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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Citations

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    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).

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