Reliability Optimization Design of a Full-Scale Aircraft Static Test System Under Uncertainty

To enhance reliability design quality and operational support capability of the full-scale aircraft static test system (FSASTS), a reliability optimization design framework based on reliability analysis, reliability allocation, multi-objective optimization, and uncertainty analysis was proposed. Firstly, a reliability model of the FSASTS and its sub-functional systems was established using the GO methodology to achieve reliability assessment at both system and sub-system levels. Secondly, the analytic hierarchy process (AHP) was employed to allocate reliability indices, clarifying the reliability design requirements for each sub-functional system. Subsequently, by incorporating constraints such as component reliability parameters and their precision ranges, as well as the probability that system reliability design results meet the specified requirements, a multi-objective reliability optimization design model for the FSASTS was constructed, with the objective functions of maximizing system reliability, minimizing cost, and maximizing system tolerance. Finally, an improved multi-objective coati optimization algorithm (IMOCOA) was developed, and Monte Carlo simulation was introduced to solve the optimization model, thereby achieving reliability optimization design for the FSASTS under uncertainty. Using a specific FSASTS case as a validation example, the results demonstrate that the proposed method not only met reliability design requirements but also, compared to the traditional NSGA-II, achieved a 26.93 improvement in the Hypervolume (HV) metric and a 42.10 reduction in the Spacing (SP) metric. This indicates the superiority of the proposed method in terms of both convergence and solution quality, thereby verifying its effectiveness.