On the Marshall–Olkin Copula Model for Network Reliability Under Dependent Failures - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles IEEE Transactions on Reliability Year : 2019

On the Marshall–Olkin Copula Model for Network Reliability Under Dependent Failures

Abstract

The Marshall-Olkin (MO) copula model has emerged as the standard tool for capturing dependence between components in failure analysis in reliability. In this model, shocks arise at exponential random times, that affect one or several components inducing a natural correlation in the failure process. However, because the number of parameter of the model grows exponentially with the number of components, MO suffers of the "curse of di-mensionality." MO models are usually intended to be applied to design a network before its construction; therefore, it is natural to assume that only partial information about failure behavior can be gathered, mostly from similar existing networks. To construct such an MO model, we propose an optimization approach to define the shock's parameters in the MO copula, in order to match marginal failures probabilities and correlations between these failures. To deal with the exponential number of parameters of this problem, we use a column-generation technique. We also discuss additional criteria that can be incorporated to obtain a suitable model. Our computational experiments show that the resulting MO model produces a close estimation of the network reliability, especially when the correlation between component failures is significant.
Fichier principal
Vignette du fichier
J57.pdf (2.9 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01962918 , version 1 (21-12-2018)

Identifiers

Cite

Omar Matus, Javiera Barrera, Eduardo Moreno, Gerardo Rubino. On the Marshall–Olkin Copula Model for Network Reliability Under Dependent Failures. IEEE Transactions on Reliability, 2019, 68 (2), pp.451 - 461. ⟨10.1109/tr.2018.2865707⟩. ⟨hal-01962918⟩
86 View
273 Download

Altmetric

Share

Gmail Facebook X LinkedIn More