间接状态监测下基于设备最小平均费用与残余寿命最优维修决策研究

发布时间：2018-03-01 09:07

本文关键词： 状态维修间接状态监测最小平均维修费用设备残余寿命决策标准　出处：《重庆大学》2011年硕士论文　论文类型：学位论文

【摘要】：现代制造业设备是企业的核心,为了保证生产能够顺利进行,企业多采用状态维修(Condition Based Maintenance)的方式对设备进行保养。状态维修是指在不同时刻点通过对设备运行状态进行跟踪并对与其相关的指标值进行收集以诊断设备的老化情况,最终对其易损件提前置换的一种预防性维修方法。在以往的国内外文献中,许多学者针对直接状态监测的情况提出了最优维修策略,本文旨在针对间接状态监测下劣化状态并不能直接诊断的设备制定费用最优的设备预防性维修策略。其中,设备的故障率采用威布尔比例故障率模型来表示;设备的状态概率采用贝叶斯公式进行计算;决策标准的制定过程采用部分可观测马尔可夫决策过程和动态规划相结合的方法。本文为了讨论的方便引入了决策标准这一概念。事实上,设备预防性维修的决策标准取决于设备的已使用年龄、设备劣化状态的概率分布和设备置换的最小平均维修费用。本文假设设备的劣化状态用马尔可夫链表示,并运用贝叶斯规则和隐马尔可夫模型建立了指标值和老化状态之间的随机概率关系。对于决策标准中最小平均维修费用的确定本文引入了递归计算方法,并通过算例验证了该迭代方法的合理性,而且对比了直接和间接状态监测下的设备长期平均费用,第三章最后还研究了参数取值不同条件下的设备长期平均维修费用的变化特性。鉴于传统维修决策模型中较少考虑设备可用度这一问题,本文第四章还给出了在设备的老化状态不能直接测得但通过状态监测可获得某些有用信息情况下的设备可靠度函数及其残余寿命的建模方法。最后通过具体案例说明了间接状态监测下基于最小维修费用和残余寿命相结合的方法制定最优维修策略的实施步骤,运用最小维修成本和残余寿命相结合的方法制定设备状态维修决策标准不仅可以节约维修费用还可以有效防止设备突然停机。
[Abstract]:Modern manufacturing equipment is the core of the enterprise, in order to ensure the smooth progress of production, Enterprises often use condition condition Based maintenance to maintain equipment. State repair refers to track the running status of equipment at different points and collect the related index value to diagnose the aging of equipment. Finally, it is a preventive maintenance method to replace the damaged parts in advance. In the previous literature at home and abroad, many scholars put forward the optimal maintenance strategy for direct condition monitoring. The purpose of this paper is to establish an optimal preventive maintenance strategy for equipment which can not be directly diagnosed under indirect condition monitoring, in which the failure rate of the equipment is expressed by Weibull proportional failure rate model. The state probability of the equipment is calculated by Bayesian formula, and the decision-making standard is formulated by combining the partially observable Markov decision process with dynamic programming. In this paper, the concept of decision criteria is introduced for the convenience of discussion. In fact, the decision criteria for preventive maintenance of equipment depend on the age at which the equipment is in service. The probability distribution of equipment deterioration state and the minimum average maintenance cost of equipment replacement. The stochastic probability relationship between the index value and the aging state is established by using the Bayesian rules and the hidden Markov model. The recursive calculation method is introduced to determine the minimum average maintenance cost in the decision standard. The rationality of the iterative method is verified by an example, and the long-term average cost of the equipment under direct and indirect condition monitoring is compared. In the third chapter, the variation characteristics of the long-term average maintenance cost of the equipment with different parameters are studied. In view of the fact that equipment availability is less considered in traditional maintenance decision models, In chapter 4th, a modeling method of reliability function and residual life of equipment under the condition that the aging state of equipment can not be directly measured but some useful information can be obtained by state monitoring is also given. Finally, a concrete example is given. The implementation steps of making optimal maintenance strategy based on the combination of minimum maintenance cost and residual life under indirect condition monitoring are explained. The method of combining minimum maintenance cost and residual life can not only save the maintenance cost but also prevent the sudden shutdown of the equipment.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH165.3

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