Reliability estimation of s-out-of-k system for non-identical stress–strength components

Abstract

This paper takes into account the estimation of system reliability of a multi-component stress–strength model of a s-out-of-k system, where both strength and stress are non-identical components and follow Weibull and Burr-III distributions, respectively. The reliability of such a system is obtained by the methods of maximum likelihood and Bayesian approach. We consider Bayes estimation under squared error loss function using conjugate priors for the parameters involved in the models. We propose Markov Chain Monte Carlo (MCMC) techniques to generate samples from the posterior distributions and in turn computing the Bayes estimator of the system reliability. Simulation study shows that Bayes estimator works better as compared to maximum likelihood estimator.