The effectiveness of accelerated degradation test and subsequent reliability assessment depends on the efficient test plan. Designing an accelerated test plan requires determining the optimal values of several decision variables. The problem of designing an accelerated degradation test becomes even more challenging when multiple stress factors and their interaction effects are considered. In this work, an optimal accelerated degradation test plan is presented considering the stochastic gamma process with constant stress loading. Both gamma parameters are treated as stress-dependent variables to capture the impact of stress levels. An optimization model is formulated considering multiple stress factors and their interaction effects for offering a more realistic test plan. The optimization criteria are set to minimizing the asymptotic variance of the maximum likelihood estimator of the lifetime at normal operating conditions within the total experimental cost constraints. The decision variables comprised in the optimization model are measurement time intervals, the frequency of measurements, stress levels, and sample allocation at each stress level. The sensitivity analysis is performed to investigate the model's robustness against the budget constraints and pre-estimates of model parameters. To demonstrate the proposed methodology, a case example of carbon film-resistors’ performance data is considered for designing the degradation test plan.

加速退化测试和后续可靠性评估的有效性取决于有效的测试计划。设计加速测试计划需要确定几个决策变量的最佳值。当考虑到多个应力因素及其相互作用影响时，设计加速降解测试的问题将变得更具挑战性。在这项工作中，考虑了具有恒定应力负载的随机伽马过程，提出了一种最佳的加速降解测试计划。这两个伽玛参数都被视为压力相关变量，以捕获压力水平的影响。考虑多个压力因素及其相互作用影响制定了优化模型，以提供更实际的测试计划。优化标准设置为在总实验成本约束范围内，在正常工作条件下将寿命的最大似然估计器的渐近方差最小化。优化模型中包含的决策变量是测量时间间隔，测量频率，应力水平以及每个应力水平下的样品分配。进行敏感性分析以针对预算约束和模型参数的预先估计调查模型的鲁棒性。为了演示所提出的方法，考虑了碳膜电阻器性能数据的案例来设计退化测试计划。优化模型中包含的决策变量是测量时间间隔，测量频率，应力水平以及每个应力水平下的样品分配。进行敏感性分析以针对预算约束和模型参数的预先估计调查模型的鲁棒性。为了演示所提出的方法，考虑了碳膜电阻器性能数据的案例来设计退化测试计划。优化模型中包含的决策变量是测量时间间隔，测量频率，应力水平以及每个应力水平下的样品分配。进行敏感性分析以针对预算约束和模型参数的预先估计调查模型的鲁棒性。为了演示所提出的方法，考虑了碳膜电阻器性能数据的案例来设计退化测试计划。