摘要
现代生产系统中的设备具有大型化、复杂化、自动化、高速化等特点。生产系统设备的维修对企业的成本控制和生产运营有着重要的影响,是决定生产企业市场竞争力和经济效益的关键因素之一。探索和研究生产系统可靠性的特点和规律,制定行之有效的设备管理策略,提高设备的可靠性和可用性,在企业中的地位日益突显。本文以淀粉生产系统为研究对象,以可靠性预测为基础对该系统的维修决策进行了理论和实证研究。论文的具体研究内容包括以下几个方面:
(1)在实际维修中,预防性维修大都是不完全维修,预防性维修不能使设备“修复如新”,而且随着维修次数和设备役龄的增加,设备的可靠性逐渐下降。为描述设备可靠性变化的实际情况,本文对设备役龄递减因子和故障率递增因子进行综合,建立了设备混合型故障率演化规则模型。在此基础上,分别以设备可靠度和最大可用度为约束条件建立了单目标维修策略模型,同时还建立了更为灵活的多目标动态预防性维修模型,具有一定的创新性和实用性。利用这三种模型可求在役设备的最佳预防性维修时间间隔和维修次数。应用企业实际生产中的维修数据对三种模型进行验证结果表明:通过这些策略模型得到的结果能够满足企业决策的需要,三种模型中多目标动态维修模型更为灵活有效。这些模型可为企业的设备维修管理决策提供支持,具有一定的实用价值。
(2)对系统可靠性进行预测是研究可靠性变化规律的重要内容之一,时刻掌握系统的可靠性水平对生产计划安排和设备维修决策有着重要的指导意义。本文在介绍可靠性预测数学模型的基础上,对中粮生化能源(XXX)有限公司淀粉加工车间净化工段的生产线进行可靠性建模。建模时把净化工段看成由多个设备串联的系统,按照串联系统的规律和特点,利用系统可靠性仿真软件BlockSim对其可靠性进行仿真。仿真结果表明:系统的整体可靠性水平随生产的时间增加而递减;对系统可靠度影响最大的设备是电子秤,如果要提高系统可靠性,必须首先考虑提高电子秤的可靠性。最后,在预防性维修条件下,对系统平均可用度的变化规律进行了仿真研究,仿真结果表明:预防性维修时间间隔的大小对系统的平均可用性具有较大的影响,当间隔较小时,系统可用性水平也较高,反之,系统可用性水平下降。
(3)备件是设备维修保障的物质基础。科学的备件管理是提高设备管理效益,保证设备可靠性和完好性的核心内容之一。本文以确定淀粉生产系统维修备件消耗量为目标,对备件消耗量的预测问题进行了研究。首先阐述了备件消耗标准确定基本概念和方法。同时,介绍了适合用于备件消耗量预测的各种模型与方法。最后利用二次移动平均法、三次指数平滑法、指数曲线趋势法、ARMA过程方法对轴承6312的消耗量进行建模拟合,对拟合结果用跟踪信号TS进行判断结果表明:三次指数平滑法、指数曲线趋势法、ARMA过程方法所建模型更合理,且ARMA模型的TS=1.27为最小,故说明用ARMA过程方法所建模型的精度最高,故选取该方法作为对轴承6321的预测方法。从预测的结果可以看出利用ARMA模型得到预测值与样本的原始观测值很接近,精度符合企业的要求,由此可知用ARMA过程对备件月消耗量预测是有效的。ARMA模型对资料的要求简单,仅需要某个变量的历史数据,模型简单,且有着严格的数学保证,在短期预测方面有着明显的优势。这种方法可以作为企业设备管理对备件消耗量预测的方法之一。
(4)本文在理论研究的基础上,针对中粮淀粉生产线的具体应用背景为企业开发了设备维修及备件管理系统,使企业能及时准确地掌握所有设备、备件的使用和维修状况,降低设备的维修费用,提高设备的可靠性,实现设备故障和维修等各种信息的实时管理与监控。文中所开发的系统是中粮生化能源(XXX)有限公司的专用系统,主要目标是通过建立一个企业设备维修和备件管理平台,使企业能够实时收集、监测、分析和处理设备、备件、维修人员、维修活动等与设备维修密切相关的各方面数据。系统采用B/S构架,所采用的数据库为Oracle数据库,实行三层数据结构管理,底层开发采用Asp.net技术。通过所开发的系统,可以收集到完整的设备运行状态信息,积累设备维修数据,对加强设备管理,防止故障的发生,保持设备高效、安全的运行,有着重要的理论意义和实践价值。同时,运用该系统能有效地控制设备的维修活动,监测备件的库存水平,使企业管理层能实时获取设备维修信息、备件库存信息,为管理层对生产中的维修决策提供参考依据。
本文研究工作的创新点在于:
(1)整合设备役龄递减因子和故障率递增因子建立符合设备实际衰退趋势的混合型故障率演化规则模型。分别构建以可靠度为约束条件和设备最大可用度为约束条件的单目标预防性维修策略模型,同时在单目标预防性维修策略模型基础上,建立了多目标动态预防性维修策略模型,使其更能满足设备可靠性的要求,为设备的维修决策提供有力的支持。
(2)在运用可靠性框图方法对生产线系统进行简化的基础上,利用可靠性仿真软件BlockSim对生产系统的可靠性变化规律和不同预防维修条件下系统的可用性进行了仿真,为进一步探索系统可靠性的发展规律奠定了基础。
(3)运用多种数学模型对备件消耗量进行预测,通过对比分析,找到了最适合于中粮生化能源(XXX)有限公司生产实际的预测方法和模型。
The equipments of modern manufacturing system have a series of features such asmaximized, complicated, automated, precision, multi-function, high-speed. Themaintenance of equipment of manufacturing system has great important effect on themanufacturing cost and production run of companies, which is the key factor of thecompetitive abilities and economic benefit of production companies. It is increasinglyimportant to explore and research on the rules of reliability of manufacturing system, toestablish effective equipment management strategies and to improve the reliability andavailability of equipment. Based on starch production line as the research object, theoreticaland empirical research on maintenance decision of starch production system based onreliability prediction are studied in this paper. The main contents of this paper are asfollows:
(1) The actual preventive maintenance mostly belongs to imperfect maintenance, which cannot make the equipments as good as new. With the increase of maintenance number andequipment ages, the reliability of equipments declines increasingly. Aiming at the abovefeature, the optimizing models of preventive maintenance strategies of single equipment areset up by integrating the age reduction factor and the failure rate increase factor. On thisbasis, respectively by equipment reliability and maximum availability as constraintconditions to build up a single goal maintenance strategy model. Furthermore,it have certaininnovation and practical, to build up more flexible multi-objective dynamic preventivemaintenance model. With the three models can be obtain the optimal time interval andfrequency of equipment preventive maintenance. With maintenance data from the enterpriseactual production to verify three models. The results shows that these strategie models cancan satisfy the demand of enterprise decision. In these models, multi-objective dynamicmaintenance model is more flexible and effective. These models can afford effective supporton the preventive maintenance decisions of the equipment in production companiesTherefore, this model has a certain practical values.
(2) The system reliability prediction is one of the important contents of the study of thereliability change rule. It has an important guiding significance to equipment maintenancedecision that to grasp the system reliability level. Based on the reliability prediction mathematical model, to build up reliability model for the starch processing workshoppurification section production line of the Cofco Biochemical Energy (XXX) Co., LTD.When modeling the purification section be regarded as a series system by multipleequipment, according to the series system rules and characteristics, its reliability simulationbe performed by the system reliability simulation software-BlockSim. The simulationresults show that the whole system reliability level is decreasing with production timeincreases; the biggest influence on the system reliability of equipment is electronic scale, ifwe want to improve system reliability, must first consider improving the reliability of theelectronic scale. Finally, the preventive maintenance condition system average availabilitychanging law be simulated, the simulation results show that the preventive maintenanceinterval size has great influence on the system average availability, when the interval islesser, the system availability level is higher, otherwise, the system availability level down.
(3) Spare parts is the material base of the equipment maintenancet. Scientific spare partsmanagement is one of the core content to improve the equipment management efficiency, toensure that equipment reliability. This paper to determine maintenance spare partsconsumption of starch production system as the goal,to study the problem of spare partsconsumption prediction. First, the basic concepts and methods to determine the spare partsconsumption standard be introduced. At the same time, all kinds of spare parts consumptionforecast model and method be introduced. Finally, using the secondary moving averagemethod, three exponential smoothing method, index curve trend method, ARMA processmethod to the consumption of bearing6312to build simulation close, with tracking signaljudgment fitting, results show that three exponential smoothing method, index curve trendmethod, the ARMA model process method is reasonable, and TS=1.27is minimum in theARMA model. The precision of the ARMA process method is the highest, so choose themethod to forecast the consumption of bearing6321. The predicted results show that we getpredicted values is very close the original observed value of the sample by using ARMAmodel, its accuracy accord with the requirements of the enterprise. Thus it is effective thatto prediction spare parts monthly consumption in ARMA processes. ARMA model formaterial requirements is simple, need only a variable of historical data, and has a strictmathematical guarantee.It has obvious advantages in short-term prediction. This method canbe used to predicted spare parts consumption in enterprise equipment management.
(4) Based on the theoretical research, this paper development the equipment maintenanceand spare parts management system for the equipment in the starch production line.By thissystem, the enterprise can timely and accurate grasp of use and maintenance condition of all equipment, spare parts, reducing the equipment maintenance cost, improving equipmentreliability, realizing various kinds of real-time information management and monitoring ofequipment failure and maintenance, etc. The development system is the special system forthe Cofco Biochemical Energy (XXX) Co., LTD. Tthrough the establishment of enterpriseequipment maintenance and spare parts management platform, the enterprise be able totimely collect, monitoring, analysis and processing equipment, spare parts, maintenancecrew, maintenance activities and equipment maintenance closely related to all aspects of thedata. The system adopts B/S framework, the database for Oracle database, a three layer datastructure management, base development using Asp.net technology. Through the developedsystem, can collect to complete the running state of the equipment information, accumulateequipment maintenance data, to strengthen equipment management, to prevent theoccurrence of fault, keep equipment efficiency, safe operation, and has important theoreticalsignificance and practical value. At the same time, using this system can effectively controlequipment maintenance activities, monitoring spare parts inventory, make the enterprisemanagement can real-time acquisition equipment maintenance information, spare partsinventory information for the production decision to provide the reference.
The innovation points in this paper are as follows:
(1) With the age reduction factor and the failure rate increase factor to establish the mixedfault evolution rule model,it conforms to the actual equipment recession trend. Respectivelyto construct the reliability constraints and equipment maximum availability constraints forsingle objective preventive maintenance strategy model, at the same time, multi-objectivedynamic preventive maintenance strategy model is established based on a single targetpreventive maintenance strategy model. It more can meet the requirements of the equipmentreliability, to provides powerful support for equipment maintenance decision.
(2) Applying the reliability block diagram method to simplify production line system, at thesame time, to simulate reliability of the production system and system availability underdifferent preventive maintenance.
(3) The utilization many kinds of mathematical model to forecast the spare partsconsumption, through the contrast analysis, find the most suitable forecast method andmodel for the Cofco Biochemical Energy (XXX) Co., LTD.
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