IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v214y2021ics0951832021003227.html
   My bibliography  Save this article

Point process-based approaches for the reliability analysis of systems modeled by costly simulators

Author

Listed:
  • Perrin, G.

Abstract

This paper addresses the issue of guaranteeing the good functioning of physical systems using expensive simulators. More precisely, it is interested in the construction of bounds allowing to majorize with a specified confidence the probability of occurrence of undesired events. In this context, this paper presents two algorithms: a first one allowing to build a bound higher than this probability at a fixed number of simulator evaluations; a second one allowing to reduce as much as possible this bound by adding in an optimized way new simulator evaluations. The efficiency of these algorithms is finally illustrated through the analysis of several test functions.

Suggested Citation

  • Perrin, G., 2021. "Point process-based approaches for the reliability analysis of systems modeled by costly simulators," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:reensy:v:214:y:2021:i:c:s0951832021003227
    DOI: 10.1016/j.ress.2021.107799
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021003227
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107799?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. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Alban, Andres & Darji, Hardik A. & Imamura, Atsuki & Nakayama, Marvin K., 2017. "Efficient Monte Carlo methods for estimating failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 376-394.
    3. Perrin, G., 2016. "Active learning surrogate models for the conception of systems with multiple failure modes," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 130-136.
    4. Balesdent, Mathieu & Morio, Jérôme & Marzat, Julien, 2015. "Recommendations for the tuning of rare event probability estimators," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 68-78.
    5. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    6. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    7. Wang, Zeyu & Shafieezadeh, Abdollah, 2020. "On confidence intervals for failure probability estimates in Kriging-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    8. Zuniga, M. Munoz & Murangira, A. & Perdrizet, T., 2021. "Structural reliability assessment through surrogate based importance sampling with dimension reduction," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    9. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "Estimation of rare event probabilities in power transmission networks subject to cascading failures," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 9-20.
    10. Razaaly, Nassim & Congedo, Pietro Marco, 2020. "Extension of AK-MCS for the efficient computation of very small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    11. Xiao, Sinan & Oladyshkin, Sergey & Nowak, Wolfgang, 2020. "Reliability analysis with stratified importance sampling based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    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. Dhulipala, Somayajulu L.N. & Shields, Michael D. & Chakroborty, Promit & Jiang, Wen & Spencer, Benjamin W. & Hales, Jason D. & Labouré, Vincent M. & Prince, Zachary M. & Bolisetti, Chandrakanth & Che, 2022. "Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Chen, Xuyong & Xu, Zhifeng & Wu, Yushun & Wu, Qiaoyun, 2023. "Heuristic algorithms for reliability estimation based on breadth-first search of a grid tree," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    3. Teixeira, Rui & Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan, 2021. "Reliability analysis using a multi-metamodel complement-basis approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Jakeman, John D. & Kouri, Drew P. & Huerta, J. Gabriel, 2022. "Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. 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).
    6. Chiron, Marie & Genest, Christian & Morio, Jérôme & Dubreuil, Sylvain, 2023. "Failure probability estimation through high-dimensional elliptical distribution modeling with multiple importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Wei, Pengfei & Zheng, Yu & Fu, Jiangfeng & Xu, Yuannan & Gao, Weikai, 2023. "An expected integrated error reduction function for accelerating Bayesian active learning of failure probability," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    9. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Perrin, G., 2020. "Adaptive calibration of a computer code with time-series output," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    11. Liu, Yushan & Li, Luyi & Zhao, Sihan & Song, Shufang, 2021. "A global surrogate model technique based on principal component analysis and Kriging for uncertainty propagation of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    12. El Masri, Maxime & Morio, Jérôme & Simatos, Florian, 2021. "Improvement of the cross-entropy method in high dimension for failure probability estimation through a one-dimensional projection without gradient estimation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Garbuno-Inigo, A. & DiazDelaO, F.A. & Zuev, K.M., 2016. "Gaussian process hyper-parameter estimation using Parallel Asymptotically Independent Markov Sampling," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 367-383.
    14. Chen, Jiahui & Chen, Zhicheng & Xu, Yang & Li, Hui, 2021. "Efficient reliability analysis combining kriging and subset simulation with two-stage convergence criterion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    15. Song, Kunling & Zhang, Yugang & Shen, Linjie & Zhao, Qingyan & Song, Bifeng, 2021. "A failure boundary exploration and exploitation framework combining adaptive Kriging model and sample space partitioning strategy for efficient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    17. Jingkui Li & Wenqi Liu & Yan Zhou & Zhandong Li, 2023. "An active learning Kriging-based method combining the weight information entropy function and the adaptive candidate sample pool," Journal of Risk and Reliability, , vol. 237(4), pages 741-751, August.
    18. Vanslette, Kevin & Tohme, Tony & Youcef-Toumi, Kamal, 2020. "A general model validation and testing tool," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    19. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    20. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.

    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:eee:reensy:v:214:y:2021:i:c:s0951832021003227. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.