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Reliability allocation model and algorithm for phased mission systems with uncertain component parameters based on importance measure

机译：基于重要性测度的不确定零件参数分阶段任务系统的可靠性分配模型和算法

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This paper presents a model to deal with reliability allocation problem of phased mission systems (PMS), especially for PMS with uncertainty in components' parameters due to inaccurate information and different phase environments. In practice, real value of the reliability of a component may become lower than its designed value due to such uncertainty, thus making the whole system fail to meet the required reliability level. Therefore, in this paper, we present a model that incorporates the component uncertainty in the system reliability allocation process and then propose a variance-based global importance hybrid heuristic algorithm for its solution. The variance-based global importance measure is used to evaluate the importance of a component to the mission reliability of PMS while the reliabilities of all components vary randomly. The main procedures of the proposed algorithm include: (1) generate feasible solutions by roulette wheel selection method based on a global importance index; (2) improve solutions by adjusting reliability parameter values of components according to their global importance; and (3) improve solutions by crossover and mutation operations of genetic algorithm (GA). To illustrate the effectiveness of the proposed model and algorithm, two examples of PMS are presented and the allocation solutions are validated through Monte-Carlo simulation method. Finally, we compare the proposed model with a general allocation model that does not consider component uncertainty, and additionally with a cost-based heuristic algorithm and a particle swarm optimization (PSO) algorithm. Our results show that component uncertainty has significant influence on the confidence level that an allocation solution satisfies the required system reliability. Hence, it is essential to consider component uncertainty in reliability allocation process. In comparison with the cost heuristic algorithm and the PSO algorithm, the proposed algorithm is more effective in reliability allocation of PMS with uncertainty in components' parameters.
机译：本文提出了一个模型来处理分阶段任务系统（PMS）的可靠性分配问题，特别是对于由于信息不准确和相位环境不同而导致组件参数不确定的PMS。实际上，由于这种不确定性，组件可靠性的实际值可能会低于其设计值，从而使整个系统无法满足所需的可靠性水平。因此，在本文中，我们提出了一个在系统可靠性分配过程中纳入组件不确定性的模型，然后提出了基于方差的全局重要性混合启发式算法进行求解。基于差异的全局重要性度量用于评估组件对PMS任务可靠性的重要性，而所有组件的可靠性随机变化。该算法的主要过程包括：（1）基于全局重要性指数的轮盘选择方法生成可行解。 （2）通过根据组件的全局重要性调整可靠性参数值来改进解决方案； （3）通过遗传算法（GA）的交叉和变异运算来改进解决方案。为了说明所提模型和算法的有效性，给出了两个PMS实例，并通过蒙特卡洛仿真方法对分配方案进行了验证。最后，我们将提出的模型与不考虑组件不确定性的一般分配模型进行了比较，此外还与基于成本的启发式算法和粒子群优化（PSO）算法进行了比较。我们的结果表明，组件的不确定性对置信解决方案满足所需系统可靠性的置信度有重大影响。因此，在可靠性分配过程中必须考虑组件的不确定性。与成本启发式算法和PSO算法相比，该算法在不确定零件参数的PMS可靠性分配中更为有效。

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