17 research outputs found
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Structures for common-cause failure analysis
Common-cause failure methodology and terminology have been reviewed and structured to provide a systematical basis for addressing and developing models and methods for quantification. The structure is based on (1) a specific set of definitions, (2) categories based on the way faults are attributable to a common cause, and (3) classes based on the time of entry and the time of elimination of the faults. The failure events are then characterized by their likelihood or frequency and the average residence time. The structure provides a basis for selecting computational models, collecting and evaluating data and assessing the importance of various failure types, and for developing effective defences against common-cause failure. The relationships of this and several other structures are described
A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems
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Multi-state component models for reliability and risk analysis
Multi-state single-plant risk models are developed and the impacts of various failure and repair characteristics associated with safe shutdown and multiple accident conditions are analyzed. Comparisons of models are presented and recommendations are made for selecting a model. Fundamental recursive equations are then derived for the probabilistic characteristics of components in general. These equations are new results for multi-state components, allow replacement components to be new or old, and allow general failure and repair time distributions to be used. The equations are solved for the time dependent state probabilities and conditional and unconditional failure and repair rates
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Development in reliability models and methods
This paper reviews analytical developments in modeling reliability characteristics for components and systems. Modeling involves definition of failure modes, relevant probability and timing parameters for the modes, and derivation of explicit equations for component and system unavailabilities and failure intensities. Some but not all developments to be discussed were carried out within the DOE-sponsored LMFBR safety program
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Response surface techniques developed for probabilistic analysis of accident consequences
Response surface techniques have been developed for obtaining probability distributions of the consequences of postulated nuclear reactor accidents. The probabilistic response surface methodology reported includes new knot-point selection schemes and response surface functions, functional transformations of both parameters and consequence variables, smooth synthesis of regionwise response surfaces and the treatment of random conditions for conditional distributions. The computer code PROSA developed for implementing these techniques is independent of the deterministic accident analysis codes. It can also be used for direct simulation of general analytical functions. The significance, accuracy and other merits of these features are discussed and typical results are presented for illustration purposes
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Methods and computer codes for probabilistic sensitivity and uncertainty analysis
This paper describes the methods and applications experience with two computer codes that are now available from the National Energy Software Center at Argonne National Laboratory. The purpose of the SCREEN code is to identify a group of most important input variables of a code that has many (tens, hundreds) input variables with uncertainties, and do this without relying on judgment or exhaustive sensitivity studies. Purpose of the PROSA-2 code is to propagate uncertainties and calculate the distributions of interesting output variable(s) of a safety analysis code using response surface techniques, based on the same runs used for screening. Several applications are discussed, but the codes are generic, not tailored to any specific safety application code. They are compatible in terms of input/output requirements but also independent of each other, e.g., PROSA-2 can be used without first using SCREEN if a set of important input variables has first been selected by other methods. Also, although SCREEN can select cases to be run (by random sampling), a user can select cases by other methods if he so prefers, and still use the rest of SCREEN for identifying important input variables