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Reliability evaluation method based on dynamic fault diagnosis results: A case study of a seabed mud lifting system

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  • Wang, Chuan
  • Liu, Yupeng
  • Wang, Dongbo
  • Wang, Guorong
  • Wang, Dingya
  • Yu, Chao

Abstract

In this paper, a reliability evaluation method based on dynamic fault diagnosis results is proposed. The feasibility of the method is verified by taking the seabed mud lifting system as an example. The method considers the influence of the degradation of sensors and system equipment on the diagnostic results. The problem of overdiagnosis in static diagnosis network is avoided. Fault monitoring stage to locate the fault of the mud lifting system, reliability assessment stage to use dynamic Bayesian reverse diagnosis and forward reasoning to calculate the reliability of the whole system. The reliability of the system with and without sensor degradation is analyzed. The effects of common cause failure, multi-state degradation, redundant design and sensor performance degradation on system reliability are analyzed. The influence of the failure of different comphonents on the reliability of the system is studied. The sensitivity of the system is analyzed and the importance sequence of different components in the system is given.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s095183202100291x
    DOI: 10.1016/j.ress.2021.107763
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    Cited by:

    1. Sahu, Atma Ram & Palei, Sanjay Kumar, 2022. "Fault analysis of dragline subsystem using Bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Wang, Jingyuan & Liu, Zhen & Wang, Jiahong & Long, Bing & Zhou, Xiuyun, 2022. "A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    3. 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.
    4. 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).
    5. Liao, Zengbu & Zhan, Keyi & Zhao, Hang & Deng, Yuntao & Geng, Jia & Chen, Xuefeng & Song, Zhiping, 2024. "Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    6. Xu, Jinjin & Wang, Rongxi & Liang, Zeming & Liu, Pengpeng & Gao, Jianmin & Wang, Zhen, 2023. "Physics-guided, data-refined fault root cause tracing framework for complex electromechanical system," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    7. Zhou, Siwei & Ye, Luyao & Xiong, Shengwu & Xiang, Jianwen, 2022. "Reliability analysis of dynamic fault trees with Priority-AND gates based on irrelevance coverage model," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    8. Liu, Zengkai & Ma, Qiang & Cai, Baoping & Shi, Xuewei & Zheng, Chao & Liu, Yonghong, 2022. "Risk coupling analysis of subsea blowout accidents based on dynamic Bayesian network and NK model," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).

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