A fully integrated double-loop approach to the design of statistically and energy efficient accelerated life tests

Accelerated Life Testing (ALT) has been widely used in reliability estimation for highly reliable products. To improve the efficiency of ALT, many optimum ALT design methods have been developed. However, most of the existing methods solely focus on the reliability estimation precision without considering the significant amounts of energy consumed by the equipment that creates the harsher-than-normal operating conditions in such experiments. In order to warrant the reliability estimation precision while reducing the total energy consumption, this article presents a fully integrated double-loop approach to the design of statistically and energy-efficient ALT experiments. As an important option, the new experimental design method is formulated as a multi-objective optimization problem with three objectives: (i) minimizing the experiment's total energy consumption; (ii) maximizing the reliability estimation precision; and (iii) minimizing the tracking error between the desired and actual stress loadings used in the experiment. A controlled elitist non-dominated sorting genetic algorithm is utilized to solve such large-scale optimization problems involving computer simulation. Numerical examples are provided to demonstrate the effectiveness and possible applications of the proposed experimental design method. Compared with the traditional and sequential optimal ALT planning methods, this method further improves the energy and statistical efficiency of ALT experiments.