Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform

Monitoring the evolution of hydrogen gas on carbon steel pipe using acoustic emission (AE) signal can be a part of a reliable technique in the modern structural health-monitoring (SHM) field. However, the extracted AE signal is always mixed up with random extraneous noise depending on the nature of...

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Main Authors: May, Z., Alam, M.K., Rahman, N.A.A., Mahmud, M.S., Nayan, N.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29827 /
Published: MDPI AG 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096142874&doi=10.3390%2fpr8111460&partnerID=40&md5=cc0e70681f0a8085ce037e0f6a08480d
http://eprints.utp.edu.my/29827/
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spelling utp-eprints.298272022-03-25T02:57:02Z Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform May, Z. Alam, M.K. Rahman, N.A.A. Mahmud, M.S. Nayan, N.A. Monitoring the evolution of hydrogen gas on carbon steel pipe using acoustic emission (AE) signal can be a part of a reliable technique in the modern structural health-monitoring (SHM) field. However, the extracted AE signal is always mixed up with random extraneous noise depending on the nature of the service structure and experimental environment. The noisy AE signals often mislead the obtaining of the desired features from the signals for SHM and degrade the performance of the monitoring system. Therefore, there is a need for the signal denoising method to improve the quality of the extracted AE signals without degrading the original properties of the signals before using them for any knowledge discovery. This article proposes a non-decimated stationary wavelet transform (ND-SWT) method based on the variable soft threshold function for denoising hydrogen evolution AE signals. The proposed method filters various types of noises from the acquired AE signal and removes them efficiently without degrading the original properties. The hydrogen evolution experiments on carbon steel pipelines are carried out for AE data acquisition. Simulations on experimentally acquired AE signals and randomly generated synthetic signals with different levels of noise are performed by the ND-SWT method for noise removal. Results show that our proposed method can effectively eliminate Gaussian white noise as well as noise from the vibration and frictional activity and provide efficient noise removal solutions for SHM applications with minimum reconstruction error, to extract meaningful AE signals from the large-scale noisy AE signals during monitoring and inspection. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. MDPI AG 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096142874&doi=10.3390%2fpr8111460&partnerID=40&md5=cc0e70681f0a8085ce037e0f6a08480d May, Z. and Alam, M.K. and Rahman, N.A.A. and Mahmud, M.S. and Nayan, N.A. (2020) Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform. Processes, 8 (11). pp. 1-19. http://eprints.utp.edu.my/29827/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Monitoring the evolution of hydrogen gas on carbon steel pipe using acoustic emission (AE) signal can be a part of a reliable technique in the modern structural health-monitoring (SHM) field. However, the extracted AE signal is always mixed up with random extraneous noise depending on the nature of the service structure and experimental environment. The noisy AE signals often mislead the obtaining of the desired features from the signals for SHM and degrade the performance of the monitoring system. Therefore, there is a need for the signal denoising method to improve the quality of the extracted AE signals without degrading the original properties of the signals before using them for any knowledge discovery. This article proposes a non-decimated stationary wavelet transform (ND-SWT) method based on the variable soft threshold function for denoising hydrogen evolution AE signals. The proposed method filters various types of noises from the acquired AE signal and removes them efficiently without degrading the original properties. The hydrogen evolution experiments on carbon steel pipelines are carried out for AE data acquisition. Simulations on experimentally acquired AE signals and randomly generated synthetic signals with different levels of noise are performed by the ND-SWT method for noise removal. Results show that our proposed method can effectively eliminate Gaussian white noise as well as noise from the vibration and frictional activity and provide efficient noise removal solutions for SHM applications with minimum reconstruction error, to extract meaningful AE signals from the large-scale noisy AE signals during monitoring and inspection. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author May, Z.
Alam, M.K.
Rahman, N.A.A.
Mahmud, M.S.
Nayan, N.A.
spellingShingle May, Z.
Alam, M.K.
Rahman, N.A.A.
Mahmud, M.S.
Nayan, N.A.
Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform
author_sort May, Z.
title Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform
title_short Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform
title_full Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform
title_fullStr Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform
title_full_unstemmed Denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform
title_sort denoising of hydrogen evolution acoustic emission signal based on non-decimated stationary wavelet transform
publisher MDPI AG
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096142874&doi=10.3390%2fpr8111460&partnerID=40&md5=cc0e70681f0a8085ce037e0f6a08480d
http://eprints.utp.edu.my/29827/
_version_ 1741197305656115200
score 11.62408

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