Asymptotic optimality of RESTART estimators in highly dependable systems

Villén Altamirano, José (2014). Asymptotic optimality of RESTART estimators in highly dependable systems. "Reliability Engineering & System Safety", v. 130 ; pp. 115-124. ISSN 0951-8320. https://doi.org/10.1016/j.ress.2014.05.012.

Description

Title: Asymptotic optimality of RESTART estimators in highly dependable systems
Author/s:
  • Villén Altamirano, José
Item Type: Article
Título de Revista/Publicación: Reliability Engineering & System Safety
Date: October 2014
ISSN: 0951-8320
Volume: 130
Subjects:
Freetext Keywords: Rare event, RESTART simulation,subset simulation, reliability, HRMS systems,asymptotic optimality
Faculty: E.T.S.I. de Sistemas Informáticos (UPM)
Department: Matemática Aplicada a las Tecnologías de la Información y las Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Abstract We consider a wide class of models that includes the highly reliable Markovian systems (HRMS) often used to represent the evolution of multi-component systems in reliability settings. Repair times and component lifetimes are random variables that follow a general distribution, and the repair service adopts a priority repair rule based on system failure risk. Since crude simulation has proved to be inefficient for highly-dependable systems, the RESTART method is used for the estimation of steady-state unavailability and other reliability measures. In this method, a number of simulation retrials are performed when the process enters regions of the state space where the chance of occurrence of a rare event (e.g., a system failure) is higher. The main difficulty involved in applying this method is finding a suitable function, called the importance function, to define the regions. In this paper we introduce an importance function which, for unbalanced systems, represents a great improvement over the importance function used in previous papers. We also demonstrate the asymptotic optimality of RESTART estimators in these models. Several examples are presented to show the effectiveness of the new approach, and probabilities up to the order of 10-42 are accurately estimated with little computational effort.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainMYTM2011-28983-C03-03UnspecifiedUnspecifiedUnspecified
Madrid Regional GovernmentS2009/ESP-1685UnspecifiedUnspecifiedUnspecified

More information

Item ID: 37455
DC Identifier: http://oa.upm.es/37455/
OAI Identifier: oai:oa.upm.es:37455
DOI: 10.1016/j.ress.2014.05.012
Official URL: http://www.sciencedirect.com/science/article/pii/S0951832014001227
Deposited by: Memoria Investigacion
Deposited on: 09 Mar 2016 19:29
Last Modified: 09 Mar 2016 19:29
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