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Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature

The pipelines are used for transporting fluids and it is an important part of the media transportation for oil and gas. However, as pipelines are often spread across vast distances and carry certain hazardous substances, the chances for accidents such as leakage accidents in oil and gas pipelines ar...

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Main Authors: Jaafar, N.S.M., Aziz, I.A., Hasan, M.H.B., Mahmood, A.K.
Format: Article
Published: Springer Verlag 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065903839&doi=10.1007%2f978-3-030-19810-7_14&partnerID=40&md5=1ed60dfdeefb9c30762b5ea13bb377b1
http://eprints.utp.edu.my/23515/
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spelling my.utp.eprints.235152021-08-19T07:58:05Z Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature Jaafar, N.S.M. Aziz, I.A. Hasan, M.H.B. Mahmood, A.K. The pipelines are used for transporting fluids and it is an important part of the media transportation for oil and gas. However, as pipelines are often spread across vast distances and carry certain hazardous substances, the chances for accidents such as leakage accidents in oil and gas pipelines are increased. Variety of factors lead to pipeline leakage accidents such as corrosion, vibration and other impacts affecting the safe operation of pipelines. Pipelines leakages cause both loss of product and as well as environmental damage. Acoustic emissions sensors have recently emerged as a promising tool for long distance pipeline monitoring due to the acoustic emission sensors advantages of high accuracy and low loss per distance. The signal processing is used to decompose the raw signal and the pre-processed signal will be analyzed in the time-frequency domain. Several existing signals processing methods such as Fourier Transform, Wavelet Transform can be used for extracting useful information. The parameters of Empirical Mode Decomposition EMD show promising results. The promising results in terms of accuracy of selections IMFs and analysis of time-frequency domain. The selected of Intrinsic Mode Functions IMFs IMFs are analyzed in the time domain by using two parameters which are standard deviation and variance. The selected IMFs are obtained to reveal the leakage and no leakage signatures of the pipeline. © Springer Nature Switzerland AG 2019. Springer Verlag 2019 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065903839&doi=10.1007%2f978-3-030-19810-7_14&partnerID=40&md5=1ed60dfdeefb9c30762b5ea13bb377b1 Jaafar, N.S.M. and Aziz, I.A. and Hasan, M.H.B. and Mahmood, A.K. (2019) Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature. Advances in Intelligent Systems and Computing, 985 . pp. 139-146. http://eprints.utp.edu.my/23515/
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 The pipelines are used for transporting fluids and it is an important part of the media transportation for oil and gas. However, as pipelines are often spread across vast distances and carry certain hazardous substances, the chances for accidents such as leakage accidents in oil and gas pipelines are increased. Variety of factors lead to pipeline leakage accidents such as corrosion, vibration and other impacts affecting the safe operation of pipelines. Pipelines leakages cause both loss of product and as well as environmental damage. Acoustic emissions sensors have recently emerged as a promising tool for long distance pipeline monitoring due to the acoustic emission sensors advantages of high accuracy and low loss per distance. The signal processing is used to decompose the raw signal and the pre-processed signal will be analyzed in the time-frequency domain. Several existing signals processing methods such as Fourier Transform, Wavelet Transform can be used for extracting useful information. The parameters of Empirical Mode Decomposition EMD show promising results. The promising results in terms of accuracy of selections IMFs and analysis of time-frequency domain. The selected of Intrinsic Mode Functions IMFs IMFs are analyzed in the time domain by using two parameters which are standard deviation and variance. The selected IMFs are obtained to reveal the leakage and no leakage signatures of the pipeline. © Springer Nature Switzerland AG 2019.
format Article
author Jaafar, N.S.M.
Aziz, I.A.
Hasan, M.H.B.
Mahmood, A.K.
spellingShingle Jaafar, N.S.M.
Aziz, I.A.
Hasan, M.H.B.
Mahmood, A.K.
Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature
author_facet Jaafar, N.S.M.
Aziz, I.A.
Hasan, M.H.B.
Mahmood, A.K.
author_sort Jaafar, N.S.M.
title Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature
title_short Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature
title_full Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature
title_fullStr Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature
title_full_unstemmed Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature
title_sort parameter calculation in time analysis for the approach of filtering to select imfs of emd in ae sensors for leakage signature
publisher Springer Verlag
publishDate 2019
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065903839&doi=10.1007%2f978-3-030-19810-7_14&partnerID=40&md5=1ed60dfdeefb9c30762b5ea13bb377b1
http://eprints.utp.edu.my/23515/
_version_ 1738656483026927616
score 13.223943

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