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

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  • 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
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    1. 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).
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    6. Chang, Yuanjiang & Wu, Xiangfei & Zhang, Changshuai & Chen, Guoming & Liu, Xiuquan & Li, Jiayi & Cai, Baoping & Xu, Liangbin, 2019. "Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 454-462.
    7. 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).
    8. 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).
    9. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    10. 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.
    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. 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).
    17. 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).
    18. 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.
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    3. Keramatinejad, Mahdi & Karbasian, Mahdi & Alimohammadi, Hamidreza & Atashgar, Karim, 2025. "A hybrid approach of adaptive surrogate model and sampling method for reliability assessment in multidisciplinary design optimization," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    4. Zhu, Enping & Li, Tao & Xiong, Jinbiao & Chai, Xiang & Zhang, Tengfei & Liu, Xiaojing, 2026. "A digital twin framework for real-time operation monitoring and its future prediction for space nuclear power," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).
    5. Gunjan, Shashi Bhushan & Srinivasu, D.S. & N, Ramesh Babu, 2025. "A new approach for product reliability prediction by considering the production factory lifecycle information," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
    6. Dong, Chenchen & Yang, Yu, 2025. "Dynamic risk-informed verification prioritization for Complex Product Systems: A tri-metric approach using a Multi-State Hierarchical Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 262(C).

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