2010 | OriginalPaper | Buchkapitel
Bayesian Reliability Analysis under Incomplete Information Using Evolutionary Algorithms
verfasst von : Rupesh Kumar Srivastava, Kalyanmoy Deb
Erschienen in: Simulated Evolution and Learning
Verlag: Springer Berlin Heidelberg
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During engineering design, it is often difficult to quantify product reliability because of insufficient data or information for modeling the uncertainties. In such cases, one needs a reliability estimate when the functional form of the uncertainty in the design variables or parameters cannot be found. In this work, a probabilistic method to estimate the reliability in such cases is implemented using Non-Dominated Sorting Genetic Algorithm-II. The method is then coupled with an existing RBDO method to solve a problem with both epistemic and aleatory uncertainties.