IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v214y2021ics0951832021002726.html
   My bibliography  Save this article

A hybrid population-based degradation model for pipeline pitting corrosion

Author

Listed:
  • Heidary, Roohollah
  • Groth, Katrina M.

Abstract

This paper presents a novel algorithm to develop a population-based pitting corrosion degradation model for piggable oil and gas pipelines. The algorithm is designed to estimate and predict the distribution of actual depth of existing pits on a pipeline segment, given two or more sets of in-line inspection data that have uncertainty in size and number of the detected pits. This algorithm eliminates the need for a defect-matching procedure for those pits that are not critical, that is required in developing defect-based pitting corrosion degradation models. A hierarchical Bayesian model based on a non-homogeneous gamma process is developed to fuse the uncertain in-line inspection data and physics of failure knowledge of pitting corrosion process. Measurement error (ME), probability of detection (POD), and probability of false call (POFC) are addressed in the developed algorithm. The application of the developed algorithm is demonstrated by implementing it on a simulated case study and the results are compared with the simulated data from a generic degradation model that is available in the literature. Results indicate that this algorithm can predict the degradation level of the pipeline with a high accuracy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021002726
    DOI: 10.1016/j.ress.2021.107740
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021002726
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107740?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. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    2. 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.
    3. Dana Kelly & Curtis Smith, 2011. "Bayesian Inference for Probabilistic Risk Assessment," Springer Series in Reliability Engineering, Springer, number 978-1-84996-187-5, January.
    4. A. Valor & F. Caleyo & L. Alfonso & J. C. Velázquez & J. M. Hallen, 2013. "Markov Chain Models for the Stochastic Modeling of Pitting Corrosion," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-13, July.
    5. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    6. 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.
    7. 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.
    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. Bansal, Parth & Zheng, Zhuoyuan & Shao, Chenhui & Li, Jingjing & Banu, Mihaela & Carlson, Blair E & Li, Yumeng, 2022. "Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Amaya-Gómez, Rafael & Schoefs, Franck & Sánchez-Silva, Mauricio & Muñoz, Felipe & Bastidas-Arteaga, Emilio, 2022. "Matching of corroded defects in onshore pipelines based on In-Line Inspections and Voronoi partitions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    3. Salem, Marwa Belhaj & Fouladirad, Mitra & Deloux, Estelle, 2022. "Variance Gamma process as degradation model for prognosis and imperfect maintenance of centrifugal pumps," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Na, Kyumin & Yoon, Heonjun & Kim, Jaedong & Kim, Sungjong & Youn, Byeng D., 2023. "PERL: Probabilistic energy-ratio-based localization for boiler tube leaks using descriptors of acoustic emission signals," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Dao, Uyen & Sajid, Zaman & Khan, Faisal & Zhang, Yahui & Tran, Trung, 2023. "Modeling and analysis of internal corrosion induced failure of oil and gas pipelines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Yin, Yuanbo & Yang, Hao & Duan, Pengfei & Li, Luling & Zio, Enrico & Liu, Cuiwei & Li, Yuxing, 2022. "Improved quantitative risk assessment of a natural gas pipeline considering high-consequence areas," Reliability Engineering and System Safety, Elsevier, vol. 225(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. 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. Yu, Weichao & Huang, Weihe & Wen, Kai & Zhang, Jie & Liu, Hongfei & Wang, Kun & Gong, Jing & Qu, Chunxu, 2021. "Subset simulation-based reliability analysis of the corroding natural gas pipeline," Reliability Engineering and System Safety, Elsevier, vol. 213(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. Aizpurua, J.I. & Catterson, V.M. & Papadopoulos, Y. & Chiacchio, F. & D'Urso, D., 2017. "Supporting group maintenance through prognostics-enhanced dynamic dependability prediction," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 171-188.
    5. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    6. Zhang, Tieyao & Shuai, Jian & Shuai, Yi & Hua, Luoyi & Xu, Kui & Xie, Dong & Mei, Yuan, 2023. "Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Roy Assaf & Phuc Do & Samia Nefti-Meziani & Philip Scarf, 2018. "Wear rate–state interactions within a multi-component system: a study of a gearbox-accelerated life testing platform," Journal of Risk and Reliability, , vol. 232(4), pages 425-434, August.
    8. Jiang, Deyin & Chen, Tianyu & Xie, Juanzhang & Cui, Weimin & Song, Bifeng, 2023. "A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Song, Kai & Shi, Jian & Yi, Xiaojian, 2020. "A time-discrete and zero-adjusted gamma process model with application to degradation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    10. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    11. Thomas Michael Welte & Iver Bakken Sperstad & Espen Høegh Sørum & Magne Lorentzen Kolstad, 2017. "Integration of Degradation Processes in a Strategic Offshore Wind Farm O&M Simulation Model," Energies, MDPI, vol. 10(7), pages 1-18, July.
    12. Finkelstein, Maxim & Cha, Ji Hwan & Langston, Amy, 2023. "Improving classical optimal age-replacement policies for degrading items," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    13. Phuc Do & Christophe Bérenguer, 2022. "Residual life-based importance measures for predictive maintenance decision-making," Journal of Risk and Reliability, , vol. 236(1), pages 98-113, February.
    14. Wang, Xiaolin & Liu, Bin & Zhao, Xiujie, 2021. "A performance-based warranty for products subject to competing hard and soft failures," International Journal of Production Economics, Elsevier, vol. 233(C).
    15. Nicolai, R.P. & Frenk, J.B.G. & Dekker, R., 2007. "Modelling and Optimizing Imperfect Maintenance of Coatings on Steel Structures," ERIM Report Series Research in Management ERS-2007-043-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    16. Xiangang Cao & Pengfei Li & Song Ming, 2021. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven," Sustainability, MDPI, vol. 13(15), pages 1-19, July.
    17. Li, Mei & Liu, Zixian & Li, Xiaopeng & Liu, Yiliu, 2019. "Dynamic risk assessment in healthcare based on Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 327-334.
    18. Lin Wang & Zhiqiang Lu & Yifei Ren, 2019. "A rolling horizon approach for production planning and condition-based maintenance under uncertain demand," Journal of Risk and Reliability, , vol. 233(6), pages 1014-1028, December.
    19. Liang, Qingzhu & Yang, Yinghao & Peng, Changhong, 2023. "A reliability model for systems subject to mutually dependent degradation processes and random shocks under dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    20. Shen, Jingyuan & Cui, Lirong & Ma, Yizhong, 2019. "Availability and optimal maintenance policy for systems degrading in dynamic environments," European Journal of Operational Research, Elsevier, vol. 276(1), pages 133-143.

    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:214:y:2021:i:c:s0951832021002726. 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.