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- W141098149 abstract "In a fault tree analysis, an uncertainty importance measure is often used to assess how much uncertainty of the top event probability (Q) is attributable to the uncertainty of a basic event probability (), and thus, to identify those basic events whose uncertainties need to be reduced to effectively reduce the uncertainty of Q. For evaluating the measures suggested by many authors which assess a percentage change in the variance V of Q with respect to unit percentage change in the variance of , V and need to be estimated analytically or by Monte Carlo simulation. However, it is very complicated to analytically compute V and for large-sized fault trees, and difficult to estimate them in a robust manner by Monte Carlo simulation. In this paper, we propose a method for experimentally evaluating the measure using a Taguchi orthogonal array. The proposed method is very computationally efficient compared to the method based on Monte Carlo simulation, and provides a stable uncertainty importance of each basic event." @default.
- W141098149 created "2016-06-24" @default.
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- W141098149 date "2009-01-01" @default.
- W141098149 modified "2023-09-24" @default.
- W141098149 title "Evaluation of Uncertainty Importance Measure by Experimental Method in Fault Tree Analysis" @default.
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