A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages

Targeting the challenge of determining the degree of blockage in buried pipelines and the difficulty of effectively extracting blockage features, a blockage detection method integrating variational mode decomposition (VMD) and information gain is proposed. Acoustic impulse response signals were obta...

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Autores principales:	Xuefeng Zhu, Zao Feng, Jiande Wu, Weiquan Deng
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sewage blockage identification VMD information gain feature selection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/acb54142b53d4f62b832f2315f1cf9dd
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spelling	oai:doaj.org-article:acb54142b53d4f62b832f2315f1cf9dd2021-11-25T16:38:53ZA Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages10.3390/app1122108242076-3417https://doaj.org/article/acb54142b53d4f62b832f2315f1cf9dd2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10824https://doaj.org/toc/2076-3417Targeting the challenge of determining the degree of blockage in buried pipelines and the difficulty of effectively extracting blockage features, a blockage detection method integrating variational mode decomposition (VMD) and information gain is proposed. Acoustic impulse response signals were obtained by deconvolving the output signals of the system, which were then subjected to VMD to obtain 12 components in different frequency ranges. Next, information gain (IG) was introduced to characterize the 12 components quantitatively, through which the components containing rich information about the pipe conditions were selected out. Meanwhile, sound pressure level conversion was performed on the selected components to amplify any changes in the sound field. Finally, the root mean square entropy (RMSE) was calculated to constitute the feature eigenvectors, which were input into Random Forests (RF) classifier for defect identification of pipeline. As the experimental results demonstrate, the proposed method is capable of determining the degree of blockage effectively in the running state. Meanwhile, it can also eliminate the interference of functional parts such as lateral connections during the identification process, thereby improving the identification accuracy. The present study has shown both theoretical significance and application value in the field of defect detection and recognition.Xuefeng ZhuZao FengJiande WuWeiquan DengMDPI AGarticlesewage blockage identificationVMDinformation gainfeature selectionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10824, p 10824 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sewage blockage identification VMD information gain feature selection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	sewage blockage identification VMD information gain feature selection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Xuefeng Zhu Zao Feng Jiande Wu Weiquan Deng A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
description	Targeting the challenge of determining the degree of blockage in buried pipelines and the difficulty of effectively extracting blockage features, a blockage detection method integrating variational mode decomposition (VMD) and information gain is proposed. Acoustic impulse response signals were obtained by deconvolving the output signals of the system, which were then subjected to VMD to obtain 12 components in different frequency ranges. Next, information gain (IG) was introduced to characterize the 12 components quantitatively, through which the components containing rich information about the pipe conditions were selected out. Meanwhile, sound pressure level conversion was performed on the selected components to amplify any changes in the sound field. Finally, the root mean square entropy (RMSE) was calculated to constitute the feature eigenvectors, which were input into Random Forests (RF) classifier for defect identification of pipeline. As the experimental results demonstrate, the proposed method is capable of determining the degree of blockage effectively in the running state. Meanwhile, it can also eliminate the interference of functional parts such as lateral connections during the identification process, thereby improving the identification accuracy. The present study has shown both theoretical significance and application value in the field of defect detection and recognition.
format	article
author	Xuefeng Zhu Zao Feng Jiande Wu Weiquan Deng
author_facet	Xuefeng Zhu Zao Feng Jiande Wu Weiquan Deng
author_sort	Xuefeng Zhu
title	A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
title_short	A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
title_full	A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
title_fullStr	A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
title_full_unstemmed	A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages
title_sort	novel feature selection based on vmd and information gain for pipe blockages
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/acb54142b53d4f62b832f2315f1cf9dd
work_keys_str_mv	AT xuefengzhu anovelfeatureselectionbasedonvmdandinformationgainforpipeblockages AT zaofeng anovelfeatureselectionbasedonvmdandinformationgainforpipeblockages AT jiandewu anovelfeatureselectionbasedonvmdandinformationgainforpipeblockages AT weiquandeng anovelfeatureselectionbasedonvmdandinformationgainforpipeblockages AT xuefengzhu novelfeatureselectionbasedonvmdandinformationgainforpipeblockages AT zaofeng novelfeatureselectionbasedonvmdandinformationgainforpipeblockages AT jiandewu novelfeatureselectionbasedonvmdandinformationgainforpipeblockages AT weiquandeng novelfeatureselectionbasedonvmdandinformationgainforpipeblockages
_version_	1718413095707082752

A Novel Feature Selection Based on VMD and Information Gain for Pipe Blockages

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