Bayesian reliability analysis under incomplete information using evolutionary algorithms

Authors:
Rupesh Kumar Srivastava;Kalyanmoy Deb
Affiliations:
Kanpur Genetic Algorithms Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, U.P., India;Kanpur Genetic Algorithms Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, U.P., India
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

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.