全寿命周期下退化的大规模系统预防性维修策略优化
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电子科技大学

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TP273

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国家自然科学基金重点项目 (7153003); 国防基础科学研究计划; 四川省创新推广应用于服务基地建设 (2017IM010700).


Preventive Maintenance Optimization for Deteriorating Large-scale Systems in Life-cycle Perspective
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University of Electronic Science and Technology of China

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    摘要:

    单元退化情形下,考虑全寿命周期的大规模系统可靠性设计与预防性维修策略的综合优化问题将变得更为复杂。针对单元失效服从威布尔分布的情形,考虑多单元联合的预防性维修模式,构建可靠性约束下大规模系统全寿命周期成本优化模型。单元数量众多带来的组合规模指数增长问题将导致非线性择优困难,利用遗传算法编程,快速求解全局最优解,包括设计阶段的单元可靠性和使用阶段的系统预防性维修周期。最后,通过典型算例分析,验证模型与算法的正确性和有效性,探究维修改善因子、单元可靠性和预防性维修周期等决策变量间的相互关系。研究成果有助于简化系统工程师的可靠性工程设计过程,具有一定的理论和应用价值。

    Abstract:

    Under the condition of unit degradation, the comprehensive optimization research of large-scale system reliability design and preventive maintenance strategy considering the life-cycle becomes more complicated. For the case that unit failure obeys Weibull distribution, considering the multi-unit preventive joint maintenance model, the life-cycle cost optimization model of large-scale system under reliability constraint is constructed. The problem of exponential growth of combination size caused by the large number of units leads to the difficulty of nonlinear optimization. Genetic algorithm programming is used to solve the global optimal solution, including the unit reliability in the design stage and the system preventive maintenance period in the operation stage. Finally, through the analysis of typical examples, the correctness and effectiveness of the model and algorithm are verified, and the relationship among decision variables such as maintenance improvement factor, unit reliability and preventive maintenance cycle is explored. The research results are helpful to simplify the process of reliability and maintainability integrated design optimization for system engineers and have certain theoretical and application value.

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历史
  • 收稿日期:2020-12-25
  • 最后修改日期:2021-05-14
  • 录用日期:2021-06-03
  • 在线发布日期: 2021-08-01
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