Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis
Abstract Electrochemical noise (EN) is a crucial technique in the monitoring of corrosion systems due to its ability to provide real-time, non-intrusive insights into the corrosion process. By measuring the spontaneous fluctuations in voltage and current that occur naturally in a corroding system, E...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00585-8 |
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| Summary: | Abstract Electrochemical noise (EN) is a crucial technique in the monitoring of corrosion systems due to its ability to provide real-time, non-intrusive insights into the corrosion process. By measuring the spontaneous fluctuations in voltage and current that occur naturally in a corroding system, EN allows for the detection of localised corrosion events, such as pitting, without the need for external perturbation. In this investigation, a multivariate statistical process monitoring framework (MSPC) based on the use of deep learning models and principal component analysis (PCA) is proposed. Electrochemical noise associated with uniform corrosion is segmented with a sliding window, with the segments converted to images from which features are extracted with deep learning models. Finally, these features are used to construct a principal component model that can be used to detect deviations from uniform corrosion. |
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| ISSN: | 2397-2106 |