A novel optimal accelerated degradation test design method considering multiple decision variables

In recent years, there has been considerable interest on the part of scientist and engineers in effectively designing accelerated degradation test, which is very useful to assess product's life information with long-life and high-reliability properties. An effective optimal ADT design method can provide a reasonable arrangement of accelerated stress level setting, sample allocation and test time. However, existing researches can hardly optimize all the multiple decision variables simultaneously in an efficient way. In the current study, an effective stochastic process based constant stress-accelerated degradation test (CSADT) model is constructed, and then an optimization design method is proposed based on the CSADT model, where the multiple decision variables involving stress level, sample allocation and test time arrangement for each stress level can be simultaneously optimized based on genetic algorithm (GA) and Monte Carlo simulation. Meanwhile, a comprehensive sensitivity analysis method incorporating orthogonal experimental design, normalization and range analysis is constructed to illustrate the robustness of the optimal design procedure. Finally, an optimal CSADT design for self-regulating heating cables is presented, and comparative results demonstrate the rationality and effectiveness of the proposed method.