Importance of Components for a System

Ebrahimi, Nader; Jalali, Nima; Soofi, Ehsan S.; Soyer, Refik

doi:10.1080/07474938.2013.807652

Cited by 10 publications

(15 citation statements)

References 27 publications

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“…Thus, C i is more (less) important for a series system than for a parallel system whenever > .</ median lifetime; The notion of information importance is also applicable to component and system lifetimes as continuous random variables. As shown in [17], it is possible to develop results for convolutions and order statistics. For convolutions, results in entropy ordering can be used to obtain component importance ordering.…”

Section: Examplementioning

confidence: 99%

“…Ebrahimi et al [17] noted that reliability importance can be interpreted in terms of how knowledge of status of a component changes our knowledge of the system. In other words, their interpretation is in terms of which component's status knowledge matters most in reducing our uncertainty about systems' status.…”

Section: Reliability Importance Of System Componentsmentioning

confidence: 99%

“…Ebrahimi et al [17] also considered an entropy-based importance measure using the Maximal Data Information Prior (MDIP) criterion proposed by Zellner [30]. The MDIP criterion which was originally proposed for developing priors for Bayesian inference, provides the same importance ordering of the components as the mutual information.…”

Section: Examplementioning

confidence: 99%

See 2 more Smart Citations

Information Theory and Bayesian Reliability Analysis: Recent Advances

Ebrahimi

Soofi

Soyer

2014

Contributions to Statistics

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Section: Examplementioning

confidence: 99%

Section: Reliability Importance Of System Componentsmentioning

confidence: 99%

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Information Theory and Bayesian Reliability Analysis: Recent Advances

Ebrahimi

Soofi

Soyer

2014

Contributions to Statistics

Self Cite

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“…We conclude this section with an observation. Recently, Ebrahimi et al . define a new importance measure of components and basic events based on the Kullback–Leibler divergence between the conditional and unconditional system failure probability functions.…”

Section: A Value‐of‐information‐based Importance Measurementioning

confidence: 99%

“…The entropy‐based importance of Ref. or the Birnbaum and Barlow–Proshan importance measures, as well as other importance measures, such as the differential, criticality, and Fussell‐Vesely, are measures of value sensitivity, insofar as they account for the change in (or contribution to) the value of the risk metric without explicit reference to changes in the preferred alternative. The decision worth differs from the above‐mentioned importance measures because it explicitly compares different alternatives and is therefore a decision‐sensitivity measure.…”

Section: A Value‐of‐information‐based Importance Measurementioning

confidence: 99%

Deciding with Thresholds: Importance Measures and Value of Information

Borgonovo

Cillo

2017

Risk Analysis

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Risk-informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value-of-information-based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.

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Component importance based on dependence measures

Hellmich

2017

Math Meth Oper Res

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We discuss the construction of component importance measures for binary coherent reliability systems from known stochastic dependence measures by measuring the dependence between system and component failures. We treat both the time-dependent case in which the system and its components are described by binary random variables at a fixed instant as well as the continuous time case where the system and component life times are random variables. As dependence measures we discuss covariance and mutual information, the latter being based on Shannon entropy. We prove some basic properties of the resulting importance measures and obtain results on importance ordering of components.

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Importance of Components for a System

Cited by 10 publications

References 27 publications

Information Theory and Bayesian Reliability Analysis: Recent Advances

Information Theory and Bayesian Reliability Analysis: Recent Advances

Deciding with Thresholds: Importance Measures and Value of Information

Component importance based on dependence measures

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