🤖 AI Summary
This paper addresses the challenge of optimal design for multi-stress-level constant-stress accelerated life testing (ALT). Methodologically, it proposes a simulation-based global optimization framework that integrates differential evolution (DE) with Monte Carlo simulation, optimizing both stress-level selection and test unit allocation jointly to minimize the root-mean-square error (RMSE) of model extrapolation. The study reveals two key insights: (i) an intrinsic matching relationship between the optimal number of stress levels and model parameters, and (ii) an inverse-proportional relationship between test unit allocation ratios and corresponding stress levels. These findings provide a scalable, surrogate-based optimization pathway for high-dimensional, complex ALT designs. Experimental results demonstrate substantial improvements in lifetime prediction accuracy and testing efficiency—particularly for large-scale, multi-point ALT scenarios where analytical solutions are intractable.
📝 Abstract
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.