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Vol. 1 Issue IV, Nov ember 2013 ISSN: 2321-9653
INTERNATIONAL JOURNAL FOR RESEARCH IN AP PLIED SCIENCE AN D E N G I N E E R I N G T E C H N O L O G Y (I J R A S E T)
Evaluating Failure of a Refrigeration cycle using Triangular Intuitionstic Fuzzy Approach Neeraj Lata Dept.of Mathematics, TMU, Moradabad, (U.P.) sirohimaths@gmail.com
Abstract: In real life systems, the information may be inaccurate or might have linguistic representation. In such cases the estimation of precise values of probability becomes very difficult. In order to handle this situation, triangular fuzzy approach is used to evaluate the failure rate status. In this paper we introduced triangular fuzzy fault tree analysis for evaluating failure range of the refrigeration cycle system.
Key Words: triangular Intuitionstic fuzzy approach, fuzzy fault tree, failure rate, refrigeration cycle etc. real number as a membership grade and in such cases it may be useful to identify meaningful lower and upper bounds for
1. INTRODUCTION The theory of fuzzy sets (FSs), proposed by Zadeh (1965) [6]
the membership grade. In 1986, Atanassov [7] introduced
has gained successful applications in various fields. However, Intuitionistic fuzzy sets (IFSs) which have been found to be the membership function of the fuzzy set is a single value very useful to deal with uncertainty information. between zero and one, which combines the favouring evidence and the opposing evidence. Due to fuzzy boundaries,
The concept of the IFSs is a generalization of that of the FSs.
this single value for the membership grade is the result of the
IFS's are being studied and used in different fields of science.
combined effect of evidences in favour and against the
Among the research works on these sets we can mention
inclusion of the element in the set the utility of the application
Atanassov [2,3,4]; Atanassov and Gargov [1]; Szmidt and
of fuzzy sets depends on the capability of the user to construct
Kacprzk (2001) [7] proposed the definition of Intuitionistic
appropriate membership functions, which are often very
Fuzzy numbers (IFN) and studied the perturbation of IFN.
precise. In many contexts it is difficult to assign a particular
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