Nondestructive one-shot device (NOSD) testing plays a crucial role in engineering, particularly in the reliability assessment of high-stakes systems such as aerospace components, medical devices, and semiconductor technologies. Accurate reliability prognosis of NOSD testing data is essential for ensuring product durability, safety, and performance optimization. The conventional estimation methods like maximum likelihood estimation (MLE) are sensitive to data contamination, leading to biased results. Consequently, this study develops robust inferential analysis for NOSD testing data under a progressive stress model. The lifetime of NOSD is assumed to follow Log-logistic distribution. The estimation procedure addresses robustness by incorporating Exponential-polynomial divergence (EPD). Equipped with three tuning parameters, EPD based estimation is proven to be more flexible than density power divergence estimation frequently used for one-shot device testing data analysis. Further, we explore the asymptotic behaviour of minimum EPD estimator (MEPDE) for large sample size. The robustness of MEPDE is analytically studied through influence function. Since tradeoff between efficiency and robustness of EPD based estimation is governed by three tuning parameters, a novel approach leveraging Concrete Score Matching (CSM) is introduced to optimize the tuning parameters of MEPDE. Moreover, a comparative study with the existing methods of finding tuning parameters is conducted through extensive simulation experiment and data analysis. Another aspect of this study is determining an optimal plan to ensure a successful ALT experiment within specified budget and time constraints. It is designed on A-optimality criteria subject to the given constraints and is executed using the constraint particle swarm optimization (CPSO) algorithm.

翻译：非破坏性单次设备测试在工程领域具有至关重要的作用，特别是在航空航天部件、医疗设备和半导体技术等高风险系统的可靠性评估中。对非破坏性单次设备测试数据进行准确的可靠性预测对于确保产品耐久性、安全性和性能优化至关重要。传统估计方法（如最大似然估计）对数据污染敏感，容易导致有偏结果。因此，本研究针对渐进应力模型下的非破坏性单次设备测试数据开发了稳健的推断分析方法。假设非破坏性单次设备寿命服从对数逻辑斯蒂分布。该估计程序通过引入指数多项式散度来处理稳健性问题。基于三个调节参数的指数多项式散度估计被证明比常用于单次设备测试数据分析的密度幂散度估计更具灵活性。此外，我们探讨了大样本量下最小指数多项式散度估计量的渐近性质。通过影响函数对最小指数多项式散度估计量的稳健性进行了分析研究。由于基于指数多项式散度的估计在效率与稳健性之间的权衡受三个调节参数控制，本文引入了一种利用混凝土分数匹配的新方法来优化最小指数多项式散度估计量的调节参数。此外，通过大量模拟实验和数据分析，与现有调节参数确定方法进行了比较研究。本研究的另一个方面是在指定预算和时间约束下确定确保加速寿命试验成功实施的最优方案。该方案基于给定约束条件下的A最优性准则设计，并采用约束粒子群优化算法执行。