Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method

Transient signal occurs when there is sudden change of pressure inside the pipeline especially due to pressure surge or opening and closing valve. Decomposition method applied in this study to analyze the transient signal and remove noise that contaminate the signal. In this paper, an adaptive decom...

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Main Authors: Muhammad Aminuddin, Pi Remli, M. F., Ghazali, Azmi, W. H., Hanafi, M. Yusop
Format: Article
Language:English
Published: Akademi Baru 2021
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/31931/1/Transient-based%20leak%20detection%20and%20monitoring%20of%20water.pdf
http://umpir.ump.edu.my/id/eprint/31931/
https://doi.org/10.37934/arfmts.83.2.135148
https://doi.org/10.37934/arfmts.83.2.135148
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Institution: Universiti Malaysia Pahang
Language: English
id my.ump.umpir.31931
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spelling my.ump.umpir.319312021-09-07T04:39:53Z http://umpir.ump.edu.my/id/eprint/31931/ Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method Muhammad Aminuddin, Pi Remli M. F., Ghazali Azmi, W. H. Hanafi, M. Yusop TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Transient signal occurs when there is sudden change of pressure inside the pipeline especially due to pressure surge or opening and closing valve. Decomposition method applied in this study to analyze the transient signal and remove noise that contaminate the signal. In this paper, an adaptive decomposition algorithm that is Complementary Ensemble Empirical Mode Decomposition (CEEMD) proposed in order to overcome the problem occur in Hilbert-Huang Transform (HHT) method. This method takes over the Empirical Mode Decomposition (EMD) method that purpose as pre-processing method in HHT. This improvement made to overcome mode mixing and reconstruction error that lies in EMD method. CEEMD method used to extract the IMF’s component of a contaminated signal to remove undesirable noise signal. Instantaneous analysis using Hilbert Transform (HT) apply for selected IMF to locate the feature’s position along the pipeline. The result prove that proposed decomposition method show success with the percentage of error between measured and analysed distance only below that 4% for all features extracted. Akademi Baru 2021-06-17 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/31931/1/Transient-based%20leak%20detection%20and%20monitoring%20of%20water.pdf Muhammad Aminuddin, Pi Remli and M. F., Ghazali and Azmi, W. H. and Hanafi, M. Yusop (2021) Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 83 (2). pp. 135-148. ISSN 2289-7879 https://doi.org/10.37934/arfmts.83.2.135148 https://doi.org/10.37934/arfmts.83.2.135148
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
spellingShingle TJ Mechanical engineering and machinery
TL Motor vehicles. Aeronautics. Astronautics
Muhammad Aminuddin, Pi Remli
M. F., Ghazali
Azmi, W. H.
Hanafi, M. Yusop
Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method
description Transient signal occurs when there is sudden change of pressure inside the pipeline especially due to pressure surge or opening and closing valve. Decomposition method applied in this study to analyze the transient signal and remove noise that contaminate the signal. In this paper, an adaptive decomposition algorithm that is Complementary Ensemble Empirical Mode Decomposition (CEEMD) proposed in order to overcome the problem occur in Hilbert-Huang Transform (HHT) method. This method takes over the Empirical Mode Decomposition (EMD) method that purpose as pre-processing method in HHT. This improvement made to overcome mode mixing and reconstruction error that lies in EMD method. CEEMD method used to extract the IMF’s component of a contaminated signal to remove undesirable noise signal. Instantaneous analysis using Hilbert Transform (HT) apply for selected IMF to locate the feature’s position along the pipeline. The result prove that proposed decomposition method show success with the percentage of error between measured and analysed distance only below that 4% for all features extracted.
format Article
author Muhammad Aminuddin, Pi Remli
M. F., Ghazali
Azmi, W. H.
Hanafi, M. Yusop
author_facet Muhammad Aminuddin, Pi Remli
M. F., Ghazali
Azmi, W. H.
Hanafi, M. Yusop
author_sort Muhammad Aminuddin, Pi Remli
title Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method
title_short Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method
title_full Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method
title_fullStr Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method
title_full_unstemmed Transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (CEEMD) method
title_sort transient-based leak detection and monitoring of water pipes using complementary ensemble empirical mode decomposition (ceemd) method
publisher Akademi Baru
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/31931/1/Transient-based%20leak%20detection%20and%20monitoring%20of%20water.pdf
http://umpir.ump.edu.my/id/eprint/31931/
https://doi.org/10.37934/arfmts.83.2.135148
https://doi.org/10.37934/arfmts.83.2.135148
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