Importance measures in ranking piping components for risk informed in-service inspection

Vinod, Gopika; Kushwaha, H. S.; Verma, Ajit Kumar; Srividya, A.

doi:10.1016/s0951-8320(02)00270-3

Cited by 28 publications

(12 citation statements)

References 4 publications

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“…The RRW measures the decrease in risk when the component is replaced by a perfectly reliable component (Vinod et al 2003). This represents the maximum improvement in system reliability that can be achieved from an improvement to the component of interest.…”

Section: Comparison With Component Importance Measuresmentioning

confidence: 99%

Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

Tien

2017

Springer Series in Reliability Engineering

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Section: Comparison With Component Importance Measuresmentioning

confidence: 99%

Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

Tien

2017

Springer Series in Reliability Engineering

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“…Based on the calculated criticality index, the order of critical components can be justified. Fussell-Vesely (FV) importance (8) can be used to calculate the criticality index of a component [12].…”

Section: Phase 4: Criticality Analysismentioning

confidence: 99%

A Hybrid Approach for Fault Tree Analysis Combining Probabilistic Method with Fuzzy Numbers

Purba

Ruan

et al. 2010

Lecture Notes in Computer Science

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Abstract. Conventional fault tree analysis in safety analysis of complex engineering systems calculates the occurrence probability of the top undesired event using probabilistic failure rates. However, it is often very difficult to obtain those failure rates well in advance due to insufficient data, environment changing or new components. Fuzzy numbers can be applied to estimate failure rates by handling linguistic terms. This study proposes a hybrid approach of Fuzzy Numbers and Fault Tree Analysis to solve the conventional problem and describes its procedures using a case study of emergency core cooling system of a typical nuclear power plant.

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“…With increasing numbers of scenarios this process of investigation can be automated, for instance by using BN specific any-time algorithms. If scenarios are regarded as being similar to hardware system components several other importance criteria to prioritize scenarios exist, for example Fussell-Vesely (FV), the Criticality Importance Factor (CIF) and the Birmbaum Importance (BI) measure; see [17].…”

Section: Scenario Prioritizationmentioning

confidence: 99%

“…Metrics like the Risk Achievement Worth (RAW), and the Risk Reduction Worth (RRW) could be used for prioritization in this situation [17].…”

Section: Scenario Prioritizationmentioning

confidence: 99%

Improving state-of-the-art power plant availability using Bayesian Networks

Luyk

Rouvroye

2009

2009 Annual Reliability and Maintainability Symposium

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The Integrated Gasification Combined Cycle technology in combination with coal as a natural resource is claimed to be one of the leading power generation alternatives for the near future. One of the main challenges facing this technology today is plant availability. Due to the specific characteristics of this technology, current practices cannot sufficiently address plant availability. A different approach is needed. This paper proposes the use of Bayesian Networks as an extension of existing methods. Ultimately, the approach described in this paper is directed towards scenario-informed decision making to improve plant availability. This includes the prioritization of potential scenarios of unavailability and allocation of resources to prevent or mitigate the most serious scenarios, as well as operator training and identification of indicators of potential unavailability. A case study that demonstrates the application of the initial steps of the proposed approach has been conducted within a Dutch IGCC plant. A Bayesian Network was constructed for the syngas treatment unit of the plant, in which identified unavailability scenarios were modeled. Results from the case study indicate that our approach can help a company to identify, quantify, and prioritize scenarios, which can act as an input to improve availability management.

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Importance measures in ranking piping components for risk informed in-service inspection

Cited by 28 publications

References 4 publications

Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

Bayesian Network Methods for Modeling and Reliability Assessment of Infrastructure Systems

A Hybrid Approach for Fault Tree Analysis Combining Probabilistic Method with Fuzzy Numbers

Improving state-of-the-art power plant availability using Bayesian Networks

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