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Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data

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  • Qin, H.
  • Zhou, W.
  • Zhang, S.

Abstract

Stochastic process-based models are developed to characterize the generation and growth of metal-loss corrosion defects on oil and gas steel pipelines. The generation of corrosion defects over time is characterized by the non-homogenous Poisson process, and the growth of depths of individual defects is modeled by the non-homogenous gamma process (NHGP). The defect generation and growth models are formulated in a hierarchical Bayesian framework, whereby the parameters of the models are evaluated from the in-line inspection (ILI) data through the Bayesian updating by accounting for the probability of detection (POD) and measurement errors associated with the ILI data. The Markov Chain Monte Carlo (MCMC) simulation in conjunction with the data augmentation (DA) technique is employed to carry out the Bayesian updating. Numerical examples that involve simulated ILI data are used to illustrate and validate the proposed methodology.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:144:y:2015:i:c:p:334-342
    DOI: 10.1016/j.ress.2015.08.007
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    4. Kuniewski, Sebastian P. & van der Weide, Johannes A.M. & van Noortwijk, Jan M., 2009. "Sampling inspection for the evaluation of time-dependent reliability of deteriorating systems under imperfect defect detection," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1480-1490.
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    Cited by:

    1. Hazra, Indranil & Pandey, Mahesh D. & Manzana, Noldainerick, 2020. "Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    2. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui, 2023. "Dynamic Bayesian network model to study under-deposit corrosion," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. 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.
    4. Woloszyk, Krzysztof & Garbatov, Yordan, 2024. "A probabilistic-driven framework for enhanced corrosion estimation of ship structural components," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Sun, Xuxue & Mraied, Hesham & Cai, Wenjun & Zhang, Qiong & Liang, Guoyuan & Li, Mingyang, 2018. "Bayesian latent degradation performance modeling and quantification of corroding aluminum alloys," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 84-96.
    6. 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.
    7. Choi, Woosung & Youn, Byeng D. & Oh, Hyunseok & Kim, Nam H., 2019. "A Bayesian approach for a damage growth model using sporadically measured and heterogeneous on-site data from a steam turbine," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 137-150.
    8. 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.
    9. Guilin Zhang & Fei Xie & Dan Wang, 2024. "Reliability assessment method for tank bottom plates based on hierarchical Bayesian corrosion growth model," Journal of Risk and Reliability, , vol. 238(1), pages 112-121, February.

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