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，一次性装置的可靠性分析通常在加速寿命试验中考虑多种应力因素。这类装置的应用可延伸至生物医学领域，其中常观察到因环境条件、生活方式特征、合并症等因素导致的疾病在多种压力因素下的出现。本研究将一次性装置数据分析应用于鼠类类鼻疽模型数据。假设寿命分布服从双参数逻辑-指数分布。除传统极大似然估计量外，还获得了用于稳健参数估计的加权最小密度功率散度估计量，并研究了其渐近行为及基于该估计量的假设检验。通过大量模拟实验评估了各估计量的性能，随后将这些方法应用于鼠类类鼻疽模型数据。鉴于精确确定一次性装置检测时间的重要性，本文开展了最优检测时间的搜索研究。该优化旨在最小化一个权衡估计精度与实验成本的代价函数，并通过基于群体的启发式优化方法——遗传算法实现搜索。