Reliability inference for VGA adapter from dual suppliers based on contaminated type‐I interval‐censored data

Abstract

Type‐I interval‐censoring scheme only documents the number of failed units within two prespecified consecutive exam times at the larger time point after putting all units on test at the initial time schedule. It is challenging to use the collected information from type‐I interval‐censoring scheme to evaluate the reliability of unit when not all admitted units are operated or tested at the same initial time and a majority of units are randomly selected to replace the failed test units at unrecorded time points. Moreover, the lifetime distribution of all pooled units from dual resources usually follows a mixture distribution. To overcome these two problems, a two‐stage inference process that consists of a data‐cleaning step and a parameter estimation step via either Markov chain Monte Carlo (MCMC) algorithm or profile likelihood method is proposed based on the contaminated type‐I interval‐censored sample from a mixture distribution with unknown proportion. An extensive simulation study is conducted under the mixture smallest extreme value distributions to evaluate the performance of the proposed method for a case study. Finally, the proposed methods are applied to the mixture lifetime distribution modeling of video graphics array adapters for the support of reliability decision.

EM Algorithm for Mixture Distributions Model with Type-I Hybrid Censoring Scheme

“…The third dataset contains 600 lifetimes of adapters which are used in the liquid-crystal display (LCD) assembling monitors. Tsai et al [22] has studied this dataset and their suggested model of two Weibull mixture distributions can characterize this dataset well.…”

“…The dataset is regarding the failure time of a type of video graphics array (VGA) adapter used for the liquid-crystal display (LCD) assembling monitors. The work done by Tsai et al [22] was suggested and the company continued to purchase adapters from two suppliers and started to record the exact failure time in terms of hours and the originality of each adapter. In this example, the dataset that contains a sample of n = 600 failure times in terms of days, with their originality, are considered.…”

Parameter Estimation for Composite Dynamical Systems Based on Sequential Order Statistics from Burr Type XII Mixture Distribution

“…Because the current on the LED panel is unchanged, each LED failure will enhance the stress loading of each surviving LED until the LED panel malfunctions. Tsai et al [33] proposed a Bayesian estimation method to evaluate the reliability of the CDS with heterogeneity condition, and Tsai et al [34] proposed a Bayesian method and a profile maximum likelihood estimation method to infer the reliability of a video graphics array (VGA) when the adapters come from dual suppliers based on interval-censored samples. When dual suppliers support components for a CDS company simultaneously, using a mixture distribution is better than using a single distribution for characterizing the baseline component lifetimes for the study of CDS reliability.…”