Selective Maintenance Modeling for a Multi-State System Considering Human Reliability

Zhonghao Zhao, Xiaoyuan Yan, B. Xiao, Xiaotong Sun
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引用次数: 5

Abstract

In an actual industrial environment, many systems need to perform multiple missions in succession. In order to ensure the success of next mission, the components require maintenance in maintenance breaks. In this case, there may not be enough time and cost to repair every component. Therefore, selective maintenance is required between two consecutive missions. In a multi-state system, each component also has multiple working states, which makes many maintenance options during maintenance break possible. But different maintenance workers will affect the states of components after maintenance. The purpose of this paper is to calculate the human error rate for maintenance of multi-state systems and give the reliability of performing next mission. For the selective maintenance of multi-state systems considering human reliability, with constraints of maintenance time and cost, an optimization model with the goal of maximum system reliability is presented, and the optimal selective maintenance strategy is given by genetic algorithm (GA). Finally, an example is presented to illustrate the effect of human reliability on the final maintenance strategy.
考虑人的可靠性的多状态系统选择维护建模
在实际的工业环境中,许多系统需要连续执行多个任务。为了确保下一次任务的成功,这些部件需要在维修间隙进行维护。在这种情况下,可能没有足够的时间和成本来修复每个组件。因此,需要在两次连续任务之间进行选择性维护。在多状态系统中,每个部件也具有多种工作状态,这使得在维修中断期间有多种维护选择成为可能。但不同的维修人员会影响维修后部件的状态。本文的目的是计算多状态系统维护的人为错误率,并给出执行下一个任务的可靠性。针对考虑人的可靠性的多状态系统在维修时间和费用约束下的选择性维修问题,建立了以系统可靠性最大化为目标的优化模型,并利用遗传算法给出了最优的选择性维修策略。最后,通过实例说明了人为可靠性对最终维修策略的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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