Reliability analysis of K-out-of-N system for Weibull components based on generalized progressive hybrid censored data

TL;DR

This paper assesses the reliability of K-out-of-N systems with Weibull components using generalized progressive hybrid censored data, providing MLEs, Bayesian estimates, and confidence intervals through simulation and real data analysis.

Contribution

It introduces a comprehensive approach for reliability estimation of K-out-of-N Weibull systems under complex censored data, including MLEs, Bayesian estimates, and interval estimation methods.

Findings

MLEs and Bayesian estimates are effectively computed.

Simulation studies validate the estimation methods.

Real data application demonstrates practical utility.

Abstract

In this paper, we have investigated the reliability of a K-out-of-N system for the components following Weibull distribution based on the generalized progressive hybrid censored data. We have obtained the maximum likelihood estimates (MLEs) of the unknown parameters and the reliability function of the system. Using asymptotic normality property of MLEs, the corresponding asymptotic confidence intervals are constructed. Furthermore, Bayes estimates are derived under squared error loss function with informative prior by using Markov Chain Monte Carlo (MCMC) technique. Highest posterior density (HPD) credible intervals are obtained. A Monte Carlo simulation study is carried out to compare performance of the established estimates. Finally, a real data set is considered for illustrative purposes.