Abstract

In accelerated life testing researcher generally use a life stress relationship between life characteristic and stress to estimate the parameters of failure time distributions at use condition which is just a re-parameterization of original parameters but from statistical point of view it is easy and reasonable to deal with original parameters of the distribution directly instead of developing inference for the parameters of the life stress relationship. So, an attempt is made here to estimate the parameters of Burr Type X life distribution directly in accelerated life testing by assuming that the lifetimes at increasing stress levels forms a geometric process. A mathematical model for the analysis of constant stress accelerated life testing for type-I censored data is developed and the estimates of parameters are obtained by using the maximum likelihood method. Also a Fisher information matrix is constructed in order to get the asymptotic variance and interval estimates of the parameters. Lastly, a simulation study is performed to illustrate the statistical properties of the parameters and the confidence intervals.

Highlights

  • In reliability analysis, researchers used to analyze time-to-failure data obtained under normal operating conditions in order to quantify the product’s failure-time distribution and its associated parameters

  • Due to the today’s highly matured technology, products are highly reliable and the life data is very difficult and costly to obtain at normal use conditions

  • Three types of stress loadings are usually applied in accelerated life tests: constant stress, step stress and linearly increasing stress

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Summary

Introduction

Researchers used to analyze time-to-failure data obtained under normal operating conditions in order to quantify the product’s failure-time distribution and its associated parameters. Nowadays products has their own guarantee or warrantee schemes so the need to be tested in advance before their launch. Kamal et al (2013) analysed constant stress accelerated life testing for Pareto distribution with complete samples by using geometric process model. Rahman, et al (2016) studied the application of geometric process for generalized exponential distribution in accelerated life testing with complete data. Lone et al (2016) extended this and presented a study of accelerated life testing design using geometric process for generalized exponential distribution using time constraint. The statistical properties of estimates and confidence intervals are examined through a simulation study

Model Description and Test Procedure
Conclusion and Future Work
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