Assessing the lifetime performance index of Rayleigh products based on the Bayesian estimation under progressive type II right censored samples

Abstract

a b s t r a c tLifetime performance assessment is important in service (or manufacturing) industries. Hence, lifetime performance index C L is used to measure the potential and performance of a process, where L is the lower specification limit. In this paper, assuming the conjugate prior distribution and squared-error loss function, this study constructs a Bayes estimator under the Rayleigh distribution with the progressive type II right censored sample. The Bayes estimator of C L is then utilized to develop a credible interval in the condition of known L. Moreover, we also propose a Bayesian test to assess the lifetime performance of products. Finally, we give two examples and the Monte Carlo simulation to assess the behavior of the lifetime performance index C L . Moreover, the purchasers can then employ the credible interval and the Bayesian test to determine whether the product performance adheres to the required level.

Repetitive Group Sampling Plan for Rayleigh Products Based on the Lifetime Performance Index CL

“…Therefore, utilizing the one-to-one relationship between P r and C L , C L is widely used as a process capability index instead of the conforming rate P r in industrial companies (see Ref. [16]). …”

Preliminary-summation-based principal component analysis for non-Gaussian processes

“…Poisson distribution and Rayleigh distribution are common non-Gaussian distribution and they are adopted to represent non-Gaussian components in industrial processes [30][31][32] x is changed to -0.25…”

E-Bayesian Estimation for Rayleigh Model Using Progressive Type-II Censoring Data

“…Among others, Sinha and Howlader [15] obtained credible and HPD intervals of the parameter and reliability of Rayleigh distribution, Lalitha and Anand [11] studied the modified maximum likelihood estimation for Rayleigh distribution, Fernandez [7] obtained the Bayesian inference from type II doubly censored Rayleigh data, Raqab and Madi [14] considered the estimation of the predictive distribution of the total time on test up to a certain failure in a future sample on the basis of a doubly censored random sample of failure times drawn from a Rayleigh distribution, Soliman and AL-Aboud [17] discussed the Bayesian inference using recored values from Rayleigh Model, Kim and Han [12] discussed estimation of the scale parameter of Rayleigh distribution under general progressive censoring. Lee et al [13] obtained a Bayes estimator under the Rayleigh distribution with the progressive type II right censored sample. Abou-Elheggag [1] obtained a Bayes estimator under the Rayleigh distribution with the progressive first-failure censored data.…”