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Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier
Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficien...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675711/ https://www.ncbi.nlm.nih.gov/pubmed/34914761 http://dx.doi.org/10.1371/journal.pone.0261040 |
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author | May, Zazilah Alam, M. K. Nayan, Nazrul Anuar Rahman, Noor A’in A. Mahmud, Muhammad Shazwan |
author_facet | May, Zazilah Alam, M. K. Nayan, Nazrul Anuar Rahman, Noor A’in A. Mahmud, Muhammad Shazwan |
author_sort | May, Zazilah |
collection | PubMed |
description | Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications. |
format | Online Article Text |
id | pubmed-8675711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-86757112021-12-17 Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier May, Zazilah Alam, M. K. Nayan, Nazrul Anuar Rahman, Noor A’in A. Mahmud, Muhammad Shazwan PLoS One Research Article Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications. Public Library of Science 2021-12-16 /pmc/articles/PMC8675711/ /pubmed/34914761 http://dx.doi.org/10.1371/journal.pone.0261040 Text en © 2021 May et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article May, Zazilah Alam, M. K. Nayan, Nazrul Anuar Rahman, Noor A’in A. Mahmud, Muhammad Shazwan Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier |
title | Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier |
title_full | Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier |
title_fullStr | Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier |
title_full_unstemmed | Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier |
title_short | Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier |
title_sort | acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675711/ https://www.ncbi.nlm.nih.gov/pubmed/34914761 http://dx.doi.org/10.1371/journal.pone.0261040 |
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