Bayesian Reliability Analysis under Incomplete Information Using Evolutionary Algorithms

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.

• Type: Journal
• Year: 2010
• Where: SEAL
• Authors: Rupesh Kumar Srivastava, Kalyanmoy Deb
• Keywords: Aleatory Uncertainties, Machine Learning, Product Reliability, Reliability Estimate, SEAL 2010