IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v248y2024ics0951832024002278.html
   My bibliography  Save this article

Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin

Author

Listed:
  • Tao, Haohan
  • Jia, Peng
  • Wang, Xiangyu
  • Wang, Liquan

Abstract

The reliability evaluation of the subsea control module(SCM) is the key to ensure the safety and stability of subsea oil-gas production. The failure probability and reliability of SCM components are dependent on time and working conditions. To analyze the SCMs’ reliability considering varying working conditions, this paper proposed a new digital twin and dynamic Bayesian network(DBN) based model utilizing historical working condition data in reliability analysis. In the proposed framework, critical working condition data is obtained by sensor-based Digital Twin(DT) simulation and used for dynamically updating the parameters in the DBN reliability analysis model. The reliability evaluation of an actual SCM electric system was carried out. The results revealed the most probable failure mode and the most vulnerable components in the system. Finally, the fault prediction based on the back-forward analysis capacity of the proposed method was conducted to predict the probability of the faulty device when unexpected failures occur.

Suggested Citation

  • Tao, Haohan & Jia, Peng & Wang, Xiangyu & Wang, Liquan, 2024. "Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:reensy:v:248:y:2024:i:c:s0951832024002278
    DOI: 10.1016/j.ress.2024.110153
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024002278
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110153?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. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    2. Guo, Haitao & Yang, Xianhui, 2007. "A simple reliability block diagram method for safety integrity verification," Reliability Engineering and System Safety, Elsevier, vol. 92(9), pages 1267-1273.
    3. Xu, Jintao & Gui, Maolei & Ding, Rui & Dai, Tao & Zheng, Mengyan & Men, Xinhong & Meng, Fanpeng & Yu, Tao & Sui, Yang, 2023. "A new approach for dynamic reliability analysis of reactor protection system for HPR1000," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Athanasios J. Kolios & Anietie Umofia & Mahmood Shafiee, 2017. "Failure mode and effects analysis using a fuzzy-TOPSIS method: a case study of subsea control module," International Journal of Multicriteria Decision Making, Inderscience Enterprises Ltd, vol. 7(1), pages 29-53.
    5. 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).
    6. Cai, Baoping & Liu, Yonghong & Liu, Zengkai & Tian, Xiaojie & Dong, Xin & Yu, Shilin, 2012. "Using Bayesian networks in reliability evaluation for subsea blowout preventer control system," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 32-41.
    7. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    8. Xiong, Liujing & He, Yifan & Chen, Yuejian & Lu, Jinjun & Niu, Gang, 2023. "Digital twin-based degradation prediction for train electro-pneumatic valve," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    9. Volkanovski, Andrija & ÄŒepin, Marko & Mavko, Borut, 2009. "Application of the fault tree analysis for assessment of power system reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1116-1127.
    10. Shao, Xiaoyan & Cai, Baoping & Liu, Yonghong & Zhang, Junyan & Sui, Zhongfei & Feng, Qiang, 2023. "Remaining useful life prediction via a hybrid DBN-KF-based method: A case of subsea Christmas tree valves," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    11. Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    12. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    13. Wu, Shengnan & Zhang, Qiao & Li, Bin & Zhang, Laibin & Zheng, Wenpei & Li, Zhong & Li, Zhandong & Liu, Yiliu, 2023. "Reliability analysis of subsea wellhead system subject to fatigue and degradation during service life," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    14. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    15. Keshun, You & Guangqi, Qiu & Yingkui, Gu, 2024. "Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    16. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(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. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system considering common cause failure: Based on a beta-factor and continuous-time bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Caetano, Henrique O. & N., Luiz Desuó & Fogliatto, Matheus S.S. & Maciel, Carlos D., 2024. "Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. 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).
    4. Li, He & Deng, Zhi-Ming & Golilarz, Noorbakhsh Amiri & Guedes Soares, C., 2021. "Reliability analysis of the main drive system of a CNC machine tool including early failures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Ramadhani, Adhitya & Khan, Faisal & Colbourne, Bruce & Ahmed, Salim & Taleb-Berrouane, Mohammed, 2022. "Resilience assessment of offshore structures subjected to ice load considering complex dependencies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Zhou, Daoqing & Sun, C.P. & Du, Yi-Mu & Guan, Xuefei, 2022. "Degradation and reliability of multi-function systems using the hazard rate matrix and Markovian approximation," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    7. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Aleatory uncertainty quantification of project resources and its application to project scheduling," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    8. Wang, Jian & Gao, Shibin & Yu, Long & Ma, Chaoqun & Zhang, Dongkai & Kou, Lei, 2023. "A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    9. Bibartiu, Otto & Dürr, Frank & Rothermel, Kurt & Ottenwälder, Beate & Grau, Andreas, 2021. "Scalable k-out-of-n models for dependability analysis with Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. 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).
    11. Cai, Baoping & Zhang, Yanping & Wang, Haifeng & Liu, Yonghong & Ji, Renjie & Gao, Chuntan & Kong, Xiangdi & Liu, Jing, 2021. "Resilience evaluation methodology of engineering systems with dynamic-Bayesian-network-based degradation and maintenance," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    12. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C. & Ariffin, A.K. & Singh, S.S., 2021. "Evidence based risk analysis of fire and explosion accident scenarios in FPSOs," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    13. He, Zhichao & Wang, Yanhui & Xia, Weifu & Shen, Yue & Hao, Yucheng & Ren, Qiuyang, 2023. "A method for reliability assessment of complex electromechanical system based on improved network connectivity entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    14. Zerouali, Bilal & Sahraoui, Yacine & Nahal, Mourad & Chateauneuf, Alaa, 2024. "Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    15. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    16. 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).
    17. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    18. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    19. Kishore, Katchalla Bala & Gangolu, Jaswanth & Ramancha, Mukesh K. & Bhuyan, Kasturi & Sharma, Hrishikesh, 2022. "Performance-based probabilistic deflection capacity models and fragility estimation for reinforced concrete column and beam subjected to blast loading," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    20. Lijie, Chen & Tao, Tang & Xianqiong, Zhao & Schnieder, Eckehard, 2012. "Verification of the safety communication protocol in train control system using colored Petri net," Reliability Engineering and System Safety, Elsevier, vol. 100(C), pages 8-18.

    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:248:y:2024:i:c:s0951832024002278. 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.