Causality diagram using normal fuzzy numbers

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 tri...

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Bibliographic Details
Published in2005 IEEE International Conference on Granular Computing Vol. 1; pp. 221 - 224 Vol. 1
Main Authors Shi Qingxi, Liang Xinyuan, Zhang Qin
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Accidents
Causality Diagram
Computer science education
Couplings
Distribution functions
Educational institutions
Educational programs
Fault Analysis
Fuzzy Number
Fuzzy systems
Hazards
Humans
Normal Fuzzy Number (NFN)
Numerical simulation
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Summary: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.
ISBN:0780390172
9780780390171
DOI:10.1109/GRC.2005.1547271