IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v219y2022ics0951832021007092.html
   My bibliography  Save this article

A KPCA-BRANN based data-driven approach to model corrosion degradation of subsea oil pipelines

Author

Listed:
  • Li, Xinhong
  • Jia, Ruichao
  • Zhang, Renren
  • Yang, Shangyu
  • Chen, Guoming

Abstract

Corrosion is an important reason for the structural degradation of offshore oil pipelines, which may cause serious economic loss and environmental pollution. Nowadays the digitalized devices make a number of monitoring data become available. The prediction of corrosion degradation based on monitoring data becomes an efficient tool to prevent corrosion failure of offshore oil pipelines. This paper integrates KPCA and BRANN techniques to develop a novel data-driven model for corrosion degradation prediction of offshore oil pipelines. The model can eliminate the redundant information from the original monitoring data and improve the robustness by regularization constraints. KPCA is applied to reduce the dimension of the factors affecting pipeline corrosion, and the extracted principal components of corrosion variables are inputted in BRANN to build a corrosion degradation prediction model. The data with dimension reduction are divided into training set and validation set. The model is compared with BRANN alone and KPCA-LMANN model, which indicates KPCA-BRANN model presents superiority in the robustness and prediction accuracy (MSEÂ =Â 0.46%; R2=0.99). The proposed model can be used as an online prediction module of digitized process safety system, and support the reliability assessment and maintenance planning of corroded subsea pipelines.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s0951832021007092
    DOI: 10.1016/j.ress.2021.108231
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021007092
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.108231?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. Pietrantuono, Roberto & Popov, Peter & Russo, Stefano, 2020. "Reliability assessment of service-based software under operational profile uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    2. 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).
    3. Xiaoxu Chen & Linyuan Wang & Zhiyu Huang, 2020. "Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, September.
    4. Kexi Liao & Quanke Yao & Xia Wu & Wenlong Jia, 2012. "A Numerical Corrosion Rate Prediction Method for Direct Assessment of Wet Gas Gathering Pipelines Internal Corrosion," Energies, MDPI, vol. 5(10), pages 1-16, October.
    5. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    6. Chiachío, Juan & Jalón, María L. & Chiachío, Manuel & Kolios, Athanasios, 2020. "A Markov chains prognostics framework for complex degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. Wang, Changxi & Elsayed, Elsayed A., 2020. "Stochastic modeling of corrosion growth," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Chookah, M. & Nuhi, M. & Modarres, M., 2011. "A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1601-1610.
    10. Yang, Yongsheng & Khan, Faisal & Thodi, Premkumar & Abbassi, Rouzbeh, 2017. "Corrosion induced failure analysis of subsea pipelines," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 214-222.
    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. Cao, Bohan & Yin, Qishuai & Guo, Yingying & Yang, Jin & Zhang, Laibin & Wang, Zhenquan & Tyagi, Mayank & Sun, Ting & Zhou, Xu, 2023. "Field data analysis and risk assessment of shallow gas hazards based on neural networks during industrial deep-water drilling," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    2. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    3. Jiang, Fengyuan & Dong, Sheng, 2024. "Probabilistic-based burst failure mechanism analysis and risk assessment of pipelines with random non-uniform corrosion defects, considering the interacting effects," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. 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).
    5. Wang, Chang & Zheng, Jianqin & Liang, Yongtu & Wang, Bohong & Klemeš, Jiří Jaromír & Zhu, Zhu & Liao, Qi, 2022. "Deeppipe: An intelligent monitoring framework for operating condition of multi-product pipelines," Energy, Elsevier, vol. 261(PB).
    6. Zhou, Jie & Lin, Haifei & Li, Shugang & Jin, Hongwei & Zhao, Bo & Liu, Shihao, 2023. "Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    7. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Chen, Zhanfeng & Li, Xuyao & Wang, Wen & Li, Yan & Shi, Lei & Li, Yuxing, 2023. "Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    9. Jiang, Shengyu & He, Rui & Chen, Guoming & Zhu, Yuan & Shi, Jiaming & Liu, Kang & Chang, Yuanjiang, 2023. "Semi-supervised health assessment of pipeline systems based on optical fiber monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Jose E. Naranjo & Gustavo Caiza & Rommel Velastegui & Maritza Castro & Andrea Alarcon-Ortiz & Marcelo V. Garcia, 2022. "A Scoping Review of Pipeline Maintenance Methodologies Based on Industry 4.0," Sustainability, MDPI, vol. 14(24), pages 1-22, December.

    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. 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).
    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. 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).
    4. Lin, Yan-Hui & Li, Yan-Fu & Zio, Enrico, 2018. "A comparison between Monte Carlo simulation and finite-volume scheme for reliability assessment of multi-state physics systems," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 1-11.
    5. 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).
    6. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    7. Jin, Guang & Matthews, David E. & Zhou, Zhongbao, 2013. "A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 7-20.
    8. Bui, Ha & Sakurahara, Tatsuya & Pence, Justin & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "An algorithm for enhancing spatiotemporal resolution of probabilistic risk assessment to address emergent safety concerns in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 405-428.
    9. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    10. 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).
    11. 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).
    12. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    13. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    14. Zhang, Sen-Ju & Kang, Rui & Lin, Yan-Hui, 2021. "Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    15. Aalirezaei, Armin & Kabir, Dr. Golam & Khan, Md Saiful Arif, 2023. "Dynamic predictive analysis of the consequences of gas pipeline failures using a Bayesian network," International Journal of Critical Infrastructure Protection, Elsevier, vol. 43(C).
    16. Taleb-Berrouane, Mohammed & Khan, Faisal & Hawboldt, Kelly, 2021. "Corrosion risk assessment using adaptive bow-tie (ABT) analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    17. Liu, Jiaquan & Hou, Lei & Zhang, Rui & Sun, Xingshen & Yu, Qiaoyan & Yang, Kai & Zhang, Xinru, 2023. "Explainable fault diagnosis of oil-gas treatment station based on transfer learning," Energy, Elsevier, vol. 262(PA).
    18. Zhang, Y. & Weng, W.G. & Huang, Z.L., 2018. "A scenario-based model for earthquake emergency management effectiveness evaluation," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 197-207.
    19. 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).
    20. Dasgupta, Agnimitra & Johnson, Erik A., 2024. "REIN: Reliability Estimation via Importance sampling with Normalizing flows," Reliability Engineering and System Safety, Elsevier, vol. 242(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:219:y:2022:i:c:s0951832021007092. 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.