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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| Format: | Article |
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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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| author | Ahmed Abdulmutaali Chris Aldrich Katerina Lepkova |
| author_facet | Ahmed Abdulmutaali Chris Aldrich Katerina Lepkova |
| author_sort | Ahmed Abdulmutaali |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-08d00fbe5fd5452fb3c133f67e231dad |
| institution | OA Journals |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-08d00fbe5fd5452fb3c133f67e231dad2025-08-20T02:10:46ZengNature Portfolionpj Materials Degradation2397-21062025-05-019111210.1038/s41529-025-00585-8Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysisAhmed Abdulmutaali0Chris Aldrich1Katerina Lepkova2Department of Chemical Engineering, Collage of Engineering, King Khalid UniversityCurtin Corrosion Centre, Curtin University, Western Australian School of Mines - Mineral, Energy and Chemical Engineering, Curtin UniversityCurtin Corrosion Centre, Curtin University, Western Australian School of Mines - Mineral, Energy and Chemical Engineering, Curtin UniversityAbstract 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.https://doi.org/10.1038/s41529-025-00585-8 |
| spellingShingle | Ahmed Abdulmutaali Chris Aldrich Katerina Lepkova Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis npj Materials Degradation |
| title | Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis |
| title_full | Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis |
| title_fullStr | Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis |
| title_full_unstemmed | Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis |
| title_short | Unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis |
| title_sort | unsupervised process monitoring of corrosion based on electrochemical noise and multivariate image analysis |
| url | https://doi.org/10.1038/s41529-025-00585-8 |
| work_keys_str_mv | AT ahmedabdulmutaali unsupervisedprocessmonitoringofcorrosionbasedonelectrochemicalnoiseandmultivariateimageanalysis AT chrisaldrich unsupervisedprocessmonitoringofcorrosionbasedonelectrochemicalnoiseandmultivariateimageanalysis AT katerinalepkova unsupervisedprocessmonitoringofcorrosionbasedonelectrochemicalnoiseandmultivariateimageanalysis |