Article ID: | iaor20117591 |
Volume: | 96 |
Issue: | 10 |
Start Page Number: | 1386 |
End Page Number: | 1395 |
Publication Date: | Oct 2011 |
Journal: | Reliability Engineering and System Safety |
Authors: | Mahadevan Sankaran, Bichon Barron J, McFarland John M |
Keywords: | estimation, gaussian processes, system failure |
Despite many advances in the field of computational reliability analysis, the efficient estimation of the reliability of a system with multiple failure modes remains a persistent challenge. Various sampling and analytical methods are available, but they typically require accepting a tradeoff between accuracy and computational efficiency. In this work, a surrogate‐based approach is presented that simultaneously addresses the issues of accuracy, efficiency, and unimportant failure modes. The method is based on the creation of Gaussian process surrogate models that are required to be locally accurate only in the regions of the component limit states that contribute to system failure. This approach to constructing surrogate models is demonstrated to be both an efficient and accurate method for system‐level reliability analysis.