Accelerated life testing (ALT) is a method of reducing the lifetime of components through exposure to extreme stress. This method of obtaining lifetime information involves the design of a testing experiment, i.e., an accelerated test plan. In this work, we adopt a simulation-based approach to obtaining optimal test plans for constant-stress accelerated life tests with multiple design points. Within this simulation framework we can easily assess a variety of test plans by modifying the number of test stresses (and their levels) and evaluating the allocation of test units. We obtain optimal test plans by utilising the differential evolution (DE) optimisation algorithm, where the inputs to the objective function are the test plan parameters, and the output is the RMSE (root mean squared error) of out-of-sample (extrapolated) model predictions. When the life-stress distribution is correctly specified, we show that the optimal number of stress levels is related to the number of model parameters. In terms of test unit allocation, we show that the proportion of test units is inversely related to the stress level. Our general simulation framework provides an alternative approach to theoretical optimisation, and is particularly favourable for large/complex multipoint test plans where analytical optimisation could prove intractable. Our procedure can be applied to a broad range of experimental scenarios, and serves as a useful tool to aid practitioners seeking to maximise component lifetime information through accelerated life testing.

翻译：加速寿命试验是一种通过暴露于极端应力条件来缩短组件寿命的方法。获取寿命信息的这种方法涉及测试实验的设计，即加速试验方案。在本研究中，我们采用基于仿真的方法，为具有多个设计点的恒定应力加速寿命试验获取最优试验方案。在此仿真框架内，我们可以通过修改测试应力数量（及其水平）并评估测试单元的分配，轻松评估各种试验方案。我们利用差分进化优化算法获得最优试验方案，其中目标函数的输入为试验方案参数，输出为样本外（外推）模型预测的均方根误差。当寿命-应力分布被正确设定时，我们证明最优应力水平数量与模型参数数量相关。在测试单元分配方面，我们证明测试单元的比例与应力水平呈反比关系。我们的通用仿真框架为理论优化提供了一种替代方法，特别适用于分析优化可能难以处理的大型/复杂多点试验方案。我们的程序可应用于广泛的实验场景，并作为辅助从业者通过加速寿命试验最大化组件寿命信息的有用工具。