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Likelihood ratio gradient estimation for dynamic reliability applications

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  • Li, Jinghui
  • Mosleh, Ali
  • Kang, Rui

Abstract

This paper investigates the issue of performing a first-order sensitivity analysis in the setting of dynamic reliability. The likelihood ratio (LR) derivative/gradient estimation method is chosen to fulfill the mission. Its formulation and implementation in the system-based Monte Carlo approach that is commonly used in dynamic reliability applications is first given. To speed up the simulation, we then apply the LR method within the framework of Z-VISA, a biasing (or importance sampling) method we have developed recently. A widely discussed dynamic reliability example (a holdup tank) is studied to test the effectiveness and behaviors of the LR method when applied to dynamic reliability problems and also the effectiveness of the Z-VISA biasing technique for reducing the variance of LR derivative estimators.

Suggested Citation

  • Li, Jinghui & Mosleh, Ali & Kang, Rui, 2011. "Likelihood ratio gradient estimation for dynamic reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1667-1679.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:12:p:1667-1679
    DOI: 10.1016/j.ress.2011.08.001
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    References listed on IDEAS

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    Cited by:

    1. Isadora Antoniano‐Villalobos & Emanuele Borgonovo & Sumeda Siriwardena, 2018. "Which Parameters Are Important? Differential Importance Under Uncertainty," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2459-2477, November.
    2. Xing Pan & Lunhu Hu & Ziling Xin & Shenghan Zhou & Yanmei Lin & Yong Wu, 2018. "Risk Scenario Generation Based on Importance Measure Analysis," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    3. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    5. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. de Saporta, Benoîte & Zhang, Huilong, 2013. "Predictive maintenance for the heated hold-up tank," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 82-90.

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