Study on Step-Stress Accelerated Life Testing for The Burr-XII Distribution Using Cumulative Exposure Model Under Progressive Type-II Censoring with Real Data Example

2021 ◽  
Vol 10 (1) ◽  
pp. 35-44
Keyword(s):  
Real Data ◽  
Accelerated Life Testing ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Type Ii ◽  
Life Testing ◽  
Cumulative Exposure Model ◽  
Accelerated Life ◽  
Progressive Type ◽  
Progressive Type Ii Censoring

scholarly journals On Bayesian estimation of step stress accelerated life testing for exponentiated Lomax distribution based on censored samples

2020 ◽  
pp. 239-248
Author(s):  
Refah Mohamed Alotaibi ◽  
Hoda Ragab Rezk
Keyword(s):  
Numerical Study ◽  
Real Data ◽  
Operating Conditions ◽  
Stress Factors ◽  
Cumulative Exposure ◽  
Exposure Model ◽  
Life Testing ◽  
Unknown Parameters ◽  
Accelerated Life ◽  
Credible Intervals

In reliability analysis and life-testing experiments, the researcher is often interested in the effects of changing stress factors such as “temperature”, “voltage” and “load” on the lifetimes of the units. Step-stress (SS) test, which is a special class from the well-known accelerated life-tests, allows the experimenter to increase the stress levels at some constant times to obtain information on the unknown parameters of the life models more speedily than under usual operating conditions. In this paper, a simple SS model from the exponentiated Lomax (ExpLx) distribution when there is time limitation on the duration of the experiment is considered. Bayesian estimates of the parameters assuming a cumulative exposure model with lifetimes being ExpLx distribution are resultant using Markov chain Monte Carlo (M.C.M.C) procedures. Also, the credible intervals and predicted values of the scale parameter, reliability and hazard are derived. Finally, the numerical study and real data are presented to illustrate the proposed study

scholarly journals A Bayes Inference for Step-Stress Accelerated Life Testing

2017 ◽  
Vol 6 (6) ◽  
pp. 1
Author(s):  
Naijun Sha ◽  
Hao Yang Teng
Keyword(s):  
Real Data ◽  
Accelerated Life Testing ◽  
Cumulative Exposure ◽  
Data Sets ◽  
Life Testing ◽  
Bayes Inference ◽  
Monte Carlo Algorithms ◽  
Mathematical Properties ◽  
Posterior Inference ◽  
Accelerated Life

In this article, we present a Bayesian analysis with convex tent priors for step-stress accelerated life testing (SSALT) using a proportional hazard (PH) model. As flexible as the cumulative exposure (CE) model in fitting step-stress data and its attractive mathematical properties, the PH model makes Bayesian inference much more accessible than the CE model. Two sampling methods through Markov chain Monte Carlo algorithms are employed for posterior inference of parameters. The performance of the methodology is investigated using both simulated and real data sets.

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