Importance and sensitivity analysis in assessing system reliability

Gandini, A.

doi:10.1109/24.52613

Cited by 72 publications

(31 citation statements)

References 5 publications

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“…Yet, there are many systems, such as, for example, the manufacturing, production, power generation and gas and oil transportation ones, whose overall performance can settle on different levels (e.g. 100%, 80%, 50% of the nominal capacity), depending on the operative conditions of their constitutive multi-state elements ( Figure 1) (Wood, 1985;Garribba et al, 1985;Gandini, 1990;Aven, 1993;Griffith, 1980;Lisnianski and Levitin, 2003;Parikh et al 2001 . If the probability that at time t the system is in state j is denoted by ) (t P j , the availability reads:…”

Section: Multi-state Systemsmentioning

confidence: 99%

Reliability engineering: Old problems and new challenges

Zio

2009

Reliability Engineering & System Safety

699

355

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Section: Multi-state Systemsmentioning

confidence: 99%

Reliability engineering: Old problems and new challenges

Zio

2009

Reliability Engineering & System Safety

699

355

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“…Equation (10) allows for the evaluation of the system availability sensitivity in any direction of interest at time t in the transient state. A similar formula evaluation has been shown in [1] by using the generalized perturbation theory (GPT) method in the context of Makovian systems.…”

Section: Transient Statementioning

confidence: 98%

Importance measure on finite time horizon and application to Markovian multistate production systems

Van

Barros

Bérenguer

2008

Proceedings of the Institution of Mechanical Engineers, Part O:

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The sensitivity analysis of complex industrial systems aims at identifying, in a multi-unit structure, which components contribute the most to a variation of the performance criterion. In this paper an importance measure, called multi-directional sensitivity measure, defined as the derivative of the system performance in the direction defined by a vector (or matrix) in an appropriate space (e.g. direction of one parameter, of a group of parameters, or generally any direction of the transition rates of a Markov process) is considered. This measure proposed for sensitivity analysis of steady state reliability is developed herein for the transient state. It is also extended and applied to the study of the production capacity of multi-state production systems such as manufacturing, production lines, and power generation, which exhibit performances that can settle on different levels depending on the operative conditions of the constitutive components. A simple numerical example is introduced to show why this measure provides an efficient tool to investigate not only the importance of a given component, but also the importance of a class of components, the importance of the maintenance and, more generally, the effect of the simultaneous change of several 1 design parameters.

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“…A aplicação de GPT em análise de confiabilidade, baseando-se em Cadeias de Markov,é discutida em [1]. A frequência de acidente de uma planta equipada com um canal de proteção em função da taxa de demanda do sistemaé analisada em [3].…”

Section: Introductionunclassified