IDEAS home Printed from https://ideas.repec.org/a/ids/ijrsaf/v9y2015i2-3p174-190.html
   My bibliography  Save this article

Targeted random sampling: a new approach for efficient reliability estimation for complex systems

Author

Listed:
  • Michael D. Shields
  • V.S. Sundar

Abstract

A novel approach, targeted random sampling, is presented for estimating failure probabilities for systems with complex limit states. The method, underpinned by the refined stratified sampling concept by Shields et al., refines the sampling strata in the vicinity of the limit state to concentrate samples near the limit state and accurately resolve the failure domain in a very small number of samples - even for problems with strongly non-linear limit states. The method is compared with importance sampling and subset simulation. It produces very accurate estimates for complex problems where the importance sampling density is difficult, or impossible, to identify and is shown to converge much more rapidly than subset simulation for problems with moderate dimension, producing very accurate estimates with greatly reduced coefficient of variation in a fraction of the number of samples. Some challenges in the method are discussed including the extension to high dimensional reliability assessment.

Suggested Citation

  • Michael D. Shields & V.S. Sundar, 2015. "Targeted random sampling: a new approach for efficient reliability estimation for complex systems," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 9(2/3), pages 174-190.
    • Handle: RePEc:ids:ijrsaf:v:9:y:2015:i:2/3:p:174-190
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=72718
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shields, Michael D. & Zhang, Jiaxin, 2016. "The generalization of Latin hypercube sampling," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 96-108.
    2. Shields, Michael D., 2018. "Adaptive Monte Carlo analysis for strongly nonlinear stochastic systems," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 207-224.

    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:ids:ijrsaf:v:9:y:2015:i:2/3:p:174-190. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=98 .

    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.