Statistical Fuzzy Reliability Assessment of a Blended System

Abstract

Fuzzy sets have been proven to constitute an asset in the evolution of reliability theory in recent decades. Their contribution in addressing the possibility of errors, insufficiency of data, randomness, or fuzziness, either in the system or in the accumulation of any data for the respective system, which is overlooked in the traditional reliability assessment, seems to be quite crucial. The present work deals with the statistical fuzzy reliability evaluation of a blended system that comprises two subsystems. One system contains two components aligned in a parallel configuration, and the other is a 3-out-of-5 system. The reliability of this model is assessed using two approaches to intuitionistic fuzzy sets (IFS), namely, traditional IFS and interval-valued intuitionistic fuzzy sets (IVIFS). Three cases are considered in each approach, which are compared individually as well as with each other. It was established that the IVIFS yield better results than the IFS. The obtained results are displayed in both tabular and graphical forms for better assessment.

Keywords

• interval-valued intuitionistic fuzzy sets (IVIFS)
• intuitionistic fuzzy reliability (IFR)
• intuitionistic fuzzy sets (IFS)
• <i>k</i>-out-of-<i>n</i> system
• statistical fuzzy reliability