USING COPULAS TO MODEL DEPENDENCE IN SIMULATION RISK ASSESSMENT

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Typical engineering systems in applications with high failure consequences such as nuclear reactor plants often employ redundancy and diversity of equipment in an effort to lower the probability of failure and therefore risk. However, it has long been recognized that dependencies exist in these redundant and diverse systems. Some dependencies, such as common sources of electrical power, are typically captured in the logic structure of the risk model. Others, usually referred to as intercomponent dependencies, are treated implicitly by introducing one or more statistical parameters into the model. Such common-cause failure models have limitations in a simulation environment. In addition, ... continued below

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Kelly, Dana L. November 1, 2007.

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Typical engineering systems in applications with high failure consequences such as nuclear reactor plants often employ redundancy and diversity of equipment in an effort to lower the probability of failure and therefore risk. However, it has long been recognized that dependencies exist in these redundant and diverse systems. Some dependencies, such as common sources of electrical power, are typically captured in the logic structure of the risk model. Others, usually referred to as intercomponent dependencies, are treated implicitly by introducing one or more statistical parameters into the model. Such common-cause failure models have limitations in a simulation environment. In addition, substantial subjectivity is associated with parameter estimation for these models. This paper describes an approach in which system performance is simulated by drawing samples from the joint distributions of dependent variables. The approach relies on the notion of a copula distribution, a notion which has been employed by the actuarial community for ten years or more, but which has seen only limited application in technological risk assessment. The paper also illustrates how equipment failure data can be used in a Bayesian framework to estimate the parameter values in the copula model. This approach avoids much of the subjectivity required to estimate parameters in traditional common-cause failure models. Simulation examples are presented for failures in time. The open-source software package R is used to perform the simulations. The open-source software package WinBUGS is used to perform the Bayesian inference via Markov chain Monte Carlo sampling.

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  • 2007 ASME International Mechanical Engineering Congress and Exposition,Seattle, Washington,11/11/2007,11/15/2007

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  • Report No.: INL/CON-07-13037
  • Grant Number: DE-AC07-99ID-13727
  • Office of Scientific & Technical Information Report Number: 912915
  • Archival Resource Key: ark:/67531/metadc880488

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  • November 1, 2007

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  • Sept. 22, 2016, 2:13 a.m.

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  • Nov. 7, 2016, 6:23 p.m.

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Kelly, Dana L. USING COPULAS TO MODEL DEPENDENCE IN SIMULATION RISK ASSESSMENT, article, November 1, 2007; [Idaho Falls, Idaho]. (digital.library.unt.edu/ark:/67531/metadc880488/: accessed September 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.