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A fault diagnosis method for the HIPPS of FPSO unit based on dynamic Bayesian network

Author

Listed:
  • Yu Han
  • Jianxing Yu
  • Chuan Wang
  • Xiaobo Xie
  • Chao Yu
  • Yupeng Liu

Abstract

The high integrity pressure protection system (HIPPS) on the Floating Production Storage and Offloading (FPSO) unit is essential for handling various emergencies. However, if the location of the fault cannot be accurately identified, proper measures may not be taken to isolate the hazard. This paper presents a fault diagnosis method for HIPPS on FPSO units based on Dynamic Bayesian network (DBN). The method considers the influence of sensor and system equipment degradation on the diagnosis results and avoids the problem of overdiagnosis in static diagnosis networks. Six fault diagnosis cases of the system are analyzed and discussed to verify the accuracy and effectiveness of the proposed method. By changing the failure rate of the faulty component, it is determined that the posterior probability of the faulty component increases with the increase of the failure rate at the same time.

Suggested Citation

  • Yu Han & Jianxing Yu & Chuan Wang & Xiaobo Xie & Chao Yu & Yupeng Liu, 2023. "A fault diagnosis method for the HIPPS of FPSO unit based on dynamic Bayesian network," Journal of Risk and Reliability, , vol. 237(4), pages 752-764, August.
  • Handle: RePEc:sae:risrel:v:237:y:2023:i:4:p:752-764
    DOI: 10.1177/1748006X221109347
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    References listed on IDEAS

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    1. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2011. "Modeling safety instrumented systems with MooN voting architectures addressing system reconfiguration for testing," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 545-563.
    2. Cai, Baoping & Liu, Yonghong & Fan, Qian & Zhang, Yunwei & Liu, Zengkai & Yu, Shilin & Ji, Renjie, 2014. "Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network," Applied Energy, Elsevier, vol. 114(C), pages 1-9.
    3. Jin, Hui & Rausand, Marvin, 2014. "Reliability of safety-instrumented systems subject to partial testing and common-cause failures," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 146-151.
    4. Wang, Chuan & Liu, Yupeng & Wang, Dongbo & Wang, Guorong & Wang, Dingya & Yu, Chao, 2021. "Reliability evaluation method based on dynamic fault diagnosis results: A case study of a seabed mud lifting system," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    5. Li, Zhuochao & Zhang, Haoran & Meng, Jing & Long, Yin & Yan, Yamin & Li, Meixuan & Huang, Zhongliang & Liang, Yongtu, 2020. "Reducing carbon footprint of deep-sea oil and gas field exploitation by optimization for Floating Production Storage and Offloading," Applied Energy, Elsevier, vol. 261(C).
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