Reliability analysis of exponentiated Poisson‐exponential constant stress accelerated life test model

Abstract

Accelerated life testing is an efficient tool frequently adopted for obtaining failure time data of test units in a lesser time period as compared to normal use conditions. We assume that the lifetime data of a product at constant level of stress follows an exponentiated Poisson-exponential distribution and the shape parameter of the model has a log-linear relationship with the stress level. Model parameters, the reliability function (RF), and the mean time to failure (MTTF) function under use conditions are estimated based on eight frequentist methods of estimation, namely, method of maximum likelihood, method of least square and weighted least square, method of maximum product of spacing, method of minimum spacing absolute-log distance, method of Cramér-von-Mises, method of Anderson-Darling, and Right-tail Anderson-Darling. The performance of the different estimation methods is evaluated in terms of their mean relative estimate and mean squared error using small and large sample sizes through a Monte Carlo simulation study. Finally, two accelerated life test data sets are considered and bootstrap confidence intervals for the unknown parameters, predicted shape parameter, predicted RF, and the MTTF at different stress levels, are obtained.

Inference for constant stress accelerated life test under the proportional reverse hazards lifetime distribution

“…Commonly used inference methods for ALT under the distribution family () are based on likelihood function and Bayesian methods 18–21 . Due to the randomness of the samples, the point estimate may be biased.…”

“…16,17 Commonly used inference methods for ALT under the distribution family (1) are based on likelihood function and Bayesian methods. [18][19][20][21] Due to the randomness of the samples, the point estimate may be biased. Compared to point estimation, interval estimation can quantify the uncertainties.…”