论文总字数:15529字
摘 要
在保养和维修方面,国内多数企业的主流任是采用古老的维修管理方法,然而这种维修保养方法不仅无法避免制药设备的经常性故障,长此以往,甚至会严重影响生产过程的安全性以及药品生产的稳定性。参考各国学者的研究,完成了预防性维修周期间故障率的递推关系式,建立了有限时间区间内的设备预防性维修策略的非线性优化模型[1]。由于对大量的参数进行的分析和强烈的非线性依赖关系,对于这些参数的处理与优化调度最优的组合搜索是一个非常艰巨的任务。由于这些原因,遗传算法可能是一个合适的优化技术被使用。该模型相较于其他模型的优越之处在于将维修、预防性维修和生产损失这三者的成本进行综合考虑,同时解决了无限时间区间稳态分析操作性差这一困扰人们多时的问题,以故障分布的形式,用遗传算法对Weibull分布的设备优化。根据计算结果,遗传算法能以极快的收敛速度达到全局最优,计算效率较高,且具有可操作性。该模型能够在制定维修计划和进行现场作业的调度时提供决策和信息支持,使决策更加理性、明智。
关键词:制药设备;预防;维修;周期;遗传算法
Study on the method of preventive repair of the pharmaceutical production equipment of GMP compliant
Abstract
For the maintenance of pharmaceutical equipment,our pharmaceutical production enterprises still remain in the stage of equipment pair.This repair management mode could not avoid frequent equipment failures,and will cause serious influence on the stability of drug production.Combined with the research results of scholars at home and abroad,to establish the nonlinear optimization model of preventive repair policy during the week of preventive repair failure rate of recurrence relation is given a finite time interval of equipment.Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies,the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules.For these reasons,genetic algorithms (GA) may be an appropriate optimization technique to be used.The model takes into account the cost of repair,preventive repair cost and production cost over the infinite time interval analysis of steady state operation of the shortcomings of the poor and the fault distribution in the form of Weibull distribution equipment were optimized by genetic algorithm.The calculation results show that the computational efficiency of genetic algorithm can converge fast to reach the global optimum with high.This model can be used in the formulation of plans and on-site repair scheduling to provide information and support,make decision-making more rational,sensible.
Keywords: pharmaceutical equipment; prevention; repair; cycle; genetic algorithm
目 录
摘 要 …………..………………………………………………………………………. . . …….…………Ⅰ
Abstract ………………………………………………………………………………. .………….…………Ⅱ
第一章 引 言 ………………………………………………………………. …….…………….…………1
1.1 研究背景…..………………………………………………………………. . . .…………..…………1
1.2 现状与设备维修方式 …………………………………………………………………….…………1
1.2.1 事后维修 ……………………………...…………………………………………….…………1
1.2.2 预防维修 ……………………………...…………………………………………….…………1
1.2.3 预知维修 ……………………………...…………………………………………….…………1
第二章 状态维修研究…………………………………………………………………….…. …….…………3
2.1 状态维修的优点 ………………………………………………. …………..………. ….…………3
2.2 状态维修实施方法 ……………………………………………. …………..………. ….. …………3
2.2.1 设备状态检测………………………...…………………………………………….. …………3
2.2.2 故障诊断…………………………...……………………………………………. .……………4
第三章 概率模型 ………………………………………………………………………. ……..……………5
3.1 预防性维修的概述 ……………………………………………. …………..………. .……………5
3.2 预防性维修调度在组件级可靠性模型…………………………………..…. ………. .……………5
3.3 全球维修政策评估的系统 ………………………………. .……………………..…..……………6
3.4 成本模型………………………………………………. .……………………….…..….……………6
第四章 遗传模型 ……………………………………………………………………………..………. ……8
4.1 基因型问题 ………………………………………. .………………………..……. . .……. .………8
4.2 目标函数………………………………………….…………………………..………..……. ………8
4.3 优化模型……………………..………….………. ………………………………. …..……. ………8
第五章 案例分析 …………………………………….…………………………………….…….…………10
5.1 案例研究 ………………………………………. .………………………………...………………10
5.2 结果分析 ……………………………………. .…………………………….……..………………11
第六章 结论与展望 ……………………………………………………………………….……. …………12
6.1 结 论 …………………………………….….……………………………………….… …………12
6.2 展望与不足……………………………….….………………………………………..…. …………12
致 谢 …………………..…..………………………………………………………………………...……13
参考文献(References) ……………………….……………………………………...………. ...……………14
第一章 引 言
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