Article2005

Convergence properties of an interval probabilistic approach to system reliability estimation

23 citations0/0 files storedInternational Journal of General Systems

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Probabilistic and Robust Engineering Design Reliability and Maintenance Optimization Bayesian Modeling and Causal Inference

Abstract

Based on a black box model of a complex system, and on intervals and probabilities describing the known information about the inputs, we want to estimate the system's reliability. This problem is motivated by a number of problem areas, most specifically in engineering reliability analysis under conditions of poor measurement and high complexity of system models. Using the results of tests performed on the system's computer model, we can estimate the lower and upper bounds of the probability that the system is in a desirable state. This is equivalent to using Monte-Carlo sampling to estimate cumulative belief and plausibility values of functionally propagated finite random intervals. In this paper, we prove that these estimates are correct in the sense that under reasonable assumptions, these estimates converge to the actual probability bounds.

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References

1994Advances in the Dempster-Shafer theory of evidence 2004Challenge problems: uncertainty in system response given uncertain parameters 2004An exploration of alternative approaches to the representation of uncertainty in model predictions 2004Alternative representations of epistemic uncertainty 2004An exploration of alternative approaches to the representation of uncertainty in model predictions 1991Evidence Theory and Its Applications 1986Arithmetic and other operations on Dempster-Shafer structures 2004Summary from the epistemic uncertainty workshop: consensus amid diversity

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