INTERVAL-VALUED FUZZY SET APPROACH

Abstract: System age is a critical but an unobservable state parameter in system reliability modeling and maintenance policy decision-making. The information generated during system operating and maintenance may be well-used to evaluate system age in terms of fuzzy covariate modeling which can be carried by the aid of fuzzy set theory since the maintenance data is vague and imprecise in nature. Currently combination fuzzy information is basically utilizing the Type I fuzzy sets, where the fuzzy membership is assumed as point-wise positive function ranging on [0,1]. Such a practice might not be practical because an interval-valued membership may reflect the vagueness of system better according to human thinking patterns. Furthermore a repairable operating system is grey because only partial information is available (mostly the operating data but not the maintenance data). In this paper, we explore the basics of the interval-valued fuzzy sets theory and also the GM(1,1) model from grey system theory for estimate the repair impacts and finally combine the grey repair effects with interval-valued fuzzy set to establish a system age evaluation