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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...

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Main Authors: May, Z., Alam, M.K., Nayan, N.A., Rahman, N.A.A., Mahmud, M.S.
Format: Article
Published: Public Library of Science 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122003922&doi=10.1371%2fjournal.pone.0261040&partnerID=40&md5=d4a15d6f950a54dcb65924af2d5bcfb6
http://eprints.utp.edu.my/29593/
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spelling my.utp.eprints.295932022-03-25T02:09:59Z Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier May, Z. Alam, M.K. Nayan, N.A. Rahman, N.A.A. Mahmud, M.S. 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. © 2021 May et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Public Library of Science 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122003922&doi=10.1371%2fjournal.pone.0261040&partnerID=40&md5=d4a15d6f950a54dcb65924af2d5bcfb6 May, Z. and Alam, M.K. and Nayan, N.A. and Rahman, N.A.A. and Mahmud, M.S. (2021) Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier. PLoS ONE, 16 (12 Dec). http://eprints.utp.edu.my/29593/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
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. © 2021 May et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
format Article
author May, Z.
Alam, M.K.
Nayan, N.A.
Rahman, N.A.A.
Mahmud, M.S.
spellingShingle May, Z.
Alam, M.K.
Nayan, N.A.
Rahman, N.A.A.
Mahmud, M.S.
Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier
author_facet May, Z.
Alam, M.K.
Nayan, N.A.
Rahman, N.A.A.
Mahmud, M.S.
author_sort May, Z.
title 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_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_sort acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier
publisher Public Library of Science
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122003922&doi=10.1371%2fjournal.pone.0261040&partnerID=40&md5=d4a15d6f950a54dcb65924af2d5bcfb6
http://eprints.utp.edu.my/29593/
_version_ 1738656987778908160
score 13.223943

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