IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v311y2022i1d10.1007_s10479-019-03234-x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-019-03234-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-019-03234-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ying-Kui Gu & Chao-Jun Fan & Ling-Qiang Liang & Jun Zhang, 2022. "Reliability calculation method based on the Copula function for mechanical systems with dependent failure," Annals of Operations Research, Springer, vol. 311(1), pages 99-116, April.
    2. Yan-Feng Li & Hong-Zhong Huang & Jinhua Mi & Weiwen Peng & Xiaomeng Han, 2022. "Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability," Annals of Operations Research, Springer, vol. 311(1), pages 195-209, April.
    3. Guang-Jun Jiang & Hong-Xia Chen & Le Gao & Hong-Hua Sun & Qing-Yang Li, 2022. "Reliability analysis on ammonium nitrate/fuel oil explosive vehicle pharmaceutical system based on dynamic fault tree and Bayesian network," Annals of Operations Research, Springer, vol. 311(1), pages 167-182, April.
    4. Jie Zhou & Hong-Zhong Huang & Yan-Feng Li & Junyu Guo, 2022. "A framework for fatigue reliability analysis of high-pressure turbine blades," Annals of Operations Research, Springer, vol. 311(1), pages 489-505, April.
    5. Jianxiong Gao & Zongwen An & Xuezong Bai, 2022. "A new representation method for probability distributions of multimodal and irregular data based on uniform mixture model," Annals of Operations Research, Springer, vol. 311(1), pages 81-97, April.
    6. Zheng Liu & Xin Liu & Hong-Zhong Huang & Pingyu Zhu & Zhongwei Liang, 2022. "A new inherent reliability modeling and analysis method based on imprecise Dirichlet model for machine tool spindle," Annals of Operations Research, Springer, vol. 311(1), pages 295-310, April.
    7. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Wang, Ning, 2024. "Algorithms for Bayesian network modeling and reliability inference of complex multistate systems with common cause failure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Mi, Jinhua & Beer, Michael & Li, Yan-Feng & Broggi, Matteo & Cheng, Yuhua, 2020. "Reliability and importance analysis of uncertain system with common cause failures based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    9. Jinhua Mi & Yuhua Cheng & Yufei Song & Libing Bai & Kai Chen, 2022. "Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions," Annals of Operations Research, Springer, vol. 311(1), pages 311-333, April.
    10. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    11. Zeng, Ying & Huang, Tudi & Li, Yan-Feng & Huang, Hong-Zhong, 2023. "Reliability modeling for power converter in satellite considering periodic phased mission," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    12. Song, Yufei & Mi, Jinhua & Cheng, Yuhua & Bai, Libing & Chen, Kai, 2020. "A dependency bounds analysis method for reliability assessment of complex system with hybrid uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    13. Yong-Hua Li & Fu-Yu Zhao & Yue-Hua Gao & Peng-Peng Zhi, 2022. "Importance analysis of underframe connection system for the pantograph lower arm rod," Annals of Operations Research, Springer, vol. 311(1), pages 211-223, April.
    14. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    15. Zheng, Xiaohu & Yao, Wen & Xu, Yingchun & Chen, Xianqi, 2019. "Improved compression inference algorithm for reliability analysis of complex multistate satellite system based on multilevel Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 123-142.
    16. Mi, Jinhua & Lu, Ning & Li, Yan-Feng & Huang, Hong-Zhong & Bai, Libing, 2022. "An evidential network-based hierarchical method for system reliability analysis with common cause failures and mixed uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    17. Li, Xiang-Yu & Xiong, Xiaoyan & Guo, Junyu & Huang, Hong-Zhong & Li, Xiaopeng, 2022. "Reliability assessment of non-repairable multi-state phased mission systems with backup missions," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    18. Li, Xiang-Yu & Li, Yan-Feng & Huang, Hong-Zhong & Zio, Enrico, 2018. "Reliability assessment of phased-mission systems under random shocks," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 352-361.
    19. 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.
    20. Ning Wang & Zhuo Zhang & Jiao Zhao & Dawei Hu, 2022. "Recognition method of equipment state with the FLDA based Mahalanobis–Taguchi system," Annals of Operations Research, Springer, vol. 311(1), pages 417-435, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-019-03234-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.