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System reliability analysis using dominant failure modes identified by selective searching technique

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  • Kim, Dong-Seok
  • Ok, Seung-Yong
  • Song, Junho
  • Koh, Hyun-Moo

Abstract

The failure of a redundant structural system is often described by innumerable system failure modes such as combinations or sequences of local failures. An efficient approach is proposed to identify dominant failure modes in the space of random variables, and then perform system reliability analysis to compute the system failure probability. To identify dominant failure modes in the decreasing order of their contributions to the system failure probability, a new simulation-based selective searching technique is developed using a genetic algorithm. The system failure probability is computed by a multi-scale matrix-based system reliability (MSR) method. Lower-scale MSR analyses evaluate the probabilities of the identified failure modes and their statistical dependence. A higher-scale MSR analysis evaluates the system failure probability based on the results of the lower-scale analyses. Three illustrative examples demonstrate the efficiency and accuracy of the approach through comparison with existing methods and Monte Carlo simulations. The results show that the proposed method skillfully identifies the dominant failure modes, including those neglected by existing approaches. The multi-scale MSR method accurately evaluates the system failure probability with statistical dependence fully considered. The decoupling between the failure mode identification and the system reliability evaluation allows for effective applications to larger structural systems.

Suggested Citation

  • Kim, Dong-Seok & Ok, Seung-Yong & Song, Junho & Koh, Hyun-Moo, 2013. "System reliability analysis using dominant failure modes identified by selective searching technique," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 316-331.
  • Handle: RePEc:eee:reensy:v:119:y:2013:i:c:p:316-331
    DOI: 10.1016/j.ress.2013.02.007
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    References listed on IDEAS

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    1. Der Kiureghian, Armen & Song, Junho, 2008. "Multi-scale reliability analysis and updating of complex systems by use of linear programming," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 288-297.
    2. Kang, Won-Hee & Song, Junho & Gardoni, Paolo, 2008. "Matrix-based system reliability method and applications to bridge networks," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1584-1593.
    3. Lee, Young-Joo & Song, Junho, 2012. "Finite-element-based system reliability analysis of fatigue-induced sequential failures," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 131-141.
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    Citations

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    1. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.
    2. Tian, Yuxuan & Guan, Xiaoshu & Sun, Huabin & Bao, Yuequan, 2024. "An adaptive structural dominant failure modes searching method based on graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Byun, Ji-Eun & Song, Junho, 2020. "Efficient probabilistic multi-objective optimization of complex systems using matrix-based Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    4. Guan, Xiaoshu & Xiang, Zhengliang & Bao, Yuequan & Li, Hui, 2022. "Structural dominant failure modes searching method based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Glavind, Sebastian T. & Sepulveda, Juan G. & Faber, Michael H., 2022. "On a simple scheme for systems modeling and identification using big data techniques," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

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