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基于可靠性的设备维修计算机辅助决策系统研究

Research on Decision Support System of Equipment Maintenance Based on Reliability Theory

【作者】 李盛

【导师】 周炳海; 樊建成;

【作者基本信息】 上海交通大学 , 工业工程, 2009, 硕士

【摘要】 随着工业的快速发展,定期维修所忽略的问题逐步暴露,这样的维修方式己经不能满足我国企业的要求。维修优化在现代工业一直发挥着重要作用,它用于提高设备的可靠性,降低维修运行费用。因此,维修优化多年来一直是维修管理领域最活跃的研究课题之一。在众多的维修优化方法中,以可靠性为中心的维修(Reliability-centered Maintenance,简称RCM)是目前国外最广泛采用的维修优化方法。本文从维修优化发展历程出发,阐述了RCM在工业应用的必然。并在介绍RCM发展历史和在国外工业领域应用情况的基础上,通过RCM方法研究,将RCM分析过程与实际维修过程结合,建立一套基于可靠性的设备维修优化决策流程和数学模型,以及详细的系统运作过程,开发一套基于可靠性的设备维修计算机辅助决策软件。最后以宝钢1880步进加热炉为例,结合设备的资产数据以及历史维修数据,加热炉业务的实际情况,利用本文开发的基于可靠性的设备维修计算机辅助决策软件,分析计算关键设备的最佳预防性维修周期,制定出具体的维修计划。

【Abstract】 With the rapid development of industrials, the problems of schedule maintenance, which method has not been able to meet demands of factories in our country, are beginning to appear. The optimization for maintenance are paying a significant role in the modern industrial, improving the reliability of plants and saving cost of maintenance and operation. Thus, the optimization for maintenance is always one of the most important and active parts in the maintenance management fields. Considering many a method used in the optimization for maintenance, reliability-centered maintenance (RCM) is the most popular one used home and abroad.Based on the history of development of the optimization for maintenance, the thesis accounts for the reason why it is necessary that RCM is used in industrial. During this process, the thesis states the applications of RCM abroad. Then, the analysis process of RCM and the process of practical maintenance are combined according to RCM theory. We point out the math model and decision process of the optimization for plant’s maintenance based on reliability theory. After then, the software program is developed to support decision processes.Finally, according to data about plant’s capital, maintenance records and practical conditions and taking use of the decision support system, the thesis takes No. 1880 furnace of Baosteel Group as an example, analyses the optimization cycle of predictive maintenance and forms the special maintenance plan.

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