Opportunistic Replacement Optimization for Multi-Component System Based on Programming Theory
Opportunistic Replacement Optimization for Multi-Component System Based on Programming Theory
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DOI：
10.1007/s12204-018-2026-6
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发表时间：
2018-12
影响因子：
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通讯作者：
Lei Xiao;Tangbin Xia
中科院分区：
文献类型：
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作者：
Lei Xiao;Tangbin Xia
人们普遍认为，对系统进行过多或过少的维护操作都是消耗性的或有潜在风险的。本文研究了一个多部件系统的机会更换优化问题，在该系统中，没有故障或悬挂历史可以用来预测系统中所有的关键部件。首先，使用实时传感器数据预测剩余使用寿命（RUL），这是基于“基于个体的寿命推断”的方法。提出了一种基于退化程度和服役时间的失效风险估计方法。随后，根据拟议的当期费率，对可能更换的组成部分组合进行比较。最后，选择最佳的替换调度。通过仿真数据集和PHM-2012竞赛轴承数据集对该框架进行了验证。进行了群体替代和个体替代的比较，并进行了敏感性分析。
It is widely accepted that too excessive or too insufficient maintenance actions on a system are consumptive or potentially risky. This paper focuses on the optimization of opportunistic replacement for a multicomponent system in which no failure or suspension histories can be used for prediction of all the critical components in the system. Firstly, the remaining useful life (RUL) is predicted using the real-time sensor data, which is based on an “individual-based lifetime inference” method. Then a failure risk estimation method is introduced, which is based on the degradation extent and service time of components. Subsequently, the possible replacement combinations of components are compared, which is based on a proposed current-term cost rate. Finally, the best replacement scheduling is selected. The proposed framework is validated by the simulation dataset and PHM-2012 competition bearing dataset. Group replacement and individual replacement are conducted for comparison, and sensitivity analysis is discussed.