Reliability Inference for the Multicomponent System Based on Progressively Type II Censored Samples from Generalized Pareto Distributions

Abstract

In this paper, the reliability of a k-component system, in which all components are subject to common stress, is considered. The multicomponent system will continue to survive if at least s out of k components’ strength exceed the common stress. The system reliability is investigated by utilizing the maximum likelihood estimator based on progressively type II censored samples from generalized Pareto distributions. The confidence interval of the system reliability can be obtained by using asymptotic normality with Fisher information matrix or bootstrap method approximation. An intensive simulation study is conducted to evaluate the performance of maximum likelihood estimators of the model parameters and system reliability for a variety of cases. For the confidence interval of the system reliability, simulation results indicate the bootstrap method approximation outperforms over the asymptotic normality approximation in terms of coverage probability.

Inferences of the Multicomponent Stress–Strength Reliability for Burr XII Distributions

“…The s-out-of-k G system has attracted extensive attention and R s,k inference has been broadly investigated by numerous studies. These include multicomponent strength-stress models for Kumaraswamy distribution by Dey et al [7], based on Chen distribution by Kayal [8], based on general class of inverse exponentiated distribution and proportional reversed hazard rate distribution by Kizilaslan [9,10], based on bivariate Kumaraswamy distribution by Kizilaslan and Nadar [11], based on Marshall-Olkin bivariate Weibull distribution by Nadar and Kizilaslan [12], based on Rayleigh stress-strength model by Rao [13], based on Burr XII distribution by Rao et al [14], based on progressively Type-II censored samples from generalized Pareto distribution by Sauer et al [15], and based on Rayleigh stress-strength model by Wang et al [16].…”

Bayesian Estimation of Reliability for Multicomponent Stress-Strength Model Based on Topp-Leone Distribution

“…In 2020, Mahato et al [22] studied the reliability of the mentioned system under progressive Type II monitoring data when the failure times fol-low Inverted exponentiated Distribution. Sauer et al, in 2020 [23]. As well, Rasekhi et al in 2020 [24], Mezaal et al in 2020 [25], Lately in 2021, Kohansal and Shoaee in [26], reported the reliability of the system using the Weibull Distribution.…”

Inference of reliability model with burr type XII distribution under two sample balanced progressive censored samples

“…This censoring is widely adopted by scholars due to its more efficient and flexible performance: testers can remove units from the experiment whenever needed and whenever failures occurred. Balakrishnan (2007), Wang et al (2014), Chaudhary and Tomer (2018), Almetwaly and Almongy (2018), Sauer et al (2020), Lone et al (2021a) and Dutta and Kayal (2022). Further, comparative lifetime experiments model is of great importance in reliability study, because it reduces the cost and accelerates the testing time.…”