Use of importance measures in risk-informed regulatory applications

Cheok, Michael C.; Parry, Gareth; Sherry, Richard R.

doi:10.1016/s0951-8320(97)00144-0

Cited by 206 publications

(134 citation statements)

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“…Due to the larger number of infrastructures and users included in the analysis, the authors use Monte Carlo simulation and importance measure concepts for the analysis of each infrastructure. The authors borrow the concept of importance measures from PRA [25] and they generalize it to include the stakeholder values. Each infrastructure is analyzed independently to assign to each location a value of the new importance measure the authors call Geographic Valued Worth (GVW).…”

Section: Nsementioning

confidence: 99%

Bulk power risk analysis: Ranking infrastructure elements according to their risk significance

Koonce

Apostolakis

Cook

2008

International Journal of Electrical Power & Energy Systems

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Section: Nsementioning

confidence: 99%

Bulk power risk analysis: Ranking infrastructure elements according to their risk significance

Koonce

Apostolakis

Cook

2008

International Journal of Electrical Power & Energy Systems

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“…The criticality term of the performance metric is based on the Fussell-Vesely importance measure, Cheok et al (1998). This importance measure is selected over other importance measures because it can be translated to sensors with ease, adapting the definition of individual components as it was originally proposed for.…”

Section: Diagnostic Termmentioning

confidence: 99%

A sensor selection method for fault diagnostics

Reeves

Remenyte‐Prescott

Andrews

2017

Safety and Reliability – Theory and Applications

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In the modern world, systems are becoming increasingly complex, consisting of large numbers of components and their failures. In order to monitor system performance and to detect faults and diagnose failures, sensors can be used. However, using sensors can increase the cost and weight of the system. Therefore, sensors need to be selected based on the information that they provide.In this paper, a sensor selection process is introduced based on a novel sensor performance metric. In this process, sensors are selected based on their ability to detect faults and diagnose failures of components in the system, as well as the severity of failure effects on system performance. A Bayesian Belief Network (BBN) is used to model the outputs of the sensors. Sensor reading evidence is introduced in the BBN to enable the component failures to be identified. A simple system example is used to illustrate the proposed approach.

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“…3 The importance measure of most interest to us is the Risk Achievement Worth (RAW). RAW is defined as…”

Section: Iva: Raw With Respect To Cdfmentioning

confidence: 99%

“…RAW is a bounding measure that provides the maximum level of risk that a basic event could cause. 3 The meaning of RAW can also be viewed with respect to the logic structure of the PRA.…”

Section: Iva: Raw With Respect To Cdfmentioning

confidence: 99%

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Including model uncertainty in risk-informed decision making

Reinert

Apostolakis

2006

Annals of Nuclear Energy

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Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the Risk Achievement Worth (RAW) importance measure with respect to both the core damage frequency (CDF) and the change in core damage frequency (ACDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision.A previously submitted licensing basis change is used as a case study. Analysis using the SAPHIRE program identifies 20 basic events as important, four of which have model uncertainties that have been identified in the literature as generally important. The decision is fairly insensitive to uncertainties in these basic events. In three of these cases, one would need to show that model uncertainties would lead to basic event probabilities that would be between two and four orders of magnitude larger than modeled in the risk assessment before they would become important to the decision. More detailed analysis would be required to determine whether these higher probabilities are reasonable. Methods to perform this analysis from the literature are reviewed and an example is demonstrated using the case study. We then look at policy issues surrounding the effects of uncertainty in decision making related to nuclear power generation.

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Use of importance measures in risk-informed regulatory applications

Cited by 206 publications

References 1 publication

Bulk power risk analysis: Ranking infrastructure elements according to their risk significance

Bulk power risk analysis: Ranking infrastructure elements according to their risk significance

A sensor selection method for fault diagnostics

Including model uncertainty in risk-informed decision making

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