IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v128y2014icp24-31.html
   My bibliography  Save this article

Bayesian dynamic linear model for growth of corrosion defects on energy pipelines

Author

Listed:
  • Zhang, Shenwei
  • Zhou, Wenxing

Abstract

This paper describes the use of the second-order polynomial dynamic linear model (DLM) to characterize the growth of the depth of corrosion defects on energy pipelines using data obtained from multiple high-resolution in-line inspections (ILI). The growth model incorporates the measurement error of the ILI tools and captures the temporal variability of the corrosion growth by allowing the artificially-constructed average growth rate between two successive inspections to vary with time. The Markov Chain Monte Carlo simulation is employed to carry out the Bayesian updating of the growth model and evaluate the posterior distributions of the model parameters. An example involving real ILI data collected from an in-service natural gas pipeline is employed to illustrate and validate the growth model. The analysis results show that the defect depths predicted by the proposed model agree well with the actual depths and are more accurate than those predicted by the Gamma process- and Inverse Gaussian process-based growth models reported in the literature.

Suggested Citation

  • Zhang, Shenwei & Zhou, Wenxing, 2014. "Bayesian dynamic linear model for growth of corrosion defects on energy pipelines," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 24-31.
    • Handle: RePEc:eee:reensy:v:128:y:2014:i:c:p:24-31
    DOI: 10.1016/j.ress.2014.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832014000635
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2014.04.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. D Randell & M Goldstein & G Hardman & P Jonathan, 2010. "Bayesian linear inspection planning for large-scale physical systems," Journal of Risk and Reliability, , vol. 224(4), pages 333-345, December.
    2. Yuan, X.-X. & Mao, D. & Pandey, M.D., 2009. "A Bayesian approach to modeling and predicting pitting flaws in steam generator tubes," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1838-1847.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dann, Markus R. & Maes, Marc A., 2018. "Stochastic corrosion growth modeling for pipelines using mass inspection data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 245-254.
    2. Hajiha, Mohammadmahdi & Liu, Xiao & Lee, Young M. & Ramin, Moghaddass, 2022. "A physics-regularized data-driven approach for health prognostics of complex engineered systems with dependent health states," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Dann, Markus R. & Dann, Christoph, 2017. "Automated matching of pipeline corrosion features from in-line inspection data," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 40-50.
    4. Heidary, Roohollah & Groth, Katrina M., 2021. "A hybrid population-based degradation model for pipeline pitting corrosion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mason, Paolo, 2016. "Approximate Bayesian Computation of the occurrence and size of defects in Advanced Gas-cooled nuclear Reactor boilers," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 21-25.
    2. Mason, Paolo, 2017. "A Bayesian analysis of component life expectancy and its implications on the inspection schedule," Reliability Engineering and System Safety, Elsevier, vol. 161(C), pages 87-94.
    3. Kaushik Chatterjee & Mohammad Modarres, 2013. "A probabilistic approach for estimating defect size and density considering detection uncertainties and measurement errors," Journal of Risk and Reliability, , vol. 227(1), pages 28-40, February.
    4. Reuel Smith & Mohammad Modarres & Enrique López Droguett, 2018. "A recursive Bayesian approach to small fatigue crack propagation and detection modeling," Journal of Risk and Reliability, , vol. 232(6), pages 738-753, December.
    5. Hermann, Simone & Ickstadt, Katja & Müller, Christine H., 2018. "Bayesian prediction for a jump diffusion process – With application to crack growth in fatigue experiments," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 83-96.
    6. Hoseyni, Seyed Mojtaba & Di Maio, Francesco & Zio, Enrico, 2019. "Condition-based probabilistic safety assessment for maintenance decision making regarding a nuclear power plant steam generator undergoing multiple degradation mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    7. Qin, H. & Zhou, W. & Zhang, S., 2015. "Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 334-342.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:128:y:2014:i:c:p:24-31. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.