IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v236y2022i3p420-438.html
   My bibliography  Save this article

A new risk assessment method based on belief rule base and fault tree analysis

Author

Listed:
  • Hai-Long Zhu
  • Shan-Shan Liu
  • Yuan-Yuan Qu
  • Xiao-Xia Han
  • Wei He
  • You Cao

Abstract

Risk assessment methods are often used in complex industrial systems to avoid risks and reduce losses. The existing methods have not effectively solved the problems of lack of evaluation data and the interpretability of the entire evaluation process. This paper proposes a new risk assessment model based on the belief rule base (BRB) and Fault Tree Analysis (FTA). The FTA algorithm overcomes the difficulties of traditional BRB model in obtaining expert knowledge, clear indicators, and establishing logical relationships. This method establishes FTA rules based on the BRB model and expands the knowledge base through the FTA algorithm. A Bayesian network is applied as a conversion bridge between the FTA and BRB model. In addition, the model is optimized to reduce the uncertainty in the model. The method proposed is described by a case and its effectiveness is verified.

Suggested Citation

  • Hai-Long Zhu & Shan-Shan Liu & Yuan-Yuan Qu & Xiao-Xia Han & Wei He & You Cao, 2022. "A new risk assessment method based on belief rule base and fault tree analysis," Journal of Risk and Reliability, , vol. 236(3), pages 420-438, June.
  • Handle: RePEc:sae:risrel:v:236:y:2022:i:3:p:420-438
    DOI: 10.1177/1748006X211011457
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X211011457
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1748006X211011457?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
    ---><---

    References listed on IDEAS

    as
    1. Bani-Mustafa, Tasneem & Flage, Roger & Vasseur, Dominique & Zeng, Zhiguo & Zio, Enrico, 2020. "An extended method for evaluating assumptions deviations in quantitative risk assessment and its application to external flooding risk assessment of a nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. Langdalen, Henrik & Abrahamsen, Eirik Bjorheim & Abrahamsen, HÃ¥kon Bjorheim, 2020. "A New Framework To Idenitfy And Assess Hidden Assumptions In The Background Knowledge Of A Risk Assessment," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. Feng, Zhichao & Zhou, Zhijie & Hu, Changhua & Ban, Xiaojun & Hu, Guanyu, 2020. "A safety assessment model based on belief rule base with new optimization method," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. Kong, Guilan & Xu, Dong-Ling & Body, Richard & Yang, Jian-Bo & Mackway-Jones, Kevin & Carley, Simon, 2012. "A belief rule-based decision support system for clinical risk assessment of cardiac chest pain," European Journal of Operational Research, Elsevier, vol. 219(3), pages 564-573.
    5. Kjartan Bjørnsen & Anders Jensen & Terje Aven, 2020. "Using qualitative types of risk assessments in conjunction with FRAM to strengthen the resilience of systems," Journal of Risk Research, Taylor & Francis Journals, vol. 23(2), pages 153-166, February.
    Full references (including those not matched with items on IDEAS)

    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. Yuan Chen & Zhijie Zhou & Lihao Yang & Guanyu Hu & Xiaoxia Han & Shuaiwen Tang, 2022. "A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method," Journal of Risk and Reliability, , vol. 236(3), pages 458-476, June.
    2. Al-Ebbini, Lina & Oztekin, Asil & Chen, Yao, 2016. "FLAS: Fuzzy lung allocation system for US-based transplantations," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1051-1065.
    3. Cavalieri, Francesco & Franchin, Paolo & Giovinazzi, Sonia, 2023. "Multi-hazard assessment of increased flooding hazard due to earthquake-induced damage to the natural drainage system," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Pan, Yan & Jing, Yunteng & Wu, Tonghai & Kong, Xiangxing, 2021. "An Integrated Data and Knowledge Model Addressing Aleatory and Epistemic Uncertainty for Oil Condition Monitoring," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    5. Guilan Kong & Lili Jiang & Xiaofeng Yin & Tianbing Wang & Dong-Ling Xu & Jian-Bo Yang & Yonghua Hu, 2018. "Combining principal component analysis and the evidential reasoning approach for healthcare quality assessment," Annals of Operations Research, Springer, vol. 271(2), pages 679-699, December.
    6. Wan, Chengpeng & Yan, Xinping & Zhang, Di & Qu, Zhuohua & Yang, Zaili, 2019. "An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 222-240.
    7. Manlin Chen & Zhijie Zhou & Xiaoxia Han & Zhichao Feng, 2023. "A Text-Oriented Fault Diagnosis Method for Electromechanical Device Based on Belief Rule Base," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    8. Othman, Abdullah & El-Saoud, Waleed A. & Habeebullah, Turki & Shaaban, Fathy & Abotalib, Abotalib Z., 2023. "Risk assessment of flash flood and soil erosion impacts on electrical infrastructures in overcrowded mountainous urban areas under climate change," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    9. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    10. Li, Baode & Lu, Jing & Li, Jing & Zhu, Xuebin & Huang, Chuan & Su, Wan, 2022. "Scenario evolutionary analysis for maritime emergencies using an ensemble belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Maria-Teresa Bosch-Badia & Joan Montllor-Serrats & Maria-Antonia Tarrazon-Rodon, 2020. "Risk Analysis through the Half-Normal Distribution," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    12. Feng, Zhichao & Zhou, Zhijie & Hu, Changhua & Ban, Xiaojun & Hu, Guanyu, 2020. "A safety assessment model based on belief rule base with new optimization method," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    13. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.

    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:sae:risrel:v:236:y:2022:i:3:p:420-438. 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: SAGE Publications (email available below). General contact details of provider: .

    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.