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Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector.

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Title: Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector.
Authors: Xu, Tianshu1,2 (AUTHOR), Zeng, Zhoumo1,2 (AUTHOR), Huang, Xinjing1,2 (AUTHOR) huangxinjing@tju.edu.cn, Li, Jian1,2 (AUTHOR), Feng, Hao1,2 (AUTHOR)
Source: Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B. Sep2021, Vol. 153, p167-177. 11p.
Subjects: Leak detection, Support vector machines, Noise, Signal detection, Detectors
Abstract: A spherical detector (SD) is capable of closely approaching a leak point and collecting leak sounds from the inside of a long pipeline, thereby enabling an extremely high leak detection sensitivity. However, acoustic noises arise from collision and friction while the SD is rolling forward, hindering the identification of leak acoustic signals. To address this challenge, this work presents a pipeline leak identification method for an SD based on combining variational mode decomposition (VMD) and a support vector machine (SVM). A leak generation system is set up where the pipe is water-filled, pressurized, and tiltable, and the SD can stand still or roll to collect a sufficient variety of leak sound samples. By decomposing the noisy signals into different modes and selecting the modes with high correlations to reconstruct the signals, the VMD can significantly decrease the collision noise. Additionally, the Mel frequency cepstral coefficients (MFCCs) are extracted and used to constitute a characteristic vector for SVM-based leak recognition. The trained neural network effectively identifies the occurrence of a leak; the recognition accuracy can reach up to 93 %, with a satisfactory specificity of 89.6 %. [ABSTRACT FROM AUTHOR]
Copyright of Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 152163700
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector.
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  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Tianshu%22">Xu, Tianshu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Zhoumo%22">Zeng, Zhoumo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Xinjing%22">Huang, Xinjing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> huangxinjing@tju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Jian%22">Li, Jian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Hao%22">Feng, Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Leak+detection%22">Leak detection</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Noise%22">Noise</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: A spherical detector (SD) is capable of closely approaching a leak point and collecting leak sounds from the inside of a long pipeline, thereby enabling an extremely high leak detection sensitivity. However, acoustic noises arise from collision and friction while the SD is rolling forward, hindering the identification of leak acoustic signals. To address this challenge, this work presents a pipeline leak identification method for an SD based on combining variational mode decomposition (VMD) and a support vector machine (SVM). A leak generation system is set up where the pipe is water-filled, pressurized, and tiltable, and the SD can stand still or roll to collect a sufficient variety of leak sound samples. By decomposing the noisy signals into different modes and selecting the modes with high correlations to reconstruct the signals, the VMD can significantly decrease the collision noise. Additionally, the Mel frequency cepstral coefficients (MFCCs) are extracted and used to constitute a characteristic vector for SVM-based leak recognition. The trained neural network effectively identifies the occurrence of a leak; the recognition accuracy can reach up to 93 %, with a satisfactory specificity of 89.6 %. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.psep.2021.07.024
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 167
    Subjects:
      – SubjectFull: Leak detection
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Noise
        Type: general
      – SubjectFull: Signal detection
        Type: general
      – SubjectFull: Detectors
        Type: general
    Titles:
      – TitleFull: Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector.
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            NameFull: Xu, Tianshu
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            NameFull: Zeng, Zhoumo
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            NameFull: Huang, Xinjing
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            NameFull: Li, Jian
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            NameFull: Feng, Hao
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          Dates:
            – D: 01
              M: 09
              Text: Sep2021
              Type: published
              Y: 2021
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              Value: 09575820
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              Value: 153
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            – TitleFull: Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B
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