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Reliability assessment using stochastic local regression

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  • Seung-Kyum Choi

Abstract

A primary challenge of stochastic analysis is to discover rigorous ways to estimate the low probability of failure which is critical to reliability constraints. In this paper, a new framework is proposed for the improved estimation of the low failure probability. Combining the significant advantages of the polynomial chaos expansion, Karhunen-Loeve transform and local regression method will result in a new simulation-based modelling technique that enables the accuracy of the structural integrity prediction. The proposed procedure can allow for realistic modelling of sophisticated statistical variations and facilitate in order to achieve improved reliability by eliminating unnecessary conservative approximations. Several specific examples including a three-bar truss and an unmanned undersea vehicle are depicted to illustrate how the method is used to provide a quantitative basis for developing robust designs associated with the low probability of failure.

Suggested Citation

  • Seung-Kyum Choi, 2009. "Reliability assessment using stochastic local regression," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 3(1/2/3), pages 267-285.
    • Handle: RePEc:ids:ijrsaf:v:3:y:2009:i:1/2/3:p:267-285
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