Stochastic behavior analysis of complex repairable industrial systems utilizing uncertain data

The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system stochastically by utilizing vague, imprecise, and uncertain data. In the present study two important tools namely Lambda-Tau methodology and particle swarm optimization are combinedly used to present a novel technique named as particle swarm optimization based Lambda-Tau (PSOBLT) for analyzing the behavior of a complex repairable system stochastically up to a desired degree of accuracy. Expressions of reliability indices like failure rate, repair time, mean time between failures (MTBF), expected number of failures (ENOF), reliability and availability for the system are obtained by using Lambda-Tau methodology and particle swarm optimization is used to construct their membership function. The interaction among the working units of the system is modeled with the help of Petri nets. The feeding unit of a paper mill situated in a northern part of India, producing approximately 200ton of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of system's behavior has also been done. The behavior analysis results computed by PSOBLT technique have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties.