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Causality diagram using normal fuzzy numbers

机译:使用正态模糊数的因果图

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This paper applies fuzzy concepts to causality diagram, where the probabilities of all events are considered as fuzzy numbers, and shows that n-ary fuzzy AND and OR operators are used to evaluate the possibility of system events failure. A normal fuzzy number (NFN) can be defined completely by a triplet (m, /spl alpha/, /spl beta/). We can diagnose system fault based on fuzzy probability of the events. The goal of this paper is to replace probabilistic considerations in the causality diagram by the probabilistic ones and to reduce the difficulty arising from the inexact and insufficient information of the distribution functions of basic event and linkage event. The result of numerical simulating is coincident with the fact, so the fuzzy causality diagram is effective. The research indicates that fuzzy causality diagram is so effective in fault analysis, and it is more flexible and adaptive than conventional causality diagram.
机译:本文将模糊概念应用于因果关系图,其中所有事件的概率均视为模糊数,并表明使用n元模糊AND和OR运算符评估系统事件失败的可能性。正常模糊数(NFN)可以完全由三元组(m,/ spl alpha /,/ spl beta /)定义。我们可以基于事件的模糊概率来诊断系统故障。本文的目的是用因果关系图替换因果关系图中的概率考虑因素,并减少由于基本事件和关联事件的分布函数的信息不准确和不足而引起的困难。数值模拟的结果与事实相吻合,因此模糊因果图是有效的。研究表明,模糊因果图在故障分析中是如此有效,并且比传统的因果图更具灵活性和适应性。

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