A TWO-PHASE APPROACH FOR RELIABILITY AND MAINTAINABILITY ANALYSIS OF AN INDUSTRIAL SYSTEM

Abstract

Probability distributions play an important role in the reliability studies and have many applications in engineering. Estimation of the distribution parameters is also significant for making a decision. It is difficult to trap the global solution for the estimation of parameters by the traditional methods such as maximum likelihood estimator (MLE), as these methods trap a local solution. To get the global values of these parameters is complicated. In this paper, we have developed a two-phase approach by taking the advantages of one of the evolutionary algorithms, namely the particle swarm optimization (PSO) for getting the global values of the distribution parameters. PSO exploits the concept of the moment method to estimate the distribution parameters by using mean, standard deviation, coefficient of variation (CV) and Anderson–Darling (AD) statistics for the best fitting of the distribution to failure data. In comparison with the widely used algorithms, a meta-heuristic algorithm (PSO) offers much more choices for system designers.