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Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network

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  • Kohda, Takehisa
  • Cui, Weimin

Abstract

To prevent an abnormal event from leading to an accident, the role of its safety monitoring system is very important. The safety monitoring system detects symptoms of an abnormal event to mitigate its effect at its early stage. As the operation time passes by, the sensor reliability decreases, which implies that the decision criteria of the safety monitoring system should be modified depending on the sensor reliability as well as the system reliability. This paper presents a framework for the decision criteria (or diagnosis logic) of the safety monitoring system. The logic can be dynamically modified based on sensor output data monitored at regular intervals to minimize the expected loss caused by two types of safety monitoring system failure events: failed-dangerous (FD) and failed-safe (FS). The former corresponds to no response under an abnormal system condition, while the latter implies a spurious activation under a normal system condition. Dynamic Bayesian network theory can be applied to modeling the entire system behavior composed of the system and its safety monitoring system. Using the estimated state probabilities, the optimal decision criterion is given to obtain the optimal diagnosis logic. An illustrative example of a three-sensor system shows the merits and characteristics of the proposed method, where the reasonable interpretation of sensor data can be obtained.

Suggested Citation

  • Kohda, Takehisa & Cui, Weimin, 2007. "Risk-based reconfiguration of safety monitoring system using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1716-1723.
  • Handle: RePEc:eee:reensy:v:92:y:2007:i:12:p:1716-1723
    DOI: 10.1016/j.ress.2006.09.012
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    Cited by:

    1. Matellini, D.B. & Wall, A.D. & Jenkinson, I.D. & Wang, J. & Pritchard, R., 2013. "Modelling dwelling fire development and occupancy escape using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 75-91.
    2. Chuan Wang & Yupeng Liu & Wen Hou & Chao Yu & Guorong Wang & Yuyan Zheng, 2021. "Reliability and availability modeling of Subsea Autonomous High Integrity Pressure Protection System with partial stroke test by Dynamic Bayesian," Journal of Risk and Reliability, , vol. 235(2), pages 268-281, April.
    3. Luo, Pengcheng & Hu, Yang, 2013. "System risk evolution analysis and risk critical event identification based on event sequence diagram," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 36-44.
    4. Nima Khakzad, 2018. "Which Fire to Extinguish First? A Risk‐Informed Approach to Emergency Response in Oil Terminals," Risk Analysis, John Wiley & Sons, vol. 38(7), pages 1444-1454, July.
    5. 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).
    6. Li, Mei & Liu, Zixian & Li, Xiaopeng & Liu, Yiliu, 2019. "Dynamic risk assessment in healthcare based on Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 327-334.
    7. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.
    8. Amin, Md. Tanjin & Khan, Faisal & Imtiaz, Syed, 2018. "Dynamic availability assessment of safety critical systems using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 108-117.
    9. Dante B Matellini & Alan D Wall & Ian D Jenkinson & Jin Wang & Robert W Pritchard, 2013. "A study of human reaction during the initial stages of a dwelling fire using a Bayesian network model," Journal of Risk and Reliability, , vol. 227(2), pages 207-221, April.
    10. Khakzad, Nima, 2015. "Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 263-272.
    11. 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).
    12. Zhong, Shengtong & Langseth, Helge & Nielsen, Thomas Dyhre, 2014. "A classification-based approach to monitoring the safety of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 61-71.
    13. D. B. Matellini & A. D. Wall & I. D. Jenkinson & J. Wang & R. Pritchard, 2018. "A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property," Risk Analysis, John Wiley & Sons, vol. 38(10), pages 2087-2104, October.
    14. Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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