基于可靠性预测的淀粉生产系统维修决策理论与实证研究
|
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.
（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.
引文
[1]谢里阳,王正,周金宇,等.机械可靠性基本理论与方法[M].北京市:科学出版社,2012.
[2]陈刚.减速机械的可靠性维修及其维修决策研究[D].武汉理工大学,2007.
[3]孔繁森,陈钦龙,刘鹏.装配生产线人因质量事故的追溯方法[J].北京工业大学学报,2010(10):1297-1304.
[4]吴玲娥.以可靠性为中心的现代设备维修策略[J].管理工程学报,1992(3):13-23.
[5]臧铁钢.先进维修技术资源信息支持系统的研究与开发[D].武汉理工大学,2003.
[6]A英凯利.工业生产的维修管理[M].北京:机械工业出版社,1982.
[7]颜景杰.HSE对设备管理的影响研究[D].天津大学,2004.
[8]郭俊义.黑岱沟露天煤矿设备状态监测与诊断[J].露天采矿技术,2009(4):57-60.
[9]王军.黑岱沟露天矿设备管理与状态维修[D].辽宁工程技术大学,2005.
[10]李葆文.现代设备资产管理[M].北京市:机械工业出版社,2006.
[11]陈涛.备件库存决策支持系统及库存模型研究[D].华中科技大学,2004.
[12]苍安裕.发动机缸体生产线维修决策方法研究[D].吉林大学,2006.
[13]姜国钰.ISO9000标准在设备管理、使用、维修中的应用[J].冶金标准化与质量,1998(1):32-33.
[14]李东进.德国维修领域标准化工作简介[J].机械工业标准化与质量,1998(6):39-40.
[15] Wang H. A survey of maintenance policies of deteriorating systems[J]. EuropeanJournal of Operational Research,2002,139(3):469-489.
[16] Pham H, Wang H. Imperfect maintenance[J]. European Journal Of OperationalResearch,1996,94:425-438.
[17] Lewis E E. Introduction to reliability engineering,Second Edition,[M]. New York:John Wiley&Sons,Inc.,1994.
[18]简绍勇.关于蒙特卡罗方法在高等数学中的应用[J].新余高专学报,2008(2):72-75.
[19] Lewis M W, Steinberg L. Maintenance of mobile mine equipment in theinformation age[J]. Journal of Quality in Maintenance Engineering,2001,7(4):264-274.
[20] Nakagawa T. Sequential imperfect preventive maintenance policies.[J]. IEEETransactions on Reliability,1988,37(3):295-298.
[21]韩帮军,范秀敏,马登哲.基于可靠度约束的预防性维修策略的优化研究[J].机械工程学报,2003(6):102-105.
[22] Barlow R, Hunter L. Optimum preventive maintenance policies[J]. OperationsResearch,1960.
[23] Wang H, Pham H. Some maintenance models and availability with imperfectmaintenancein production systems[J]. Annals of Operations Research,1999,91:305-318.
[24] Wang H, Pham H. Optimal maintenance policies for several imperfect repairmodels[J]. International Journal of Systems Science,1996,27(6):543-549.
[25] Jiang R, Ji P. Age replacement policy: A multi-attribute value model[J]. ReliabilityEngineering and System Safety,2002,76(3):311-318.
[26]孔令丞,贾东兰,陈力.设备大修周期和更新期的优化分析[J].地质技术经济管理,1999(2):29-32.
[27] Bergman B. Optimal replacement under a general failure model[J]. Advances inApplied Probability,1978,10(2):431-451.
[28] Malik M A K. Reliable preventive maintenance policy[J]. AIIE Transactions,1979,11(3):221-228.
[29] Jayabalan V, Chaudhuri D. Optimal maintenance and replacement policy for adeteriorating system with increased mean downtime[J]. Naval Research Logistics,1992,39(1):67-78.
[30]韩帮军,范秀敏,马登哲,等.用遗传算法优化制造设备的预防性维修周期模型[J].计算机集成制造系统-CIMS,2003(3):206-209.
[31] Drinkwater R W, Hastings N V J. An economic replacement model[J]. OperationalResearch Quarterly,1967,18:121-138.
[32] Yun W Y, Bai D S. Cost limit replacement policy under imperfect repair.[J].Reliability engineering,1987,19(1):23-28.
[33] Beichelt F. A replacement policy based on limits for the repair cost rate[J]. IEEETransactions on Reliability,1982,31(4):401-403.
[34] Nakagawa T, Osaki S. The optimum repair limit replacement policies[J].Operational Research Quarterly,1974,25:311-317.
[35] Nguyen D G, Murthy D N P. A note of the repair limit replacement policy[J].Journal of Operational Research Society,1980,31:1103-1104.
[36] Dagpunar J S. New approach for solving repair limit problems[J]. European Journalof Operational Research,1999,113(1):137-146.
[37] Dohi T, Ashioka A, Kaio N, et al. Statistical estimation algorithms for repairs-timelimit replacement scheduling under earning rate criteria: Computers andMathematics with Applications,2006[C].
[38] Dohi T, Matsushima N, Kaio N, et al. Nonparametric repair-limit replacementpolicies with imperfect repair[J]. European Journal of Operational Research,1997,96(2):260-273.
[39] Jack N. Repair replacement modelling over finite time horizons[J]. Journal of theOperational Research Society,1991,42(9):759-766.
[40] Muth E J. OPTIMAL DECISION RULE FOR REPAIR VS REPLACEMENT.[J].IEEE Transactions on Reliability,1977,R-26(3):179-181.
[41] Makis V, Jardine A K S. Optimal replacement of a system with imperfect repair[J].Microelectronics Reliability,1991,31(2-3):381-388.
[42] Makis V, Jardine A K S. Note on optimal replacement policy under generalrepair[J]. European Journal of Operational Research,1993,69(1):75-82.
[43] Makis V, Jarding A K S. Optimal replacement policy for a general model withimperfect repair[J]. Journal of the Operational Research Society,1992,43(2):111-120.
[44] Wildeman R E, Dekker R, Smit A C J M. Dynamic policy for groupingmaintenance activities[J]. European Journal of Operational Research,1997,99(3):530-551.
[45] Assaf D, Shanthikumar J G. Optimal group maintenance policies with continuousand periodic inspections[J]. Management Science,1987,33:1440-1450.
[46] Das K, Lashkari R S, Sengupta S. Machine reliability and preventive maintenanceplanning for cellular manufacturing systems[J]. European Journal of OperationalResearch,2007,183(1):162-180.
[47] de Smidt Destombes K S, van der Heijden M C, van Harten A. Availability ofk-out-of-N systems under block replacement sharing limited spares and repaircapacity[J]. International Journal of Production Economics,2007,107(2):404-421.
[48] Radner R, Jorgenson D W. Opportunistic replacement of a single part in the presentof several monitored parts[J]. Management Science,1963,10:70-84.
[49] McCall J J. Operating characteristics of opportunistic replacement and inspectionpolicies[J]. Management Science,1963,10:85-97.
[50] Bris R, Chatelet E, Yalaoui F. New method to minimize the preventivemaintenance cost of series-parallel systems[J]. Reliability Engineering and SystemSafety,2003,82(3):247-255.
[51]王辉,范秀敏,严隽琪.考虑机会维修的等风险预防性维修策略优化[J].机械设计与研究,2003(3):51-55.
[52]王凌.维修决策模型和方法的理论与应用研究[D].浙江大学,2007.
[53] Suryadi H, Papageorgiou L G. Optimal maintenance planning and crew allocationfor multipurpose batch plants[J]. International Journal of Production Research,2004,42(2):355-377.
[54] Crespo Marquez A, Sanchez Heguedas A. Models for maintenance optimization: Astudy for repairable systems and finite time periods[J]. Reliability Engineering andSystem Safety,2002,75(3):367-377.
[55]朱昌铭,金永杰.有摩擦弹性接触问题的边界元——数学规划法[J].上海交通大学学报,1993(1):34-40.
[56]孔繁森,于骏一,潘志刚.极限切削宽度的模糊随机可靠性分析[J].应用力学学报,1999(4):52-57.
[57]孔繁森,王军,孙海港.基于层次分析法的发动机缸体生产线设备可用性的模糊综合评价[J].吉林大学学报(工学版),2008(6):1332-1336.
[58]孔繁森,刘鹏,曹阳华,等.变速箱厂生产作业环境的模糊综合评价[J].吉林大学学报(工学版),2010(2):475-479.
[59]方春明,孔繁森,隽志才.基于模糊综合评判与灰色关联分析的汽车工业第三方物流服务商评价指标体系[J].吉林大学学报(工学版),2009(S1):133-137.
[60] Sergaki A, Kalaitzakis K. A fuzzy knowledge based method for maintenanceplanning in a power system[J]. Reliability Engineering and System Safety,2002,77(1):19-30.
[61] Mechefske C K, Wang Z. Using fuzzy linguistics to select optimum maintenanceand condition monitoring strategies[J]. Mechanical Systems and Signal Processing,2003,17(2):305-316.
[62] Packianather M S, Drake P R, Rowlands H. Optimizing the parameters ofmultilayered feedforward neural networks through Taguchi design ofexperiments[J]. Quality and Reliability Engineering International,2000,16(6):461-473.
[63] Bhide V M, Piovoso M J, Kosanovich K A. Statistics on reliability of neuralnetwork estimates: Proceedings of the American Control Conference, Seattle, WA,USA,1995[C].
[64]郭孔辉,孔繁森,宗长富.遗传算法在汽车操纵稳定性评价及结构参数优化中的应用[J].机械工程学报,2000(10):34-36.
[65] Cavory G, Dupas R, Goncalves G. A genetic approach to the scheduling ofpreventive maintenance tasks on a single product manufacturing production line[J].International Journal of Production Economics,2001,74(1-3):135-146.
[66] Kim K J, Han I. Maintaining case-based reasoning systems using a geneticalgorithms approach[J]. Expert Systems with Applications,2001,21(3):139-145.
[67]孔繁森,苍安裕,邹青.发动机缸体生产线维修策略的可拓分析方法[J].计算机集成制造系统,2007(9):1847-1851.
[68] Chen H, Varshney P K, Michels J H, et al. Can addition of noise improvedistributed detection performance?20069th International Conference onInformation Fusion, FUSION, Florence, Italy,2006[C].
[69]翁妙凤.解Job-shop调度问题的混合模拟退火进化规划[J].信息与控制,1999(2).
[70] Barata J, Soares C G, Marseguerra M, et al. Simulation modelling of repairablemulti-component deteriorating systems for 'on condition' maintenanceoptimisation[J]. Reliability Engineering and System Safety,2002,76(3):255-264.
[71] Marseguerra M, Zio E, Podofillini L. Condition-based maintenance optimization bymeans of genetic algorithms and Monte Carlo simulation[J]. ReliabilityEngineering and System Safety,2002,77(2):151-166.
[72] Dopazo J F, Merrill H M. OPTIMAL GENERATOR MAINTENANCESCHEDULING USING INTEGER PROGRAMMING.[J]. IEEE Transactions onPower Apparatus and Systems,1975,PAS-94(5):1537-1545.
[73] Iakovou E, Ip C M, Koulamas C. Machining economics with phase-type distributedtool lives and periodic maintenance control[J]. Computers and Operations Research,1996,23(1):53-62.
[74] Zuern H H, Quintana V H. GENERATOR MAINTENANCE SCHEDULING VIASUCCESSIVE APPROXIMATIONS DYNAMIC PROGRAMMING.[J]. IEEETransactions on Power Apparatus and Systems,1975,PAS-94(2):665-671.
[75] Dahal K P, Burt G M, McDonald J R, et al. GA/SA-based hybrid techniques for thescheduling of generator maintenance in power systems: Proceedings of the IEEEConference on Evolutionary Computation, ICEC, California, CA, USA,2000[C].
[76]陈健.基于空间划分的搜索算法[D].山东大学,2005.
[77]简炜.物流过程的建模和优化方法研究[D].浙江大学,2004.
[78] Munoz A, Martorell S, Serradell V. Genetic algorithms in optimizing surveillanceand maintenance of components[J]. Reliability Engineering and System Safety,1997,57(2):107-120.
[79]蔡文,杨春燕,林伟初.可拓工程方法[M].北京:科学出版社,1997.
[80]孙支合,付经东.公路工程项目评价中的可拓方法应用[J].建筑技术开发,2009(12):59-60.
[81]潘全科,朱剑英.基于进化算法和模拟退火算法的混合调度算法[J].机械工程学报,2005(6):224-227.
[82]刘爱华,范世东.采用模拟退火算法优化维修周期模型[J].船海工程,2005(3):38-40.
[83] Kim H, Hayashi Y, Nara K. Performance of hybridized algorithm of GA SA andTS for thermal unit maintenance scheduling: Proceedings of the IEEE Conferenceon Evolutionary Computation, Perth, Aust,1995[C].
[84]郭波等.系统可靠性分析[M].长沙市:国防科技大学出版社,2002.
[85]周经伦,孙权,冯静.非单调关联系统可靠性技术[M].长沙市:国防科技大学出版社,2008.
[86]董聪,郦正能,何庆芝.系统可靠性预测方法[J].强度与环境,1996(1):47-53.
[87]董聪,戎海武,夏人伟.一种新的用于结构系统可靠度计算的Monte Carlo仿真算法[J].机械强度,1994(1):6-8.
[88]任工昌,王党席,苗新强.改进灰色模型在刀具状态监控中的研究与应用[J].机械设计与制造,2010(9):71-73.
[89]任工昌,王党席,苗新强.基于灰色系统理论的数控机床可靠性预计研究[J].机械设计与制造,2010(5):191-193.
[90]张爱华,任工昌,燕向阳.基于灰色关联度分析的数控机床主轴系统故障诊断方法[J].机械设计与制造,2010(11):160-161.
[91]韩红彩.灰色系统预测的研究与分析[J].中国科技信息,2010(14):29-30.
[92] J·莫布雷英,石磊,谷宁昌译.以可靠性为中心的维修[M].北京:机械工业出版社,1995.
[93]蒋祖华,奚立峰等编著.工业工程实践案例及方法[M].北京:清华大学出版社,2008.
[94]贾希胜著.以可靠性为中心的维修决策模型[M].北京:国防工业出版社,2007.
[95]周晓军,奚立峰,李杰.一种基于可靠性的设备顺序预防性维护模型[J].上海交通大学学报,2005(12):2044-2047.
[96]埃贝灵.可靠性与维修性工程概论An Introduction to Reliability andMaintainaility Engineering英文eng[M].北京市:清华大学出版社,2008.
[97]郭永基.可靠性工程原理[M].北京市:清华大学出版社；施普林格出版社,2002.
[98]黄祥瑞.可靠性工程[M].北京市:清华大学出版社,1990.
[99]康锐等埃贝灵著.可靠性与维修性工程概论翻译版[M].北京市:清华大学出版社,2010.
[100]卢明银,徐人平.系统可靠性[M].北京:机械工业出版社,2008.
[101]余国林,魏本成.关于上、下极限的一个新定理[J].大学数学,2007(5):163-166.
[102]沈伟琴,陈铁云.极限上下限定律的对偶性和规划问题[J].上海交通大学学报,1989(3):37-46.
[103]吴杰,朱培勇,吴新星.拓扑空间上映射的上极限与下极限[J].西南师范大学学报(自然科学版),2012(7):24-28.
[104]陈路生,陈长进,张鉴祖.轴胶子质量上下限的估计[J].山西大学学报(自然科学版),1990(3).
[105]赖斯,卢秀玉.蒙特卡罗方法与拟蒙特卡罗方法解线性方程组[J].东华大学学报(自然科学版),2010(2):224-228.
[106]刘林祥,戚盛达.工业设备管理[M].北京市:清华大学出版社,1989.
[107]严广乐,张宁,刘媛华.系统工程[M].北京市:机械工业出版社,2008.
[108] ZdzislawBrzezniak, TomaszZastawniak.随机过程基础英文[M].北京市:清华大学出版社,2009.
[109]张波,张景肖.应用随机过程[M].北京市:清华大学出版社,2004.
[110]吴京慧,杜宾,杨波. Oracle数据库管理及应用开发教程[M].北京市:清华大学出版社,2007.
[111] Kauffman John,高猛Bradley Millington著. ASP.NET2.0数据库入门经典特别版[M].北京市:清华大学出版社,2007.
[112]王剑南等.软件B/S前台开发[M].北京市:清华大学出版社,2012.
[113]陈卫. B/S结构应用程序开发秘笈[M].北京市:国防工业出版社,2001.
[114]任泰明.基于B/S结构的软件开发技术[M].西安市:西安电子科技大学出版社,2006.
[115]孔凡航. Oracle9i中文版基础教程[M].北京市:清华大学出版社,2007.
[116]龚赤兵等. Visual Studio2008中的LINQ开发技术[M].北京市:机械工业出版社,2008.
[117]兰多夫,加德纳. Visual Studio2008高级编程[M].北京市:清华大学出版社,2009.
[2]陈刚.减速机械的可靠性维修及其维修决策研究[D].武汉理工大学,2007.
[3]孔繁森,陈钦龙,刘鹏.装配生产线人因质量事故的追溯方法[J].北京工业大学学报,2010(10):1297-1304.
[4]吴玲娥.以可靠性为中心的现代设备维修策略[J].管理工程学报,1992(3):13-23.
[5]臧铁钢.先进维修技术资源信息支持系统的研究与开发[D].武汉理工大学,2003.
[6]A英凯利.工业生产的维修管理[M].北京:机械工业出版社,1982.
[7]颜景杰.HSE对设备管理的影响研究[D].天津大学,2004.
[8]郭俊义.黑岱沟露天煤矿设备状态监测与诊断[J].露天采矿技术,2009(4):57-60.
[9]王军.黑岱沟露天矿设备管理与状态维修[D].辽宁工程技术大学,2005.
[10]李葆文.现代设备资产管理[M].北京市:机械工业出版社,2006.
[11]陈涛.备件库存决策支持系统及库存模型研究[D].华中科技大学,2004.
[12]苍安裕.发动机缸体生产线维修决策方法研究[D].吉林大学,2006.
[13]姜国钰.ISO9000标准在设备管理、使用、维修中的应用[J].冶金标准化与质量,1998(1):32-33.
[14]李东进.德国维修领域标准化工作简介[J].机械工业标准化与质量,1998(6):39-40.
[15] Wang H. A survey of maintenance policies of deteriorating systems[J]. EuropeanJournal of Operational Research,2002,139(3):469-489.
[16] Pham H, Wang H. Imperfect maintenance[J]. European Journal Of OperationalResearch,1996,94:425-438.
[17] Lewis E E. Introduction to reliability engineering,Second Edition,[M]. New York:John Wiley&Sons,Inc.,1994.
[18]简绍勇.关于蒙特卡罗方法在高等数学中的应用[J].新余高专学报,2008(2):72-75.
[19] Lewis M W, Steinberg L. Maintenance of mobile mine equipment in theinformation age[J]. Journal of Quality in Maintenance Engineering,2001,7(4):264-274.
[20] Nakagawa T. Sequential imperfect preventive maintenance policies.[J]. IEEETransactions on Reliability,1988,37(3):295-298.
[21]韩帮军,范秀敏,马登哲.基于可靠度约束的预防性维修策略的优化研究[J].机械工程学报,2003(6):102-105.
[22] Barlow R, Hunter L. Optimum preventive maintenance policies[J]. OperationsResearch,1960.
[23] Wang H, Pham H. Some maintenance models and availability with imperfectmaintenancein production systems[J]. Annals of Operations Research,1999,91:305-318.
[24] Wang H, Pham H. Optimal maintenance policies for several imperfect repairmodels[J]. International Journal of Systems Science,1996,27(6):543-549.
[25] Jiang R, Ji P. Age replacement policy: A multi-attribute value model[J]. ReliabilityEngineering and System Safety,2002,76(3):311-318.
[26]孔令丞,贾东兰,陈力.设备大修周期和更新期的优化分析[J].地质技术经济管理,1999(2):29-32.
[27] Bergman B. Optimal replacement under a general failure model[J]. Advances inApplied Probability,1978,10(2):431-451.
[28] Malik M A K. Reliable preventive maintenance policy[J]. AIIE Transactions,1979,11(3):221-228.
[29] Jayabalan V, Chaudhuri D. Optimal maintenance and replacement policy for adeteriorating system with increased mean downtime[J]. Naval Research Logistics,1992,39(1):67-78.
[30]韩帮军,范秀敏,马登哲,等.用遗传算法优化制造设备的预防性维修周期模型[J].计算机集成制造系统-CIMS,2003(3):206-209.
[31] Drinkwater R W, Hastings N V J. An economic replacement model[J]. OperationalResearch Quarterly,1967,18:121-138.
[32] Yun W Y, Bai D S. Cost limit replacement policy under imperfect repair.[J].Reliability engineering,1987,19(1):23-28.
[33] Beichelt F. A replacement policy based on limits for the repair cost rate[J]. IEEETransactions on Reliability,1982,31(4):401-403.
[34] Nakagawa T, Osaki S. The optimum repair limit replacement policies[J].Operational Research Quarterly,1974,25:311-317.
[35] Nguyen D G, Murthy D N P. A note of the repair limit replacement policy[J].Journal of Operational Research Society,1980,31:1103-1104.
[36] Dagpunar J S. New approach for solving repair limit problems[J]. European Journalof Operational Research,1999,113(1):137-146.
[37] Dohi T, Ashioka A, Kaio N, et al. Statistical estimation algorithms for repairs-timelimit replacement scheduling under earning rate criteria: Computers andMathematics with Applications,2006[C].
[38] Dohi T, Matsushima N, Kaio N, et al. Nonparametric repair-limit replacementpolicies with imperfect repair[J]. European Journal of Operational Research,1997,96(2):260-273.
[39] Jack N. Repair replacement modelling over finite time horizons[J]. Journal of theOperational Research Society,1991,42(9):759-766.
[40] Muth E J. OPTIMAL DECISION RULE FOR REPAIR VS REPLACEMENT.[J].IEEE Transactions on Reliability,1977,R-26(3):179-181.
[41] Makis V, Jardine A K S. Optimal replacement of a system with imperfect repair[J].Microelectronics Reliability,1991,31(2-3):381-388.
[42] Makis V, Jardine A K S. Note on optimal replacement policy under generalrepair[J]. European Journal of Operational Research,1993,69(1):75-82.
[43] Makis V, Jarding A K S. Optimal replacement policy for a general model withimperfect repair[J]. Journal of the Operational Research Society,1992,43(2):111-120.
[44] Wildeman R E, Dekker R, Smit A C J M. Dynamic policy for groupingmaintenance activities[J]. European Journal of Operational Research,1997,99(3):530-551.
[45] Assaf D, Shanthikumar J G. Optimal group maintenance policies with continuousand periodic inspections[J]. Management Science,1987,33:1440-1450.
[46] Das K, Lashkari R S, Sengupta S. Machine reliability and preventive maintenanceplanning for cellular manufacturing systems[J]. European Journal of OperationalResearch,2007,183(1):162-180.
[47] de Smidt Destombes K S, van der Heijden M C, van Harten A. Availability ofk-out-of-N systems under block replacement sharing limited spares and repaircapacity[J]. International Journal of Production Economics,2007,107(2):404-421.
[48] Radner R, Jorgenson D W. Opportunistic replacement of a single part in the presentof several monitored parts[J]. Management Science,1963,10:70-84.
[49] McCall J J. Operating characteristics of opportunistic replacement and inspectionpolicies[J]. Management Science,1963,10:85-97.
[50] Bris R, Chatelet E, Yalaoui F. New method to minimize the preventivemaintenance cost of series-parallel systems[J]. Reliability Engineering and SystemSafety,2003,82(3):247-255.
[51]王辉,范秀敏,严隽琪.考虑机会维修的等风险预防性维修策略优化[J].机械设计与研究,2003(3):51-55.
[52]王凌.维修决策模型和方法的理论与应用研究[D].浙江大学,2007.
[53] Suryadi H, Papageorgiou L G. Optimal maintenance planning and crew allocationfor multipurpose batch plants[J]. International Journal of Production Research,2004,42(2):355-377.
[54] Crespo Marquez A, Sanchez Heguedas A. Models for maintenance optimization: Astudy for repairable systems and finite time periods[J]. Reliability Engineering andSystem Safety,2002,75(3):367-377.
[55]朱昌铭,金永杰.有摩擦弹性接触问题的边界元——数学规划法[J].上海交通大学学报,1993(1):34-40.
[56]孔繁森,于骏一,潘志刚.极限切削宽度的模糊随机可靠性分析[J].应用力学学报,1999(4):52-57.
[57]孔繁森,王军,孙海港.基于层次分析法的发动机缸体生产线设备可用性的模糊综合评价[J].吉林大学学报(工学版),2008(6):1332-1336.
[58]孔繁森,刘鹏,曹阳华,等.变速箱厂生产作业环境的模糊综合评价[J].吉林大学学报(工学版),2010(2):475-479.
[59]方春明,孔繁森,隽志才.基于模糊综合评判与灰色关联分析的汽车工业第三方物流服务商评价指标体系[J].吉林大学学报(工学版),2009(S1):133-137.
[60] Sergaki A, Kalaitzakis K. A fuzzy knowledge based method for maintenanceplanning in a power system[J]. Reliability Engineering and System Safety,2002,77(1):19-30.
[61] Mechefske C K, Wang Z. Using fuzzy linguistics to select optimum maintenanceand condition monitoring strategies[J]. Mechanical Systems and Signal Processing,2003,17(2):305-316.
[62] Packianather M S, Drake P R, Rowlands H. Optimizing the parameters ofmultilayered feedforward neural networks through Taguchi design ofexperiments[J]. Quality and Reliability Engineering International,2000,16(6):461-473.
[63] Bhide V M, Piovoso M J, Kosanovich K A. Statistics on reliability of neuralnetwork estimates: Proceedings of the American Control Conference, Seattle, WA,USA,1995[C].
[64]郭孔辉,孔繁森,宗长富.遗传算法在汽车操纵稳定性评价及结构参数优化中的应用[J].机械工程学报,2000(10):34-36.
[65] Cavory G, Dupas R, Goncalves G. A genetic approach to the scheduling ofpreventive maintenance tasks on a single product manufacturing production line[J].International Journal of Production Economics,2001,74(1-3):135-146.
[66] Kim K J, Han I. Maintaining case-based reasoning systems using a geneticalgorithms approach[J]. Expert Systems with Applications,2001,21(3):139-145.
[67]孔繁森,苍安裕,邹青.发动机缸体生产线维修策略的可拓分析方法[J].计算机集成制造系统,2007(9):1847-1851.
[68] Chen H, Varshney P K, Michels J H, et al. Can addition of noise improvedistributed detection performance?20069th International Conference onInformation Fusion, FUSION, Florence, Italy,2006[C].
[69]翁妙凤.解Job-shop调度问题的混合模拟退火进化规划[J].信息与控制,1999(2).
[70] Barata J, Soares C G, Marseguerra M, et al. Simulation modelling of repairablemulti-component deteriorating systems for 'on condition' maintenanceoptimisation[J]. Reliability Engineering and System Safety,2002,76(3):255-264.
[71] Marseguerra M, Zio E, Podofillini L. Condition-based maintenance optimization bymeans of genetic algorithms and Monte Carlo simulation[J]. ReliabilityEngineering and System Safety,2002,77(2):151-166.
[72] Dopazo J F, Merrill H M. OPTIMAL GENERATOR MAINTENANCESCHEDULING USING INTEGER PROGRAMMING.[J]. IEEE Transactions onPower Apparatus and Systems,1975,PAS-94(5):1537-1545.
[73] Iakovou E, Ip C M, Koulamas C. Machining economics with phase-type distributedtool lives and periodic maintenance control[J]. Computers and Operations Research,1996,23(1):53-62.
[74] Zuern H H, Quintana V H. GENERATOR MAINTENANCE SCHEDULING VIASUCCESSIVE APPROXIMATIONS DYNAMIC PROGRAMMING.[J]. IEEETransactions on Power Apparatus and Systems,1975,PAS-94(2):665-671.
[75] Dahal K P, Burt G M, McDonald J R, et al. GA/SA-based hybrid techniques for thescheduling of generator maintenance in power systems: Proceedings of the IEEEConference on Evolutionary Computation, ICEC, California, CA, USA,2000[C].
[76]陈健.基于空间划分的搜索算法[D].山东大学,2005.
[77]简炜.物流过程的建模和优化方法研究[D].浙江大学,2004.
[78] Munoz A, Martorell S, Serradell V. Genetic algorithms in optimizing surveillanceand maintenance of components[J]. Reliability Engineering and System Safety,1997,57(2):107-120.
[79]蔡文,杨春燕,林伟初.可拓工程方法[M].北京:科学出版社,1997.
[80]孙支合,付经东.公路工程项目评价中的可拓方法应用[J].建筑技术开发,2009(12):59-60.
[81]潘全科,朱剑英.基于进化算法和模拟退火算法的混合调度算法[J].机械工程学报,2005(6):224-227.
[82]刘爱华,范世东.采用模拟退火算法优化维修周期模型[J].船海工程,2005(3):38-40.
[83] Kim H, Hayashi Y, Nara K. Performance of hybridized algorithm of GA SA andTS for thermal unit maintenance scheduling: Proceedings of the IEEE Conferenceon Evolutionary Computation, Perth, Aust,1995[C].
[84]郭波等.系统可靠性分析[M].长沙市:国防科技大学出版社,2002.
[85]周经伦,孙权,冯静.非单调关联系统可靠性技术[M].长沙市:国防科技大学出版社,2008.
[86]董聪,郦正能,何庆芝.系统可靠性预测方法[J].强度与环境,1996(1):47-53.
[87]董聪,戎海武,夏人伟.一种新的用于结构系统可靠度计算的Monte Carlo仿真算法[J].机械强度,1994(1):6-8.
[88]任工昌,王党席,苗新强.改进灰色模型在刀具状态监控中的研究与应用[J].机械设计与制造,2010(9):71-73.
[89]任工昌,王党席,苗新强.基于灰色系统理论的数控机床可靠性预计研究[J].机械设计与制造,2010(5):191-193.
[90]张爱华,任工昌,燕向阳.基于灰色关联度分析的数控机床主轴系统故障诊断方法[J].机械设计与制造,2010(11):160-161.
[91]韩红彩.灰色系统预测的研究与分析[J].中国科技信息,2010(14):29-30.
[92] J·莫布雷英,石磊,谷宁昌译.以可靠性为中心的维修[M].北京:机械工业出版社,1995.
[93]蒋祖华,奚立峰等编著.工业工程实践案例及方法[M].北京:清华大学出版社,2008.
[94]贾希胜著.以可靠性为中心的维修决策模型[M].北京:国防工业出版社,2007.
[95]周晓军,奚立峰,李杰.一种基于可靠性的设备顺序预防性维护模型[J].上海交通大学学报,2005(12):2044-2047.
[96]埃贝灵.可靠性与维修性工程概论An Introduction to Reliability andMaintainaility Engineering英文eng[M].北京市:清华大学出版社,2008.
[97]郭永基.可靠性工程原理[M].北京市:清华大学出版社；施普林格出版社,2002.
[98]黄祥瑞.可靠性工程[M].北京市:清华大学出版社,1990.
[99]康锐等埃贝灵著.可靠性与维修性工程概论翻译版[M].北京市:清华大学出版社,2010.
[100]卢明银,徐人平.系统可靠性[M].北京:机械工业出版社,2008.
[101]余国林,魏本成.关于上、下极限的一个新定理[J].大学数学,2007(5):163-166.
[102]沈伟琴,陈铁云.极限上下限定律的对偶性和规划问题[J].上海交通大学学报,1989(3):37-46.
[103]吴杰,朱培勇,吴新星.拓扑空间上映射的上极限与下极限[J].西南师范大学学报(自然科学版),2012(7):24-28.
[104]陈路生,陈长进,张鉴祖.轴胶子质量上下限的估计[J].山西大学学报(自然科学版),1990(3).
[105]赖斯,卢秀玉.蒙特卡罗方法与拟蒙特卡罗方法解线性方程组[J].东华大学学报(自然科学版),2010(2):224-228.
[106]刘林祥,戚盛达.工业设备管理[M].北京市:清华大学出版社,1989.
[107]严广乐,张宁,刘媛华.系统工程[M].北京市:机械工业出版社,2008.
[108] ZdzislawBrzezniak, TomaszZastawniak.随机过程基础英文[M].北京市:清华大学出版社,2009.
[109]张波,张景肖.应用随机过程[M].北京市:清华大学出版社,2004.
[110]吴京慧,杜宾,杨波. Oracle数据库管理及应用开发教程[M].北京市:清华大学出版社,2007.
[111] Kauffman John,高猛Bradley Millington著. ASP.NET2.0数据库入门经典特别版[M].北京市:清华大学出版社,2007.
[112]王剑南等.软件B/S前台开发[M].北京市:清华大学出版社,2012.
[113]陈卫. B/S结构应用程序开发秘笈[M].北京市:国防工业出版社,2001.
[114]任泰明.基于B/S结构的软件开发技术[M].西安市:西安电子科技大学出版社,2006.
[115]孔凡航. Oracle9i中文版基础教程[M].北京市:清华大学出版社,2007.
[116]龚赤兵等. Visual Studio2008中的LINQ开发技术[M].北京市:机械工业出版社,2008.
[117]兰多夫,加德纳. Visual Studio2008高级编程[M].北京市:清华大学出版社,2009.