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Reliability analysis of continuous emission monitoring system with common cause failure based on fuzzy FMECA and Bayesian networks

Author

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  • Yuan-Jian Yang

    (Chongqing University of Science and Technology)

  • Ya-Lan Xiong

    (Chongqing University of Science and Technology)

  • Xin-Yin Zhang

    (Chongqing University of Science and Technology)

  • Gui-Hua Wang

    (Chongqing University of Science and Technology)

  • Bihai Zou

    (Chongqing University of Science and Technology)

Abstract

Continuous emission monitoring system (CEMS) has been widely used in many engineering fields. Common cause failures (CCFs) have remarkable effects on the system reliability of CEMS, because of shared work conditions and dependent failures for different components. A method for reliability evaluation of CEMS with CCFs is proposed based on fuzzy Failure Mode Effects and Criticality Analysis (FMECA) as well as Bayesian network (BN). By introducing the system composition and function principles of CEMS, the CEMS failure mode is clearly defined and the weak components of the system are identified. According to the hazard ranking of the CEMS failure modes, the places where reliability improvement or preventive maintenance should be implemented are found out. Then, BN-based reliability model of the sampling system, which is the weakest subsystem of CEMS, is constructed according to the results of a fault tree analysis. The behavior of CCF is further incorporated, and the α-factor model is used to evaluate the probability of CCF. Lastly, a numerical example is used to illustrate the proposed method. A comparison between the proposed method and the one without considering CCF is carried out. The result demonstrates that the proposed method has better reliability assessment accuracy for the CEMS with CCF than the one without considering CCF.

Suggested Citation

  • Yuan-Jian Yang & Ya-Lan Xiong & Xin-Yin Zhang & Gui-Hua Wang & Bihai Zou, 2022. "Reliability analysis of continuous emission monitoring system with common cause failure based on fuzzy FMECA and Bayesian networks," Annals of Operations Research, Springer, vol. 311(1), pages 451-467, April.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03234-x
    DOI: 10.1007/s10479-019-03234-x
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    References listed on IDEAS

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    1. Samitha Khaiyum & Y.S. Kumaraswamy & K. Karibasappa, 2016. "Pattern analysis of phase wise occurrence, severity and detection of failures in real time embedded projects," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 17(1), pages 36-60.
    2. Zhang, Xiaoqiang & Gao, Huiying & Huang, Hong-Zhong & Li, Yan-Feng & Mi, Jinhua, 2018. "Dynamic reliability modeling for system analysis under complex load," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 345-351.
    3. Mi, Jinhua & Li, Yan-Feng & Yang, Yuan-Jian & Peng, Weiwen & Huang, Hong-Zhong, 2016. "Reliability assessment of complex electromechanical systems under epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 1-15.
    4. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
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    Cited by:

    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).

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