IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v241y2024ics0951832023006117.html
   My bibliography  Save this article

Spatial characterization and simulation of new defects in corroded pipeline based on In-Line Inspections

Author

Listed:
  • Amaya-Gómez, Rafael
  • Sánchez-Silva, Mauricio
  • Muñoz, Felipe
  • Schoefs, Franck
  • Bastidas-Arteaga, Emilio

Abstract

Onshore pipelines are exposed to corrosion degradation, facilitated by the pipeline’s management and surrounding aggressive environmental conditions. Every 2 to 6 years, pipeline operators often conduct In-Line (ILI) inspections to screen for pipe damage using magnetic or ultrasonic sensors. Considering soil and fluid aggressive conditions, and the possibility of false alarms or a miss-detections from the inspection device, new defects, i.e., metal loss at either the inner or outer wall, should be expected to occur between consecutive inspections. Considering the possibility of “corrosion colonies†and their significance in the pipeline’s reliability assessment, different authors have incorporated new corrosion defects in degradation and further reliability assessments using a Homogeneous Poisson Process. This process assumes that corrosion points are evenly distributed, which can be classified as conservative. This study aims to characterize the main spatial distribution of corrosion defects using the Complete Spatial Randomness (CSR) assumption under hypothesis testing. Additionally, it assesses how is the interaction between new and old defects from a repulsion–attraction perspective, and it proposes an alternative to simulate them for further reliability analyses. The suggested approach was applied in a real case study, obtaining that corrosion defects tend to be clustered and little repelled from those already detected.

Suggested Citation

  • Amaya-Gómez, Rafael & Sánchez-Silva, Mauricio & Muñoz, Felipe & Schoefs, Franck & Bastidas-Arteaga, Emilio, 2024. "Spatial characterization and simulation of new defects in corroded pipeline based on In-Line Inspections," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023006117
    DOI: 10.1016/j.ress.2023.109697
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023006117
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109697?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. Yazdi, Mohammad & Khan, Faisal & Abbassi, Rouzbeh & Quddus, Noor & Castaneda-Lopez, Homero, 2022. "A review of risk-based decision-making models for microbiologically influenced corrosion (MIC) in offshore pipelines," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. O Cronie & M N M Van Lieshout, 2018. "A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions," Biometrika, Biometrika Trust, vol. 105(2), pages 455-462.
    3. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
    4. Rainer Winkelmann, 2000. "Seemingly Unrelated Negative Binomial Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 553-560, September.
    5. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
    Full references (including those not matched with items on IDEAS)

    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. Antonello Maruotti & Pierfrancesco Alaimo Di Loro, 2023. "CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    2. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    3. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008. "Estimating demand systems when outcomes are correlated counts," Journal of Econometrics, Elsevier, vol. 147(2), pages 282-298, December.
    4. Ping Zhang & Chenzhu Wang & Fei Chen & Suping Cui & Jianchuan Cheng & Wu Bo, 2022. "A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    5. Borrajo, M.I. & González-Manteiga, W. & Martínez-Miranda, M.D., 2020. "Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    6. M. N. M. Lieshout, 2020. "Infill Asymptotics and Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions," Methodology and Computing in Applied Probability, Springer, vol. 22(3), pages 995-1008, September.
    7. Aristidis Nikoloulopoulos & Dimitris Karlis, 2010. "Regression in a copula model for bivariate count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1555-1568.
    8. S Ward & H S Battey & E A K Cohen, 2023. "Nonparametric estimation of the intensity function of a spatial point process on a Riemannian manifold," Biometrika, Biometrika Trust, vol. 110(4), pages 1009-1021.
    9. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    10. Azam, Kazim & Pitt, Michael, 2014. "Bayesian Inference for a Semi-Parametric Copula-based Markov Chain," The Warwick Economics Research Paper Series (TWERPS) 1051, University of Warwick, Department of Economics.
    11. Burkey, Mark L. & Obeng, Kofi, 2005. "Crash Risk Reduction at Signalized Intersections Using Longitudinal Data," MPRA Paper 36281, University Library of Munich, Germany.
    12. Atella, Vincenzo & Deb, Partha, 2008. "Are primary care physicians, public and private sector specialists substitutes or complements? Evidence from a simultaneous equations model for count data," Journal of Health Economics, Elsevier, vol. 27(3), pages 770-785, May.
    13. Mola-Yudego, Blas & Selkimäki, Mari & González-Olabarria, José Ramón, 2014. "Spatial analysis of the wood pellet production for energy in Europe," Renewable Energy, Elsevier, vol. 63(C), pages 76-83.
    14. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
    15. Yingqi Zhao & Donglin Zeng & Amy H. Herring & Amy Ising & Anna Waller & David Richardson & Michael R. Kosorok, 2011. "Detecting Disease Outbreaks Using Local Spatiotemporal Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1508-1517, December.
    16. Kristian Bjørn Hessellund & Ganggang Xu & Yongtao Guan & Rasmus Waagepetersen, 2022. "Second‐order semi‐parametric inference for multivariate log Gaussian Cox processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 244-268, January.
    17. Ondřej Šedivý & Antti Penttinen, 2014. "Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(3), pages 225-249, August.
    18. Vishaal Baulkaran & Pawan Jain, 2023. "Who uses robo‐advising and how?," The Financial Review, Eastern Finance Association, vol. 58(1), pages 65-89, February.
    19. Bouezmarni, Taoufik & Rombouts, Jeroen V.K., 2010. "Nonparametric density estimation for positive time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 245-261, February.
    20. Trevor C. Bailey & Paul J. Hewson, 2004. "Simultaneous modelling of multiple traffic safety performance indicators by using a multivariate generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 501-517, August.

    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:241:y:2024:i:c:s0951832023006117. 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.