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A new reliability allocation weight for reducing the occurrence of severe failure effects

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  • Kim, Kyungmee O.
  • Yang, Yoonjung
  • Zuo, Ming J.

Abstract

A reliability allocation weight is used during the early design stage of a system to apportion the system reliability requirement to its individual subsystems. Since some failures have serious effects on public safety, cost and environmental issues especially in a mission critical system, the failure effect must be considered as one of the important factors in determining the allocation weight. Previously, the risk priority number or the criticality number was used to consider the failure effect in the allocation weight. In this paper, we identify the limitations of the previous approach and propose a new allocation weight based on the subsystem failure severity and its relative frequency. An example is given to illustrate that the proposed method is more effective than the previous method for reducing the occurrence of the unacceptable failure effects in a newly designed system.

Suggested Citation

  • Kim, Kyungmee O. & Yang, Yoonjung & Zuo, Ming J., 2013. "A new reliability allocation weight for reducing the occurrence of severe failure effects," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 81-88.
    • Handle: RePEc:eee:reensy:v:117:y:2013:i:c:p:81-88
    DOI: 10.1016/j.ress.2013.04.002
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    References listed on IDEAS

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    1. Yalaoui, Alice & Chu, Chengbin & Châtelet, Eric, 2005. "Reliability allocation problem in a series–parallel system," Reliability Engineering and System Safety, Elsevier, vol. 90(1), pages 55-61.
    2. Yadav, Om Prakash & Singh, Nanua & Goel, Parveen S., 2006. "Reliability demonstration test planning: A three dimensional consideration," Reliability Engineering and System Safety, Elsevier, vol. 91(8), pages 882-893.
    3. Hu, Shenping & Fang, Quangen & Xia, Haibo & Xi, Yongtao, 2007. "Formal safety assessment based on relative risks model in ship navigation," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 369-377.
    4. Salazar, Daniel & Rocco, Claudio M. & Galván, Blas J., 2006. "Optimization of constrained multiple-objective reliability problems using evolutionary algorithms," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1057-1070.
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    Citations

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    Cited by:

    1. Ding, Rui & Liu, Zehua & Xu, Jintao & Meng, Fanpeng & Sui, Yang & Men, Xinhong, 2021. "A novel approach for reliability assessment of residual heat removal system for HPR1000 based on failure mode and effect analysis, fault tree analysis, and fuzzy Bayesian network methods," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Yadav, Om Prakash & Zhuang, Xing, 2014. "A practical reliability allocation method considering modified criticality factors," Reliability Engineering and System Safety, Elsevier, vol. 129(C), pages 57-65.
    3. Kim, Kyungmee O. & Zuo, Ming J., 2018. "Optimal allocation of reliability improvement target based on the failure risk and improvement cost," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 104-110.
    4. Yixiong Feng & Zhaoxi Hong & Jin Cheng & Likai Jia & Jianrong Tan, 2017. "Low Carbon-Oriented Optimal Reliability Design with Interval Product Failure Analysis and Grey Correlation Analysis," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
    5. Kuei-Hu Chang, 2016. "A novel reliability allocation approach using the OWA tree and soft set," Annals of Operations Research, Springer, vol. 244(1), pages 3-22, September.

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