Comparison of optimal accelerated life tests with competing risks model under exponential distribution

Accelerated life testing (ALT) is the process of testing products by subjecting them to strict conditions in order to observe more failure data in a short time period. In this study, we compare the methods of two-level constant-stress ALT (CSALT) and simple step-stress ALT (SSALT) based on competing risks of two or more failure modes with independent exponential lifetime distributions. Optimal sample size allocation during CSALT and the optimal stress change-time in SSALT are considered based on V- and D-optimality, respectively. Under Type-I censoring, numerical results show that the optimal SSALT outperforms the optimal CSALT in a wide variety of settings. We theoretically also show that the optimal SSALT is better than the optimal CSALT under a set of conditions. A real data example is analyzed to demonstrate the performance of the optimal plans for both ALTs.