IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v242y2024ics095183202300635x.html
   My bibliography  Save this article

A probabilistic-driven framework for enhanced corrosion estimation of ship structural components

Author

Listed:
  • Woloszyk, Krzysztof
  • Garbatov, Yordan

Abstract

The work proposes a probabilistic-driven framework for enhanced corrosion estimation of ship structural components using Bayesian inference and limited measurement data. The new approach for modelling measurement uncertainty is proposed based on the results of previous corrosion tests that incorporate the non-uniform character of the corroded surface of structural components. The proposed framework's basic features are outlined, and the detailed algorithm is presented. Further, the proposed framework is validated by comparison with the classical statistical approach and mass measurements, considering previous experimental work results. Notably, the impact of the number of measuring points is investigated, and the accuracy index is proposed to identify the optimum number of measurements. The developed framework has a significant advantage over the classical approach since measuring uncertainty is incorporated. Additionally, the confidence intervals of both mean value corrosion depth and standard deviation could be gathered due to the probabilistic character of the framework. Thus, the presented approach can potentially be used in the structural health monitoring of ship structural components and reliability analysis.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s095183202300635x
    DOI: 10.1016/j.ress.2023.109721
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202300635X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109721?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. 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).
    3. Roubos, Alfred A. & Allaix, Diego L. & Schweckendiek, Timo & Steenbergen, Raphael D.J.M. & Jonkman, Sebastiaan N., 2020. "Time-dependent reliability analysis of service-proven quay walls subject to corrosion-induced degradation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. 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.
    5. Kim, Kyeongsu & Lee, Gunhak & Park, Keonhee & Park, Seongho & Lee, Won Bo, 2021. "Adaptive approach for estimation of pipeline corrosion defects via Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. 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.
    7. Vishwanath, B Sharanbaswa & Banerjee, Swagata, 2023. "Considering uncertainty in corrosion process to estimate life-cycle seismic vulnerability and risk of aging bridge piers," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    8. 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).
    9. Wang, Changxi & Elsayed, Elsayed A., 2020. "Stochastic modeling of corrosion growth," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Li, He & Guedes Soares, C, 2022. "Assessment of failure rates and reliability of floating offshore wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    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. 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).
    2. 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).
    3. 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).
    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, 2023. "Dynamic Bayesian network model to study under-deposit corrosion," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Li, Xinhong & Jia, Ruichao & Zhang, Renren & Yang, Shangyu & Chen, Guoming, 2022. "A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Miao, Xingyuan & Zhao, Hong, 2024. "Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    8. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    9. Vishwanath, B Sharanbaswa & Banerjee, Swagata, 2023. "Considering uncertainty in corrosion process to estimate life-cycle seismic vulnerability and risk of aging bridge piers," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    10. 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).
    11. 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.
    12. Gao, Dawei & Huang, Kai & Zhu, Yongsheng & Zhu, Linbo & Yan, Ke & Ren, Zhijun & Guedes Soares, C., 2024. "Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Ivana Racetin & Nives Ostojić Škomrlj & Marina Peko & Mladen Zrinjski, 2023. "Fuzzy Multi-Criteria Decision for Geoinformation System-Based Offshore Wind Farm Positioning in Croatia," Energies, MDPI, vol. 16(13), pages 1-18, June.
    14. 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).
    15. Huang, Peng & Gu, Yingkui & Li, He & Yazdi, Mohammad & Qiu, Guangqi, 2023. "An Optimal Tolerance Design Approach of Robot Manipulators for Positioning Accuracy Reliability," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    16. 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.
    17. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    18. Ji, Ziguang & Chen, Yi & Ma, Xiaobing & Cai, Yikun & Yang, Li, 2024. "Hierarchical condition-based maintenance planning for corrosion process considering natural environmental impact," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    19. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    20. Ghigo, Alberto & Faraggiana, Emilio & Giorgi, Giuseppe & Mattiazzo, Giuliana & Bracco, Giovanni, 2024. "Floating Vertical Axis Wind Turbines for offshore applications among potentialities and challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).

    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:242:y:2024:i:c:s095183202300635x. 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.