In the literature, the reliability analysis of one-shot devices is found under accelerated life testing in presence of various stress factors. The application of one-shot devices can be extended to the bio-medical field, where often we evidence the emergence of certain diseases under different stress factors due to environmental conditions, lifestyle aspects, presence of co-morbidity etc. In this work, one-shot device data analysis is performed in application to the Murine model for Melioidosis data. The two-parameter logistic exponential distribution is assumed as a lifetime distribution. Weighted minimum density power divergence estimators (WMDPDEs) for robust parameter estimation are obtained along with the conventional maximum likelihood estimators (MLEs). The asymptotic behaviour of the WMDPDEs and testing of the hypothesis based on it are also studied. The performances of estimators are evaluated through extensive simulation experiments. Later those developments are applied to the Murine model for Melioidosis Data. Citing the importance of knowing exactly when to inspect the one-shot devices put to test, a search for optimum inspection times is performed. This optimization is designed to minimize a defined cost function which strikes a trade-off between the precision of the estimation and experimental cost. The search is performed through the population-based heuristic optimization method Genetic Algorithm.

翻译：在文献中,一发装置的可靠性分析是在加速生命测试中找到的,有各种压力因素;一发装置的应用可以扩大到生物医学领域,在那里,我们常常看到由于环境条件、生活方式方面、共同发病的存在等原因,某些疾病在不同压力因素下出现。在这项工作中,对Murine模型的米利多松病数据应用了一发装置数据分析。两发装置后勤指数分布假定为终身分布。在生物医学领域可以应用一发装置,与传统的最大可能性估计器(MLEs)一起,对稳健的参数估计进行了加权最小密度功率差估计仪(MSPDEs),在生物医学领域,我们经常看到由于环境条件、生活方式、共同发病情况等原因,在不同的压力因素下出现某些疾病。在应用Murine模型的米利多兹病数据模型时,对一发装置的逻辑指数分布假定为终身分布。精确了解何时检查一发装置的重要性,同时进行最佳检查时间的搜索。这一优化利用了大规模毁灭性武器估计仪(MMMMMERDE)的失色行为和根据测算法对测算的精确度进行了研究,以尽量减少人口进行了最精确的计算。这一研究,以尽量减少成本进行。