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首页> 外文期刊>Computers & Industrial Engineering >Condition based maintenance policy for series-parallel systems through Proportional Hazards Model: A multi-stage stochastic programming approach
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Condition based maintenance policy for series-parallel systems through Proportional Hazards Model: A multi-stage stochastic programming approach

机译：基于比例风险模型的串并联系统基于状态的维护策略：多阶段随机规划方法

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摘要

Condition based maintenance for series-parallel systems is studied in this paper. Due to the effect of covariate values on the component's deterioration, proportional hazard model would be adopted to model hazard rate of each component in the whole system. A control limit is determined at each inspection point for each component to minimize a total expected cost during planning horizon subject to reliability constraint of the whole series parallel system. Because, covariates play a stochastic role in the proportional hazard model and the maintenance planning has a sequential nature, we would employ a multi-stage stochastic programming to model CBM for series-parallel systems. Different from other studies that researchers attempt to present a fixed control limit at the start point maintenance planning, this paper presents an optimal control limit per each inspection point and provides flexible dynamic control limits. Due to curse of dimensionality, a novel hybrid meta-heuristic algorithm constructed by Parallel Genetic Algorithm and Invasive weed optimization is proposed to find efficient control limits for each component over planning horizon. Its efficiency would be compared with some other classical meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimization and Invasive Weed Optimization. The results of the computational experiments are statistically discussed and indicate that the proposed hybrid algorithm outperforms the other mentioned algorithms.
机译：本文研究了串并联系统的基于状态的维护。由于协变量值对部件退化的影响，将采用比例风险模型来模拟整个系统中每个部件的风险率。受制于整个串联并联系统的可靠性约束，在每个检查点的每个组件确定一个控制极限，以最大程度地减少计划范围内的总预期成本。因为协变量在比例风险模型中起随机作用，并且维护计划具有顺序性质，所以我们将采用多阶段随机规划来为串并联系统建模。与其他研究人员试图在起点维护计划中提出固定控制限值的其他研究不同，本文提出了每个检查点的最佳控制限值，并提供了灵活的动态控制限值。由于维数的诅咒，提出了一种由并行遗传算法和入侵杂草优化构造的新型混合元启发式算法，以寻找规划范围内每个组件的有效控制极限。它的效率将与其他一些经典的元启发式算法进行比较，例如遗传算法，粒子群优化和入侵杂草优化。对统计实验的结果进行了统计讨论，表明所提出的混合算法优于其他算法。

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