Performance analysis of repairable system using GA and fuzzy Lambda-Tau methodology

Abstract

Purpose – The purpose of this paper is to develop a new approach for computing various performance measures such as reliability, availability, MTBF, ENOF, etc. for any industrial system. Design/methodology/approach – Pulping system, the main functionary part of paper industry, is the subject of the study. The interactions among the working components are shown using Petri nets (PNs). Failure and repair rates are represented using triangular fuzzy numbers (TFNs), as they allow expert opinion, linguistic variables, operating conditions, uncertainty and imprecision in reliability information, to be incorporated into system model. The failure rates and repair times of all constituent components are obtained using genetic algorithms (GAs) and then various performance measures are computed using fuzzy Lambda-Tau methodology (FLTM). Findings – The proposed methodology provides a better understanding about the behavior of any repairable system through its graphical representation. Originality/value – A new approach has been given to compute various performance measures and based on calculated reliability parameters, a structured framework has been developed that may help the maintenance engineers to analyze and predict the system behavior.