings
Adaboost algorithm can accurately identify the corrosion type, and the accuracy rate is 99.25%. The identification results of Adaboost for the corrosion type are consistent with corroded morphology analysis. Compared with other machine learning, Adaboost can identify corrosion types more accurately. For corrosion type transition, Adaboost can effectively identify the transition from passivation to uniform corrosion and from passivation to pitting corrosion consistent with corroded morphology analysis.
Originality/value
Adaboost is a suitable method for prediction of corrosion type and transitions. Adaboost can establish the classification model of metal corrosion, which can more conveniently and accurately explore the corrosion types. Adaboost provides important reference for corrosion prediction and protection.
Keywords
Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 51771133).
Conflicts of interest. There are no conflicts of interest to declare.
Citation
Ren, Z., Li, Q., Yang, X. and Wang, J. (2023), "A novel method for identifying corrosion types and transitions based on Adaboost and electrochemical noise", Anti-Corrosion Methods and Materials, Vol. 70 No. 2, pp. 78-85. https://doi.org/10.1108/ACMM-11-2022-2725
Publisher
:Emerald Publishing Limited
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