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Semi-supervised health assessment of pipeline systems based on optical fiber monitoring

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Listed:
  • Jiang, Shengyu
  • He, Rui
  • Chen, Guoming
  • Zhu, Yuan
  • Shi, Jiaming
  • Liu, Kang
  • Chang, Yuanjiang

Abstract

Health assessment of pipeline systems is of deep significance for improving pipeline reliability and integrity. Traditional health assessment methods may be difficult or costly to perform on pipeline systems due to the long distances and environmental constraints of pipelines. This paper incorporates the distributed optical fiber sensor (DOFS) technique and the semi-supervised learning algorithm into the pipeline health assessment framework. Three critical problems that limit the application of DOFS in pipeline health assessments are addressed. First, an applicable damage monitoring experiment of a pipeline system is designed, which is effective in obtaining the necessary base data for data-driven modeling. Second, the correspondence between the pipeline health status and the monitored strain features is established. The experimental data are shared for public research, which is expected to solve the problem of the lack of benchmark research data in related fields. Third, considering the scarcity of labeled degradation data in pipelines, a semi-supervised denoising autoencoder model is proposed specifically for pipeline health assessment. The proposed method is demonstrated and validated using a comparative experimental case study.

Suggested Citation

  • Jiang, Shengyu & He, Rui & Chen, Guoming & Zhu, Yuan & Shi, Jiaming & Liu, Kang & Chang, Yuanjiang, 2023. "Semi-supervised health assessment of pipeline systems based on optical fiber monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005476
    DOI: 10.1016/j.ress.2022.108932
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    References listed on IDEAS

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    1. He, Rui & Dai, Yiyang & Lu, Jiachen & Mou, Chuanlin, 2018. "Developing ladder network for intelligent evaluation system: Case of remaining useful life prediction for centrifugal pumps," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 385-393.
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    4. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    6. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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    Citations

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    Cited by:

    1. Miao, Xingyuan & Zhao, Hong, 2023. "Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    3. Xiaoyu Cheng & Guangyu Qian & Wei He & Guohui Zhou, 2022. "A Liquid Launch Vehicle Safety Assessment Model Based on Semi-Quantitative Interval Belief Rule Base," Mathematics, MDPI, vol. 10(24), pages 1-24, December.

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