Abstract
A simple step-stress accelerated life testing plan with two stress variables is considered, when the failure times in each level of stress follow the lognormal distribution. The lognormal distribution is commonly used to model certain types of data that arise in several fields of engineering such as, for example, different types of lifetime data or coefficients of wear and friction. The problem of choosing the optimal times to change the stress level is investigated by minimizing the asymptotic variance of the reliability estimate and maximizing the determinant of Fisher information matrix. In this paper, we obtain the optimal bivariate step-stress accelerated life test using both the criteria. Due to the nonlinearity and complexity of problem, the particle swarm optimization algorithm is developed to calculate the optimal hold times. In this method, the research speed is very fast and the optimization ability is more. To illustrate the effect of the initial estimates on the optimal values, sensitivity analysis is performed. Finally, numerical studies are discussed to illustrate the proposed criterion. Simulation results show that the proposed optimum plan is robust.
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