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1.8.4 Probabilistic Risk/Reliability Analysis (PRA) Using a Simulation Approach
Author(s) -
Miller Ian,
Kossik Rick,
Nutt Mark W.,
Hill Ralph S.
Publication year - 2003
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2003.tb02702.x
Subject(s) - probabilistic logic , fault tree analysis , computer science , complex system , reliability (semiconductor) , probabilistic risk assessment , monte carlo method , reliability engineering , component (thermodynamics) , function (biology) , nonlinear system , power (physics) , artificial intelligence , engineering , mathematics , statistics , physics , quantum mechanics , evolutionary biology , biology , thermodynamics
Probabilistic risk/reliability (PRA) analyses for engineered systems are conventionally based on fault‐tree methods. These methods are mature and efficient, and are well suited to systems consisting of interacting components with known, low probabilities of failure. Even complex systems, such as nuclear power plants or aircraft, are modeled by the careful application of these approaches. However, for systems that may evolve in complex and nonlinear ways, and where the performance of components may be a sensitive function of the history of their working environments, fault‐tree methods can be very demanding. This paper proposes an alternative method of evaluating such systems, based on probabilistic time‐based simulation using intelligent software objects to represent the components of such systems. Using a Monte Carlo approach, simulation models can be constructed from relatively simple interacting objects that capture the essential behavior of the components that they represent. Such models are capable of reflecting the complex behaviors of the systems that they represent in a natural and realistic way.