Loading [a11y]/accessibility-menu.js
A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models | IEEE Journals & Magazine | IEEE Xplore

A Set-Theoretic Method for Parametric Uncertainty Analysis in Markov Reliability and Reward Models


Abstract:

This paper proposes a set-theoretic method to capture the effect of parametric uncertainty in reliability and performability indices obtained from Markov reliability and ...Show More

Abstract:

This paper proposes a set-theoretic method to capture the effect of parametric uncertainty in reliability and performability indices obtained from Markov reliability and reward models. We assume that model parameters, i.e., component failure and repair rates, are not perfectly known, except for upper and lower bounds obtained from engineering judgment or field data. Thus, the values that these parameters can take are constrained to lie within a set. In our method, we first construct a minimum volume ellipsoid that upper bounds this set, and hence contains all possible values that the parameters can take. This ellipsoid is then propagated via set operations through a second-order Taylor series expansion of the Markov chain stationary distribution, resulting in a set that provides approximate bounds on reliability and performability indices of interest. Case studies pertaining to a two-component shared load system with common-cause failures, and preventative maintenance of an electric-power distribution transformer, are presented.
Published in: IEEE Transactions on Reliability ( Volume: 62, Issue: 3, September 2013)
Page(s): 658 - 669
Date of Publication: 02 July 2013

ISSN Information:


References

References is not available for this document.