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Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model.

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Title: Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model.
Authors: Miao, Xingyuan1 (AUTHOR), Zhao, Hong1 (AUTHOR) hzhao@cup.edu.cn, Gao, Boxuan1 (AUTHOR), Song, Fulin1 (AUTHOR)
Source: Reliability Engineering & System Safety. Oct2023, Vol. 238, pN.PAG-N.PAG. 1p.
Subjects: Pipelines, Supervised learning, Petroleum pipelines, Generative adversarial networks, Capsule neural networks, Pipeline corrosion, Leakage
Abstract: • A semi-supervised domain generalization leakage diagnosis method is proposed. • Laser optical sensing technology is applied on the pipeline leakage diagnosis. • IACGAN model and pseudo-label strategy are developed for feature extraction. • D-CapsNet classifier with metric learning is designed for cross-domain diagnosis. Pipeline corrosion will lead to leakage, significantly affecting pipeline reliability and transportation safety. Accurate leakage diagnosis is vital to the operational safety of the oil and gas industry. However, current supervised learning diagnosis methods are limited in addressing cross-domain problems and limited labeled fault samples. And the potential leakage which has the leakage risk is difficult to diagnosis. Therefore, we propose a novel semi-supervised domain generalization method for leakage diagnosis based on laser optical sensing technology. An improved auxiliary classifier generative adversarial network (IACGAN) is developed with new structure and loss function to extract discriminative features. The Capsule network is improved with DenseBlock (D-CapsNet) for determining the leakage situation of source domain and unseen target domain. To make full use of limited data, the metric learning is combined with pseudo-label strategy in semi-supervised learning to enhance feature representations. The experimental results demonstrate that the domain generalization model performs well in cross-domain leakage diagnosis, where the potential leakage risk can also be accurately recognized. The average recognition accuracy is greater than 95%, which has better diagnosis accuracy than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Copyright of Reliability Engineering & System Safety 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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 165470550
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Miao%2C+Xingyuan%22">Miao, Xingyuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Hong%22">Zhao, Hong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hzhao@cup.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Gao%2C+Boxuan%22">Gao, Boxuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Fulin%22">Song, Fulin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Reliability+Engineering+%26+System+Safety%22">Reliability Engineering & System Safety</searchLink>. Oct2023, Vol. 238, pN.PAG-N.PAG. 1p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Pipelines%22">Pipelines</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Petroleum+pipelines%22">Petroleum pipelines</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Capsule+neural+networks%22">Capsule neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Pipeline+corrosion%22">Pipeline corrosion</searchLink><br /><searchLink fieldCode="DE" term="%22Leakage%22">Leakage</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • A semi-supervised domain generalization leakage diagnosis method is proposed. • Laser optical sensing technology is applied on the pipeline leakage diagnosis. • IACGAN model and pseudo-label strategy are developed for feature extraction. • D-CapsNet classifier with metric learning is designed for cross-domain diagnosis. Pipeline corrosion will lead to leakage, significantly affecting pipeline reliability and transportation safety. Accurate leakage diagnosis is vital to the operational safety of the oil and gas industry. However, current supervised learning diagnosis methods are limited in addressing cross-domain problems and limited labeled fault samples. And the potential leakage which has the leakage risk is difficult to diagnosis. Therefore, we propose a novel semi-supervised domain generalization method for leakage diagnosis based on laser optical sensing technology. An improved auxiliary classifier generative adversarial network (IACGAN) is developed with new structure and loss function to extract discriminative features. The Capsule network is improved with DenseBlock (D-CapsNet) for determining the leakage situation of source domain and unseen target domain. To make full use of limited data, the metric learning is combined with pseudo-label strategy in semi-supervised learning to enhance feature representations. The experimental results demonstrate that the domain generalization model performs well in cross-domain leakage diagnosis, where the potential leakage risk can also be accurately recognized. The average recognition accuracy is greater than 95%, which has better diagnosis accuracy than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Reliability Engineering & System Safety 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.ress.2023.109486
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Pipelines
        Type: general
      – SubjectFull: Supervised learning
        Type: general
      – SubjectFull: Petroleum pipelines
        Type: general
      – SubjectFull: Generative adversarial networks
        Type: general
      – SubjectFull: Capsule neural networks
        Type: general
      – SubjectFull: Pipeline corrosion
        Type: general
      – SubjectFull: Leakage
        Type: general
    Titles:
      – TitleFull: Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Miao, Xingyuan
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          Name:
            NameFull: Zhao, Hong
      – PersonEntity:
          Name:
            NameFull: Gao, Boxuan
      – PersonEntity:
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            NameFull: Song, Fulin
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          Dates:
            – D: 01
              M: 10
              Text: Oct2023
              Type: published
              Y: 2023
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              Value: 09518320
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              Value: 238
          Titles:
            – TitleFull: Reliability Engineering & System Safety
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