加工中心可信性影响度分析及增长技术研究
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摘要
数控装备是装备制造业的“工作母机”，是先进制造技术的核心载体，它一方面保障了现代化工业和国防工业的发展，另一方面也成为战略性新兴产业的切入点。是否拥有高性能和高质量的数控装备，已经成为体现一个国家工业化程度和综合实力的标志。近年来随着中国数控装备市场不断扩大，外资企业不断抢滩中国市场，在日趋激烈的市场竞争面前国产数控机床的发展面临巨大压力和严峻挑战。
在严酷的市场竞争中如何保证数控机床长时间在高性能、高精度和高效率下正常运行，即提高数控机床本身的可信性水平，已经成为国内外市场竞争的焦点。可信性既是产品质量的核心又是产品质量的重要体现，目前在国内市场上众多机床生产厂商争夺的焦点仍然是可信性水平，可以说机床市场竞争的根本是可信性的竞争。因此本文在国家自然基金课题“基于全寿命周期的数控机床可用性影响度耦合模型及分析技术”的基础上，以国产加工中心为研究对象，根据可信性现场实验数据建立了加工中心整机可靠性模型、维修性模型、可用性模型；建立了加工中心各子系统可靠性模型、维修性模型、可用性模型；对加工中心子系统的可信性影响度进行分析；最后进行了加工中心可靠性增长研究。
本文主要研究内容如下：
加工中心整机可信性建模。可信性研究需要以大量的现场数据为基础，本文结合课题，通过对15台某系列加工中心进行现场试验，采用定时截尾试验方案获得大量现场实验数据，对所得到的故障数据应用Johnson法进行处理。在进行整机可靠性建模过程中，对以最小二乘法为核心的一元线性回归分析的拟合方向问题首次进行分析，对于x拟合方向和y拟合方向分别经过统计特性分析和蒙特卡洛模拟分析之后，根据所得结论选择合适的拟合方向后对整机可靠性模型进行参数估计，在此基础上应用差分进化算法，以相关系数为目标函数，通过在Matlab中编程对三参数威布尔分布的位置参数进行优化，最后对整机可靠性进行评价。在进行整机维修性建模过程中，在对维修性模型分布类型进行判断之后采用极大似然估计进行参数估计，在建立起两参数的对数正态分布模型后进行维修性评价。在进行整机可用性建模过程中，采用序贯仿真与蒙特卡洛模拟相结合的算法进行可用性的耦合仿真分析，这种方法既体现了序贯仿真的系统状态随时间变化的思想又体现了蒙特卡洛模拟中随机抽样的思想，有效的将可靠性和维修性耦合在可用性之中。根据仿真结果得到整机的瞬时可用度曲线和数学模型。通过整机可信性建模与评价研究，为加工中心整机可靠性、维修性、可用性建模技术提出了更为准确有效的新方法，对该型号加工中心整机可信性模型参数有了准确的估计。
加工中心子系统可信性建模。在子系统建模过程中，由于各子系统的现场试验故障数据较少，针对大样本的普通建模方法很难得到正确的评估结果。因此，对于子系统可靠性模型，本文采用基于Bayes理论的小样本威布尔分布建模方法，并应用差分进化算法对相关系数优化，从而得到威布尔分布的参数估计值。对于子系统维修性模型，不但分布于每个子系统的维修数据较少而且关于子系统的维修性先验信息也较少，针对这种情况本文采用一种无需先验信息的对数正态分布小样本建模方法建立了各子系统维修性模型。对于子系统可用性模型，将各子系统视为串联系统前提下，采用基于序贯仿真思想的蒙特卡洛模拟方法进行仿真，根据仿真结果建立各子系统可用性模型。子系统可信性模型的建立，为识别整机中的关键子系统、进一步分析子系统影响度奠定基础。
加工中心子系统可信性影响度分析。以往的影响度研究都是在假设系统为不可修系统的前提下进行的可靠性影响度研究，但实际上加工中心是一个典型的可修系统，针对这种可修系统本文首次采用Natvig影响度对加工中心子系统分别进行了可靠性影响度、维修性影响度、可用性影响度分析，根据影响度分析结果找到对整机可信性影响最为严重的几个子系统。为研究所得各子系统影响度值的精确程度以及影响度值的误差范围，在基于序贯仿真思想的蒙特卡洛模拟方法下，对各个子系统的可靠性影响度、维修性影响度、可用性影响度值分别进行了区间估计并且得到了相应影响度的双侧置信区间和单侧置信下限，区间估计结果验证了所得影响度值的合理性。子系统可信性影响度的研究为有针对性提出加工中心可信性增长措施奠定了基础。进一步的为促进加工中心新产品可信性提高提供了依据。
加工中心可靠性增长研究。对主轴箱、刀库、电气系统这三个影响度最大的关键子系统进行了故障模式分析和故障原因分析。在对这三个关键子系统进行危害度模糊分析时，由于加工中心是典型的机电一体化产品，其故障模式复杂多样，故障原因、故障现象与故障机理间不但存在着随机性而且存在模糊性等十分复杂的不确定性关系，也就是说在危害度分析时存在很大的模糊性。因此本文在进行危害度模糊分析时，将三角-区间型Type-2模糊隶属函数引入危害度模糊分析当中，很好的体现了隶属度函数本身的不确定性使隶属度函数更加准确。在此基础上以区间数的形式表示各因素的隶属度，这样保证了隶属度函数的所有信息不会丢失，而后进行区间数模糊综合评判。对于危害度模糊分析所得区间数结果采用基于TOPSIS思想的区间数排序方法进行排序，最后找到高危害度的故障模式。根据故障分析与危害度分析结果，侧重于主轴箱、刀库、电气系统这三个关键子系统提出了加工中心可靠性设计改进措施，制造装配过程的可靠性保证措施，关键配套件、外购件的可靠性保证措施。
本文在对加工中心整机可靠性模型和维修性模型进行研究之后，采用模拟仿真的方法将可靠性与维修性耦合在可用性当中从而建立起整机可用性模型。针对加工中心子系统故障数据较少的情况，采用小样本方法对各子系统可靠性模型、维修性模型、可用性模型进行研究使所得子系统模型更加真实合理。根据所得子系统模型采用Natvig影响度对子系统可靠性、维修性、可用性影响度计算并进行区间估计，结果表明对整机影响程度较大的子系统为：主轴箱、刀库、电气系统。对这三个关键子系统进行了故障分析与危害度模糊分析，分析结果表明：刀库失调、主轴失调、电气系统元器件损坏等故障模式危害度等级较高。最后侧重于主轴箱、刀库、电气系统对该型号加工中心提出了可靠性设计改进措施，制造装配过程的可靠性保证措施，关键配套件、外购件的可靠性保证措施。
CNC equipments are the “machine tools” of equipment manufacture, important carriersof advanced manufacturing technology. On the one hand, CNC equipments preserve thedevelopment of modern industrial and defense industrys; On the other hand, CNCequipments have also become the entry point of strategic emerging industries. Whether acountry has high performance and high quality CNC equipments, it has become the sign ofdegree of industrialization and comprehensive strength for a country. In recent years, withthe expansion of CNC equipment market, foreign invested enterprises keep grabing the shareof the market. The development of domestic CNC machine tools facing enormous pressureand stern challenges, because the increasingly fierce competition in marketplace.
In the harsh market competition, how can we keep the high performance, high precisionand high efficiency of CNC machine tools in a long time, in other words raise thecreditability level of CNC machine tools, this has become the focus of competition ofdomestic and international market. Creditability is both the core for the quality of productand an important manifestation of the quality of product. The focus of competition for thedomestic market is also the creditability level for numerous machine tool manufacturers.Fundamental of the competition for machine tool market is the competition of the credibility.This article relies on the National Natural Science Foundation of China "The coupling modelof availability influence and analysis techniques for CNC machine tools based on life cycle ",taking domestic machining center as the research object, bases on a large number ofcredibility field data, studies on: the reliability model, maintenance model and availabilitymodel of whole machining center were established; the reliability model, maintainabilitymodel and availability model of subsystem were established; influences of subsystems ofmachining center were analyzed. At last the reliability growth measures for machining centerwere studied.
The studies of this paper are as follows:
The credibility modeling and evaluation for whole machining center. The research ofcredibility need base on a large number of field data, this article combined with subject,15machining centers of a series were field tested. A large number of field test data were got,adopting censored time test program. The failure data was processed using Johnson method.In the whole machine reliability modeling process, the problem of fitting the direction for thelinear regression analysis to least squares method as the core was analyzed for the first time. x fitting direction and y fitting direction were analyzed for statistical properties and MonteCarlo simulation. The parameters of whole machine reliability model were estimated afterselecting the appropriate fitting direction according to the conclusions. Location parameterof three-parameter weibull distribution was optimized, using differential evolution algorithm,taking related coefficient as objective function, programming in Matlab. Finally, reliabilityevaluation of whole machine was given. In the whole machine maintainability modelingprocess, parameters were estimated, using maximum likelihood estimates after deciding thedistribution type of maintainability model. Maintainability evaluation of whole machine wasgiven after establishing the two-parameter lognormal distribution model. In the wholemachine availability modeling process, coupling simulation analysis of the availability wasdone, using the method of combining sequential simulation and Monte Carlo simulation.This method not only reflect the idea that system state changes over time in sequentialsimulation, but also reflect the idea of random sampling in Monte Carlo simulation. Thismethod could combine reliability and maintainability into availability effectively. A moreaccurate and effective new method of reliability, maintainability and availability modelingtechnique for whole machining center, the accurate evaluation of credibility level of wholemachine was given, by credibility modeling and evaluation for whole machining center.
The credibility modeling for subsystem of machining center. In the subsystem modelingprocess, there is less field test failure data, it is difficult to get the accurate results by generalmodeling approach of large sample. Therefore for the reliability models of subsystems, smallsample weibull distribution modeling method based on Bayes theory was adopted. Objectivefunction was optimized by differential evolution algorithm, thus estimation of weibulldistribution parameters were gotten. For the maintainability models of subsystems, not onlydid each subsystem has less maintenance data, but the priori information in maintainabilityof subsystem was less. In view of this situation, maintainability models of each subsystemwere established by lognormal distribution modeling approach of the small sample withoutpriori information. The availability models of subsystems were simulated, using the methodof combining sequential simulation and Monte Carlo simulation, regarding subsystems asseries system. According to the simulation results, the availability model of each subsystemwas established. The credibility model for subsystem was foundations of identifying keysubsystem in whole machine and further analysis influence of subsystem.
The creditability influence analysis of subsystem machining center. In the past, thereliability influence research was on the condition that the system was non-repairable system,but actually machining center is a typical repairable system. For the first time this paper studied reliability influence, maintainability influence and availability influence forsubsystem, using Natvig influence that apply to repairable system. Based on influenceanalysis result, the subsystems which affect credibility of machining center most seriouslywere found out. In order to research on the accuracy of influence and give error range.one-sided confidence limits and two-sided confidence intervals for reliability influence,maintainability influence and availability influence were obtained, using the method ofcombining sequential simulation and Monte Carlo simulation. The result of intervalestimation verified that influence degrees are rational. Influence of subsystem research laythe foundation for targeted failure analysis and further proposing credibility growthmeasures, provide basis that improve the credibility of new product of machining center.
Reliability growth of machining center. The three key subsystem of spindle box, toolmagazine and electrical system failure analysis included failure mode analysis and failurecause analysis. When do the criticality fuzzy analysis of the three key subsystems,machining center is a typical electromechanical integration product, failure modes arediverse, the relationship between failure cause, failure phenomenon and failure mechanism isnot only random but fuzzy and some complex uncertain, in other words there is manyfuzziness in criticality analysis. When fuzzy critical analysis is done in this paper, weintroduce triangle-interval type-2fuzzy membership function into fuzzy critical analysis.The triangle-interval type-2fuzzy membership function reflect the uncertainty of themembership function and make the membership more accurate. On this basis the eachfactor’s degree of membership was expressed in the form of interval numbers, so that all theinformation of membership function was preserved. The result was gotten using fuzzycomprehensive evaluation of interval numbers at last. The interval numbers result of fuzzycritical analysis were prioritized, using interval number sorting method that base on TOPSISidea. Finally, found out the failure modes of high criticality. According to failure analysisresult and criticality analysis result, for spindle box, tool magazine and electrical system,propose reliability design improvement, reliability assurance measures in manufacturingassembly process, reliability assurance measures of key corollary parts and purchased parts.
In this paper, after studying the reliability model and maintainability model ofmachining center, the simulation method is used of reliability and maintainability ofcoupling in availability,in order to establish the whole machine availability model. For theless failure data of machining center subsystem, the subsystem model could be more realisticand reasonable, using a small sample to research the reliability model, maintainability modeland availability model of each subsystem. According to subsystem model, calculate and interval estimate the reliability influence, maintainability influence and usability influenceby Natvig influence, and the results show that the larger influence subsystem for machine:the spindle box, tool magazine, electrical system. After the three key subsystems failureanalysis and criticality fuzzy analysis, the results show that: tool magazine disorder, spindledisorder, components damage have the higher influence for the whole machine. Finally,focus on the spindle box, tool magazine, electrical system, we put forward reliability designimprovement, reliability assurance measures in manufacturing assembly process, reliabilityassurance measures of key corollary parts and purchased parts.
在严酷的市场竞争中如何保证数控机床长时间在高性能、高精度和高效率下正常运行，即提高数控机床本身的可信性水平，已经成为国内外市场竞争的焦点。可信性既是产品质量的核心又是产品质量的重要体现，目前在国内市场上众多机床生产厂商争夺的焦点仍然是可信性水平，可以说机床市场竞争的根本是可信性的竞争。因此本文在国家自然基金课题“基于全寿命周期的数控机床可用性影响度耦合模型及分析技术”的基础上，以国产加工中心为研究对象，根据可信性现场实验数据建立了加工中心整机可靠性模型、维修性模型、可用性模型；建立了加工中心各子系统可靠性模型、维修性模型、可用性模型；对加工中心子系统的可信性影响度进行分析；最后进行了加工中心可靠性增长研究。
本文主要研究内容如下：
加工中心整机可信性建模。可信性研究需要以大量的现场数据为基础，本文结合课题，通过对15台某系列加工中心进行现场试验，采用定时截尾试验方案获得大量现场实验数据，对所得到的故障数据应用Johnson法进行处理。在进行整机可靠性建模过程中，对以最小二乘法为核心的一元线性回归分析的拟合方向问题首次进行分析，对于x拟合方向和y拟合方向分别经过统计特性分析和蒙特卡洛模拟分析之后，根据所得结论选择合适的拟合方向后对整机可靠性模型进行参数估计，在此基础上应用差分进化算法，以相关系数为目标函数，通过在Matlab中编程对三参数威布尔分布的位置参数进行优化，最后对整机可靠性进行评价。在进行整机维修性建模过程中，在对维修性模型分布类型进行判断之后采用极大似然估计进行参数估计，在建立起两参数的对数正态分布模型后进行维修性评价。在进行整机可用性建模过程中，采用序贯仿真与蒙特卡洛模拟相结合的算法进行可用性的耦合仿真分析，这种方法既体现了序贯仿真的系统状态随时间变化的思想又体现了蒙特卡洛模拟中随机抽样的思想，有效的将可靠性和维修性耦合在可用性之中。根据仿真结果得到整机的瞬时可用度曲线和数学模型。通过整机可信性建模与评价研究，为加工中心整机可靠性、维修性、可用性建模技术提出了更为准确有效的新方法，对该型号加工中心整机可信性模型参数有了准确的估计。
加工中心子系统可信性建模。在子系统建模过程中，由于各子系统的现场试验故障数据较少，针对大样本的普通建模方法很难得到正确的评估结果。因此，对于子系统可靠性模型，本文采用基于Bayes理论的小样本威布尔分布建模方法，并应用差分进化算法对相关系数优化，从而得到威布尔分布的参数估计值。对于子系统维修性模型，不但分布于每个子系统的维修数据较少而且关于子系统的维修性先验信息也较少，针对这种情况本文采用一种无需先验信息的对数正态分布小样本建模方法建立了各子系统维修性模型。对于子系统可用性模型，将各子系统视为串联系统前提下，采用基于序贯仿真思想的蒙特卡洛模拟方法进行仿真，根据仿真结果建立各子系统可用性模型。子系统可信性模型的建立，为识别整机中的关键子系统、进一步分析子系统影响度奠定基础。
加工中心子系统可信性影响度分析。以往的影响度研究都是在假设系统为不可修系统的前提下进行的可靠性影响度研究，但实际上加工中心是一个典型的可修系统，针对这种可修系统本文首次采用Natvig影响度对加工中心子系统分别进行了可靠性影响度、维修性影响度、可用性影响度分析，根据影响度分析结果找到对整机可信性影响最为严重的几个子系统。为研究所得各子系统影响度值的精确程度以及影响度值的误差范围，在基于序贯仿真思想的蒙特卡洛模拟方法下，对各个子系统的可靠性影响度、维修性影响度、可用性影响度值分别进行了区间估计并且得到了相应影响度的双侧置信区间和单侧置信下限，区间估计结果验证了所得影响度值的合理性。子系统可信性影响度的研究为有针对性提出加工中心可信性增长措施奠定了基础。进一步的为促进加工中心新产品可信性提高提供了依据。
加工中心可靠性增长研究。对主轴箱、刀库、电气系统这三个影响度最大的关键子系统进行了故障模式分析和故障原因分析。在对这三个关键子系统进行危害度模糊分析时，由于加工中心是典型的机电一体化产品，其故障模式复杂多样，故障原因、故障现象与故障机理间不但存在着随机性而且存在模糊性等十分复杂的不确定性关系，也就是说在危害度分析时存在很大的模糊性。因此本文在进行危害度模糊分析时，将三角-区间型Type-2模糊隶属函数引入危害度模糊分析当中，很好的体现了隶属度函数本身的不确定性使隶属度函数更加准确。在此基础上以区间数的形式表示各因素的隶属度，这样保证了隶属度函数的所有信息不会丢失，而后进行区间数模糊综合评判。对于危害度模糊分析所得区间数结果采用基于TOPSIS思想的区间数排序方法进行排序，最后找到高危害度的故障模式。根据故障分析与危害度分析结果，侧重于主轴箱、刀库、电气系统这三个关键子系统提出了加工中心可靠性设计改进措施，制造装配过程的可靠性保证措施，关键配套件、外购件的可靠性保证措施。
本文在对加工中心整机可靠性模型和维修性模型进行研究之后，采用模拟仿真的方法将可靠性与维修性耦合在可用性当中从而建立起整机可用性模型。针对加工中心子系统故障数据较少的情况，采用小样本方法对各子系统可靠性模型、维修性模型、可用性模型进行研究使所得子系统模型更加真实合理。根据所得子系统模型采用Natvig影响度对子系统可靠性、维修性、可用性影响度计算并进行区间估计，结果表明对整机影响程度较大的子系统为：主轴箱、刀库、电气系统。对这三个关键子系统进行了故障分析与危害度模糊分析，分析结果表明：刀库失调、主轴失调、电气系统元器件损坏等故障模式危害度等级较高。最后侧重于主轴箱、刀库、电气系统对该型号加工中心提出了可靠性设计改进措施，制造装配过程的可靠性保证措施，关键配套件、外购件的可靠性保证措施。
CNC equipments are the “machine tools” of equipment manufacture, important carriersof advanced manufacturing technology. On the one hand, CNC equipments preserve thedevelopment of modern industrial and defense industrys; On the other hand, CNCequipments have also become the entry point of strategic emerging industries. Whether acountry has high performance and high quality CNC equipments, it has become the sign ofdegree of industrialization and comprehensive strength for a country. In recent years, withthe expansion of CNC equipment market, foreign invested enterprises keep grabing the shareof the market. The development of domestic CNC machine tools facing enormous pressureand stern challenges, because the increasingly fierce competition in marketplace.
In the harsh market competition, how can we keep the high performance, high precisionand high efficiency of CNC machine tools in a long time, in other words raise thecreditability level of CNC machine tools, this has become the focus of competition ofdomestic and international market. Creditability is both the core for the quality of productand an important manifestation of the quality of product. The focus of competition for thedomestic market is also the creditability level for numerous machine tool manufacturers.Fundamental of the competition for machine tool market is the competition of the credibility.This article relies on the National Natural Science Foundation of China "The coupling modelof availability influence and analysis techniques for CNC machine tools based on life cycle ",taking domestic machining center as the research object, bases on a large number ofcredibility field data, studies on: the reliability model, maintenance model and availabilitymodel of whole machining center were established; the reliability model, maintainabilitymodel and availability model of subsystem were established; influences of subsystems ofmachining center were analyzed. At last the reliability growth measures for machining centerwere studied.
The studies of this paper are as follows:
The credibility modeling and evaluation for whole machining center. The research ofcredibility need base on a large number of field data, this article combined with subject,15machining centers of a series were field tested. A large number of field test data were got,adopting censored time test program. The failure data was processed using Johnson method.In the whole machine reliability modeling process, the problem of fitting the direction for thelinear regression analysis to least squares method as the core was analyzed for the first time. x fitting direction and y fitting direction were analyzed for statistical properties and MonteCarlo simulation. The parameters of whole machine reliability model were estimated afterselecting the appropriate fitting direction according to the conclusions. Location parameterof three-parameter weibull distribution was optimized, using differential evolution algorithm,taking related coefficient as objective function, programming in Matlab. Finally, reliabilityevaluation of whole machine was given. In the whole machine maintainability modelingprocess, parameters were estimated, using maximum likelihood estimates after deciding thedistribution type of maintainability model. Maintainability evaluation of whole machine wasgiven after establishing the two-parameter lognormal distribution model. In the wholemachine availability modeling process, coupling simulation analysis of the availability wasdone, using the method of combining sequential simulation and Monte Carlo simulation.This method not only reflect the idea that system state changes over time in sequentialsimulation, but also reflect the idea of random sampling in Monte Carlo simulation. Thismethod could combine reliability and maintainability into availability effectively. A moreaccurate and effective new method of reliability, maintainability and availability modelingtechnique for whole machining center, the accurate evaluation of credibility level of wholemachine was given, by credibility modeling and evaluation for whole machining center.
The credibility modeling for subsystem of machining center. In the subsystem modelingprocess, there is less field test failure data, it is difficult to get the accurate results by generalmodeling approach of large sample. Therefore for the reliability models of subsystems, smallsample weibull distribution modeling method based on Bayes theory was adopted. Objectivefunction was optimized by differential evolution algorithm, thus estimation of weibulldistribution parameters were gotten. For the maintainability models of subsystems, not onlydid each subsystem has less maintenance data, but the priori information in maintainabilityof subsystem was less. In view of this situation, maintainability models of each subsystemwere established by lognormal distribution modeling approach of the small sample withoutpriori information. The availability models of subsystems were simulated, using the methodof combining sequential simulation and Monte Carlo simulation, regarding subsystems asseries system. According to the simulation results, the availability model of each subsystemwas established. The credibility model for subsystem was foundations of identifying keysubsystem in whole machine and further analysis influence of subsystem.
The creditability influence analysis of subsystem machining center. In the past, thereliability influence research was on the condition that the system was non-repairable system,but actually machining center is a typical repairable system. For the first time this paper studied reliability influence, maintainability influence and availability influence forsubsystem, using Natvig influence that apply to repairable system. Based on influenceanalysis result, the subsystems which affect credibility of machining center most seriouslywere found out. In order to research on the accuracy of influence and give error range.one-sided confidence limits and two-sided confidence intervals for reliability influence,maintainability influence and availability influence were obtained, using the method ofcombining sequential simulation and Monte Carlo simulation. The result of intervalestimation verified that influence degrees are rational. Influence of subsystem research laythe foundation for targeted failure analysis and further proposing credibility growthmeasures, provide basis that improve the credibility of new product of machining center.
Reliability growth of machining center. The three key subsystem of spindle box, toolmagazine and electrical system failure analysis included failure mode analysis and failurecause analysis. When do the criticality fuzzy analysis of the three key subsystems,machining center is a typical electromechanical integration product, failure modes arediverse, the relationship between failure cause, failure phenomenon and failure mechanism isnot only random but fuzzy and some complex uncertain, in other words there is manyfuzziness in criticality analysis. When fuzzy critical analysis is done in this paper, weintroduce triangle-interval type-2fuzzy membership function into fuzzy critical analysis.The triangle-interval type-2fuzzy membership function reflect the uncertainty of themembership function and make the membership more accurate. On this basis the eachfactor’s degree of membership was expressed in the form of interval numbers, so that all theinformation of membership function was preserved. The result was gotten using fuzzycomprehensive evaluation of interval numbers at last. The interval numbers result of fuzzycritical analysis were prioritized, using interval number sorting method that base on TOPSISidea. Finally, found out the failure modes of high criticality. According to failure analysisresult and criticality analysis result, for spindle box, tool magazine and electrical system,propose reliability design improvement, reliability assurance measures in manufacturingassembly process, reliability assurance measures of key corollary parts and purchased parts.
In this paper, after studying the reliability model and maintainability model ofmachining center, the simulation method is used of reliability and maintainability ofcoupling in availability,in order to establish the whole machine availability model. For theless failure data of machining center subsystem, the subsystem model could be more realisticand reasonable, using a small sample to research the reliability model, maintainability modeland availability model of each subsystem. According to subsystem model, calculate and interval estimate the reliability influence, maintainability influence and usability influenceby Natvig influence, and the results show that the larger influence subsystem for machine:the spindle box, tool magazine, electrical system. After the three key subsystems failureanalysis and criticality fuzzy analysis, the results show that: tool magazine disorder, spindledisorder, components damage have the higher influence for the whole machine. Finally,focus on the spindle box, tool magazine, electrical system, we put forward reliability designimprovement, reliability assurance measures in manufacturing assembly process, reliabilityassurance measures of key corollary parts and purchased parts.
引文
[1]贾亚洲,杨兆军.数控机床可靠性国内外现状与技术发展策略[J].中国制造业信息化,2008(4):35-37.
[2]韩向宏.加快我国数控机床产业的高端化发展[J].高科技与产业化,2010(10):110-111.
[3]王黎明.我国中高档数控机床市场潜力巨大[N].机电商报,2010-9-27.
[4]安周. CIMES2010外资企业云集彰显中国机床市场战略地位[N].机电商报,2010-6-5.
[5]陈循介.世界机床发展态势分析与预测[J].北京机床研究所,2011(8):64-67.
[6]黄琴.关于数控机床发展的几点思考[J].科技传播,2010(22):70-74.
[7]唐勇.关于我国数控机床行业发展之路的探讨[J].科技创新导报,2011(21):57-59.
[8]“十一五”到“十二五”我国数控机床发展市场需求[J].现代制造技术与装备,2011(3).
[9]丰笑容.《机床工具行业“十二五”发展规划》出台2015年目标总产值8000亿元[J].机电商报,2011-7-25.
[10]中国机床工具工业协会市场部.2009年中国机床市场需求分析(二)[J].产销市场,2010,6(3).
[11]王立平.关于国产数控机床发展的几点思考[J].航空制造技术,2010(4).
[12]焦炬.浅析数控机床发展现状[J].机电产品开发与创新,2011,24(3):16-18.
[13]技术联结世界,创新推动发展[J].世界制造技术与装备市场,2008,(6):30-37.
[14]卜基桥.专家纵论数控机床发展新趋势[J].现代制造,2007(3):14-14.
[15]梁训瑄.把脉机床产业九大动态[J].装备制造与信息化,2007,(5):69-71.
[16] X.W.Xu,S.T.Newman. Making CNC machine tools more open, interoperable andintelligent-a review of the technologies[J]. Computersin Industry,2006,57(2):141-152.
[17]李众,高柏宏.打破我国数控机床产业发展桎梏[J].数字工厂/制造,2010(7).
[18]贾亚洲,戴怡,张立斌.研究可信性技术提高数控机床的质量与可信性水平[J].世界制造技术与装备市场,2001(3):11-13.
[19]咸奎桐.可信性管理GB/T19000.4(ISO9000-4)实施指南[M].北京:中国标准出版社,1997.
[20]何国伟.可信性工程[M].北京:中国标准出版社,1998:36-60.
[21]李昌琪,遇立基译, A.C.普罗尼科夫.数控机床的精度与可靠性[M].北京:机械工业出版社,1987.
[22]曾声奎.前苏联可靠性工程的几个特点[J].北京航空航天大学学报,1995,4(21):1-6.
[23]方济道.苏联机床工业进展述评[J].西安工业大学学报,1987,4:92-95.
[24] В．С．Васильев、В．В．Барабанов，Обеспечение надёжностиметаллорежущего оборудования 《Станки и инструмент》,1987(7):10-12.
[25] Д．Н．Колониус，Прогнозирование нагруженности станков《Станкииинструмент》,1991(5):10-12.
[26] В．В．Барабанов、А．Л．Чирков，Исследования надёжности работизированныхтехнологическихкомплексов вначальныйпериодэксплуатации《Станкииинструмент》,1990(1):3-4..
[27] Peter F. McGoldrick and Halil Kulluk，Machine Tool Reliability—A Critical Factor inManufacturing Systems[J]. Reliability Engineering,1986,14:205-221.
[28]中村正弘.トラプル防止する加工监视システムと故障诊断システム[J].机械技术,1995,35(8):37-42.
[29] Marina Karyagina,Walter Wong. Life Cycle Cost Modelling Using Marked PointProcesses[J]. Reliability Engineering and System Safety,1998(59):225-238.
[30] Wang Y,Jia Y,Yu J,Yi S. Field failure database of CNC lathes[J]. I International Journalof Quality&Reliability Management,1999,16(4):330-340.
[31] Wang Y,Jia Y,Yu J,Zheng Y,Yi S. Failure probabilistic model of CNC lathes[J].Reliability Engineering and System Safety,1999,65(3):307-314.
[32] Yiqiang Wanga, Richard C. M. Yamb, Ming J. Zuo, Peter Tseb. A comprehensivereliability allocation method for design of CNC lathes[J]. Reliability Engineering andSystem Safety,2001,72(3):247-252.
[33] Yiqiang Wang, Richard C.M. Yam, Ming J. Zuo.A multi-criterion evaluation approachto selection of the best statistical distribution[J]. Computers&Industrial Engineering,2004,47:165-180.
[34] Yiqiang Wang,Yazhou Jia,Jian Qiu,Guixiang Shen. Load Spectra of CNC MachineTools[J]. Quality and Reliability Engineering Interational,2000,16:229-234.
[35]贾亚洲,马健,贾志新.数空机床现场故障分布模型的统计研究[J].数理统计与管理,1996,15(5):24-28.
[36]申桂香,贾亚洲,马健. CNC机床故障分析及其可靠性[J].中国机械工程,1996(6):67-69.
[37]章国栋,陆廷孝.系统可靠性与维修性的分析与设计[M].北京：北京航空航天大学出版社,1990.
[38]赵跃进,董伟燕.一种综合系统固有可用度的新算法及发现[J].北京理工大学学报,2005,25(5):54-57.
[39]曹晋华,程侃.可靠性数学引论[M].北京:北京科学出版社,1986.
[40]孔德良,王少萍.可修系统的可用度分析方法研究[J].北京航空航天大学学报,2002,28(2):129-132.
[41]史定华.可修系统的运行特征[J].数学进展.1996,1:34-42.
[42]张英锋,蒋涛.用马尔柯夫过程分析可修复系统的稳态可用度[J].徐州师范大学学报,2000,18(2):27-28.
[43] Bris R, Chatelet E, Yalaoui F. New method to minimize the Preventive maintenancecost of series-Parallel systems[J]. Reliability Engineering&System Safety,2003.82(3):247-255.
[44]李剑涛,蒋里强,黄立坡.装备最佳预防性维修间隔时间研究[J].装备指挥技术学院学报,2004,15(3):26-29.
[45] Hassett T F, Dietrieh D L, Szidarovszky F. Time-varying failure rates in theavailability&reliability analysis of repairable systems[J]. IEEE Transaetion OnReliability.1995.44:155-160.
[46] SunH, HanJ J. Instantaneous availability and interval availability for systems withtime-varying failure rate: stai-step approximation[C].2001Paeific Rim InternationalSymposium on Dependable Computing (PRDC2001).2001,371-375.
[47]郑治华.故障影响忽略或滞后的单部件马尔可夫可修系统研究[D].北京:北京理工大学,2006.
[48] Sarkar J, Sarkar S. Availability of a Periodically inspected system supported by a spareunit，under perfect repair or Perfect upgrade[J]. Statisties&Probability Letters,2011,53:207-217.
[49] Cui L R, Xie M. Availability of a Periodieally inspected system with random repair orreplacement times[J]. Journal of Statistical Planning and Inference,2005,131:89-100.
[50] Cassady C R, Lyoob I M, Seheider K, etal. A Generic Model of EquipmentAvailability Under Imperfect Maintenance[J]. IEEE Transactions on reliability,2005,54(4):564-571.
[51]张元林.可修的环形相邻2/n(F)系统[J].系统工程理论与实践,1998(7):50-55.
[52]张元林,林辉能.具有马氏相依和维修有优先权的线性相邻2/n(F)可修系统[J].自动化学报,2000,26(3):317-323.
[53]蔡春娥. N个不同部件并联可修系统可用度的分析[J].湖南轻工业高等专科学校学报,2002,14(1):13-15.
[54]史定华.两部件串联系统的可靠性分析[J].福建师范大学学报,1982,2:13-22.
[55]史定华.两部件串联可修模型分析[J].自动化学报,1985,1:71-79.
[56]谢文祥.冗余可修系统可用度的随机Petri网建模与分析[J].系统工程学报,1998,13(4):93-97.
[57]谢文祥.基于随机Petri网可修控制系统的可用度计算[J].西安交通大学学报,1997,31(11):83-87.
[58]黄龙呈,洪延姬,金星等.可修复部件可用度计算的积分方程方法[J].机电产品开发与创新,2007,20(5):16-25.
[59]金星,洪延姬,文明等.服从任意分布的部件可用度快速计算方法[J].中国空间科学技术,2002(5):67-70.
[60]金星,洪延姬.系统可靠性与可用性分析方法[M].北京:国防工业出版社,2007.
[61]肖刚.评估复杂可维修系统可靠度与瞬态可用度的蒙特卡洛方法[J].兵工学报,2002,23(2):46-50.
[62] Mostafa Bacha, Gilles Celeux. Bayesian Estimation of a Weibull distribution in ahighly censored and small sample setting[R]. Institut National de RechercheInformatique et Automatique,1996.
[63]金光,冯静.长寿命卫星活动部件Bayes-Weibull可靠性评估方法[J].系统工程与电子技术,2009,31(8):2020-2025.
[64]金少华.电工产品可靠性评估方法与贝叶斯理论的应用[D].天津:河北工业大学,2002.
[65]刘海涛,张志华.威布尔分布无失效数据的Bayes可靠性分析[J].系统工程理论与实践,2008(11):103-106.
[66]张恒,张志华.基于极小样本的Weibull分布参数估计方法[J].船海工程,2010,39(5):177-179.
[67] Z.W.Birnbaum. On the importance of different component in a multi-componentsystem[J]. in Krisnaiah(Ed), Multi-variate Analysis II, Academic Press,1969:581-592.
[68] H.E.Lambert. Fault trees for decision making in systems analysis[J]. PhDthesis,UCRL-51829. University of California Liver more.1975.
[69] J.B. Fussell. How to hand-calculate systemre-liability and safety characteristics[J].IEEE Trans. Reliability,1975,24:101-106.
[70] Zhi-JiePan, Ya-ChuanTai. Variance importance of system components by MonteCarlo[J]. IEEE Trans.Reliability,1988,37(4).
[71] Patrik Hilber. Component Reliability Importance Indices for Electrical Networks[C].The Proceeding of8th International Power Engineering conference, July2007:258-262.
[72] J.S.Hong, C.H.Lie. Joint reliability importance of two edges in an undirectednetwork[J]. IEEE Truns. Reliability,1993,42:17-33.
[73] Xueli Gao, Lirong Cui. Analysis for joint importance of components in a coherentsystem[J]. European Journal of Operational Research,2007,182:282–299.
[74] Jose F. Espiritu, David W. Coit, Upyukt Prakash. Component criticality importancemeasures for the power industry[J]. Electric Power Systems Research,2007，77:407-420.
[75] Yong Ou. Joanne Bechta Dugan. Sensitiviyt Analysis of Modular Dynamic FaultTree[J]. IEEE,2000:35-43.
[76] Yong Ou. APProximat esensitivity analysis of acyclic Markov reliability models[J].IEEE TRANSACTIONS ON RELIABILITY,2003,52(2).
[77]李青,陆廷金.模糊重要度分析方法的研究[J].模糊系统与数学,2003,14(1):89-93.
[78]王永传,郁文贤,庄钊文.一种故障树模糊重要度分析的新方法[J].国防科技大学学报,1999,21(3):63-65.
[79]李常有,徐敏强.基于改进的topsis的设备重要度分析[J].震动与冲击,2009,28(6).
[80] Wendai Wang, James Loman,Pantelis Vassiliou. Reliability Importance of Componentsin a Complex System the Institute of Electrical and Electronic Engineers[J].PROCEEDINGS Annua RELIABILITY and MAINTAINABILITY Symposium,2004,1:26-29.
[81] Wendai Wang, James M. Loman. Reliability Block Diagram Simulation TechniquesApplied to the IEEE Std.493Standard Network[J]. IEEE TRANSACTIONS ONINDUSTRY APPLICATIONS,2004,40(3).
[82] Natvig. B, J. Gasemyr. New results on the Barlow-Proschan and Natvig measures ofcomponent importance in nonrepairable and repairable systems[J]. Methodol. Comput.Appl. Prob,2009,11.
[83] B. Natvig. The Natvig measures of component importance in repairable systemsapplied to an offshore oil and gas production system[J]. Safety, Reliability and RiskAnalysis: Theory, Methods and Applications,2008,3:2029-2035.
[84]康锐,石荣德. FMECA技术及其应用[M].北京:国防工业出版社,2006.
[85]戴云徽,韩之俊,朱海荣.故障模式及影响分析-FMEA-研究进展[J].中国质量,2007,(10):23-26.
[86] US MIL-STD-1629/notice2. Procedures for performing a failure mode effects andcriticality analysis. US Department of Defence, Washington DC, USA,1984.
[87] Ford Motor Company. Potential Failure Mode and Effects Analvsis In Design (DesignFMEA) and For Manufacturing and Assembly Processes (Process FMEA)Instruction Manual. Dearborn, Michigan,1988.
[88] ECSS-Q-30-02A. Failure modes, Effects and Criticality analysis,.EuropeanCooperation for Space Standardization,2001.
[89] Wang J, Ruxton T, Labrie CR. Design for safety of engineering systems with multiplefailure state variables[J]. Reliab Eng Syst Saf1995;50:271-84.
[90] Sankar NR, Prabhu B S. Modified approach for prioritization of failures in a systemfailure mode and effects analysis[J]. Int J Qual Reliab Manage2001;18(3):324-35.
[91] Bowles JB. Fuzzy logic prioritization of failures in a system failure mode, effects andcriticality analysis[J]. Reliability Engineering&System Safety1995;50:203-13.
[92] Anand Pillay, Jin Wang. Modified failure mode and effects analysis using approximatereasoning[J]. Reliability Engineering&System Safety2003,79:69-85.
[93] Seyed-Hosseini SM, Safaei N. Reprioritization of failures in a system failure mode andeffects analysis by decision making trial and evaluation laboratory technique[J]. ReliabEng Syst Safety2006,91:872-81.
[94]蒋仁言,左明建.可靠性模型与应用[M].北京:机械工业出版社,1999:14-54.
[95]潘吉安.可靠性维修性可用性评估手册[M].北京:国防工业出版社,1995:253.
[96]秦英孝,张耀文,江劲勇等.可靠性维修性保障性管理[M].北京:国防工业出版社,2003:63-99.
[97]贺国芳.可靠性数据的收集与分析[M].北京:国防工业出版社,1995.
[98] Abbasi, Babak, Rabelo, Luis.Hosseinkouchack, Mehdi. Estimating parameters of thethree-parameter Weibull distribution using a neural network[J]. European Journal ofIndustrial Engineering,2008(5):428-445.
[99]张英芝,申桂香.数控车床故障分布规律及可靠性[J].农业机械学报,2006,37(1):156-159.
[100]于捷,申桂香.基于三参数威布尔分布的数控机床的可靠性评价[J].现代制造工程,2007,(5):18-20.
[101] Jia Yazhou, Wang Molin. Probability distribution of machining center failures[J].Reliability Engineering and System Safety,1995,50:121-125.
[102]石瑞平.基于一元回归分析模型的研究[D].石家庄:河北科技大学.2009.
[103] White JS. The Moments of Log-Weibull Order Statistics[J]. Technometrics1969,1(2):65-72.
[104] Mann NR, Schafer RE, Singpurwalla ND. Methods for statistical analysis of reliabilityand life data[M]. New York: Wiley,1974.
[105] Ross R. Comparing Linear Regression and Maximum Likelihood Methods to EstimateWeibull Distributions on Limited Data Sets:Systematic and Random Errors[J].Conference on electrical insulation and dielectric phenomena,1999(1):170-173.
[106] Thoman DR, Bain LJ, Antle E. Inferences on the Parameters of the WeibullDistribution. Technometrics1969,11(3).
[107]张英芝,申桂香.随机截尾数控机床三参数威布尔分布模型[J].吉林大学学报,2009,39(2):378-381.
[108]赵德让,刘瑞元.三参数威布尔分布参数估计的合理性[J].青海师范大学学报(自然科学版),2002(3).
[109] STORN R, PRICE K.Differential Evolution-a Simple and Efficient Adaptive Schemefor Global Optimization over Continuous Spaces[J]. Technical Report, InternationalComputer Science Institute,1995(8):22-25.
[110] Price K, Storn R, Lampinen J. Differential Evolution-A Practical Approach to GlobalOptimization[M]. New York: Springer,2005.
[111]刘波,王凌.差分进化算法研究进展[J].控制与决策,2007(7):721-729.
[112]周艳平.差分进化算法研究进展[J].化工自动化及仪表,2007,34(3):1-5.
[113]吴亮红.差分进化算法及应用研究[D].长沙:湖南大学,2007.
[114]陈家鼎.序贯分析[M].北京:北京大学出版社,1995.
[115]朱一凡,赵新,李群.基于参数稳态判定的两阶段序贯仿真分析策略[J].国防科技大学学报,2011,33(1):120-124.
[116]姚李孝,崔杜武,伍利.基于时序模拟法和Petri网的电气主接线可靠性评估[J].西安理工大学学报,2005,21(1):42-46.
[117]宋晓通.基于蒙特卡罗方法的电力系统可靠性评估[D].济南:山东大学,2008.
[118]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985.
[119]段利民,曾凯尹. MATLAB在模拟法中的应用[J].技术与创新管理,2007,28(03):115-117.
[120]张仙风,吕志鹏.基于MATLAB的蒙特卡罗方法在可靠性设计中的应用[J].装备制造技术,2006,(4):76-78.
[121]欧宜贵,李志林,洪世煌.计算机模拟在数学建模中的应用[J].海南大学学报自然科学版,2004,22(1):89-95.
[122]魏领会.数控机床可用性耦合建模及影响度分析[D].长春:吉林大学,2011.
[123]茆诗松.贝叶斯统计[M].北京:中国统计出版社,1999,10.
[124]朱炜.小样本下正态分布可靠度评估方法[J].质量与可靠性,2006,(6):17-19.
[125]游学民.正态分布的小样本检验及可靠度评估[J].科技信息,2007,(20).
[126]赵选民.数理统计[M].北京:科学出版社,2002.
[127] L.A. Zadeh. The concept of a linguistic variable and its application to approximatereasoning[J]. Information Sciences,1975,8:199-249.
[128] Karnik, Mendel. Introduction to Type-2Fuzzy Logic Systems[C]. In:Proceedings ofIEEE FUZZ Conference, Anchorage AK,1998:915-920.
[129] Jerry M. Mendel. Interval Type-2Fuzzy Logic Systems Made Simple[C]. IEEE Trans.Fuzzy Syst,2006,14(12):808–810.
[130]李海. Type-2模糊系统的比较研究与仿真应用[D].镇江:江苏大学,2006.
[131]李霞.基于新距离测度的区间数排序[J].西华大学学报,2008,27(1):87-89.
[132] Ting-Yu Chen. The interval-valued fuzzy TOPSIS methodand experimental analysis[J].Fuzzy Sets and Systems,2008,159(11):1410-1428.
[133]陈殿生.数控机床可信性应用研究[D].长春:吉林大学,2002.
[134]魏领会.面向生命周期的加工中心可用性保证技术[D].长春:吉林大学,2007.
[135]薛玉霞.可用性关键技术研究[D].长春:吉林大学,2009.
[136]裴咏红.数控机床可靠性增长实用技术[D].长春:吉林大学,2004.
[137]卜波.数控车床可靠性增长技术的应用研究[D].长春:吉林大学,2008.
[138]楚文龙,李英,杰杨冬.基于卧式加工中心主轴箱的可靠性增长技术研究[J].现代制造技术与装备,2011,(2):17-18.
[139]王国强,王玉金.高速数控机床主轴用陶瓷轴承的研究[J].陶瓷,2004,(2):43-45.
[2]韩向宏.加快我国数控机床产业的高端化发展[J].高科技与产业化,2010(10):110-111.
[3]王黎明.我国中高档数控机床市场潜力巨大[N].机电商报,2010-9-27.
[4]安周. CIMES2010外资企业云集彰显中国机床市场战略地位[N].机电商报,2010-6-5.
[5]陈循介.世界机床发展态势分析与预测[J].北京机床研究所,2011(8):64-67.
[6]黄琴.关于数控机床发展的几点思考[J].科技传播,2010(22):70-74.
[7]唐勇.关于我国数控机床行业发展之路的探讨[J].科技创新导报,2011(21):57-59.
[8]“十一五”到“十二五”我国数控机床发展市场需求[J].现代制造技术与装备,2011(3).
[9]丰笑容.《机床工具行业“十二五”发展规划》出台2015年目标总产值8000亿元[J].机电商报,2011-7-25.
[10]中国机床工具工业协会市场部.2009年中国机床市场需求分析(二)[J].产销市场,2010,6(3).
[11]王立平.关于国产数控机床发展的几点思考[J].航空制造技术,2010(4).
[12]焦炬.浅析数控机床发展现状[J].机电产品开发与创新,2011,24(3):16-18.
[13]技术联结世界,创新推动发展[J].世界制造技术与装备市场,2008,(6):30-37.
[14]卜基桥.专家纵论数控机床发展新趋势[J].现代制造,2007(3):14-14.
[15]梁训瑄.把脉机床产业九大动态[J].装备制造与信息化,2007,(5):69-71.
[16] X.W.Xu,S.T.Newman. Making CNC machine tools more open, interoperable andintelligent-a review of the technologies[J]. Computersin Industry,2006,57(2):141-152.
[17]李众,高柏宏.打破我国数控机床产业发展桎梏[J].数字工厂/制造,2010(7).
[18]贾亚洲,戴怡,张立斌.研究可信性技术提高数控机床的质量与可信性水平[J].世界制造技术与装备市场,2001(3):11-13.
[19]咸奎桐.可信性管理GB/T19000.4(ISO9000-4)实施指南[M].北京:中国标准出版社,1997.
[20]何国伟.可信性工程[M].北京:中国标准出版社,1998:36-60.
[21]李昌琪,遇立基译, A.C.普罗尼科夫.数控机床的精度与可靠性[M].北京:机械工业出版社,1987.
[22]曾声奎.前苏联可靠性工程的几个特点[J].北京航空航天大学学报,1995,4(21):1-6.
[23]方济道.苏联机床工业进展述评[J].西安工业大学学报,1987,4:92-95.
[24] В．С．Васильев、В．В．Барабанов，Обеспечение надёжностиметаллорежущего оборудования 《Станки и инструмент》,1987(7):10-12.
[25] Д．Н．Колониус，Прогнозирование нагруженности станков《Станкииинструмент》,1991(5):10-12.
[26] В．В．Барабанов、А．Л．Чирков，Исследования надёжности работизированныхтехнологическихкомплексов вначальныйпериодэксплуатации《Станкииинструмент》,1990(1):3-4..
[27] Peter F. McGoldrick and Halil Kulluk，Machine Tool Reliability—A Critical Factor inManufacturing Systems[J]. Reliability Engineering,1986,14:205-221.
[28]中村正弘.トラプル防止する加工监视システムと故障诊断システム[J].机械技术,1995,35(8):37-42.
[29] Marina Karyagina,Walter Wong. Life Cycle Cost Modelling Using Marked PointProcesses[J]. Reliability Engineering and System Safety,1998(59):225-238.
[30] Wang Y,Jia Y,Yu J,Yi S. Field failure database of CNC lathes[J]. I International Journalof Quality&Reliability Management,1999,16(4):330-340.
[31] Wang Y,Jia Y,Yu J,Zheng Y,Yi S. Failure probabilistic model of CNC lathes[J].Reliability Engineering and System Safety,1999,65(3):307-314.
[32] Yiqiang Wanga, Richard C. M. Yamb, Ming J. Zuo, Peter Tseb. A comprehensivereliability allocation method for design of CNC lathes[J]. Reliability Engineering andSystem Safety,2001,72(3):247-252.
[33] Yiqiang Wang, Richard C.M. Yam, Ming J. Zuo.A multi-criterion evaluation approachto selection of the best statistical distribution[J]. Computers&Industrial Engineering,2004,47:165-180.
[34] Yiqiang Wang,Yazhou Jia,Jian Qiu,Guixiang Shen. Load Spectra of CNC MachineTools[J]. Quality and Reliability Engineering Interational,2000,16:229-234.
[35]贾亚洲,马健,贾志新.数空机床现场故障分布模型的统计研究[J].数理统计与管理,1996,15(5):24-28.
[36]申桂香,贾亚洲,马健. CNC机床故障分析及其可靠性[J].中国机械工程,1996(6):67-69.
[37]章国栋,陆廷孝.系统可靠性与维修性的分析与设计[M].北京：北京航空航天大学出版社,1990.
[38]赵跃进,董伟燕.一种综合系统固有可用度的新算法及发现[J].北京理工大学学报,2005,25(5):54-57.
[39]曹晋华,程侃.可靠性数学引论[M].北京:北京科学出版社,1986.
[40]孔德良,王少萍.可修系统的可用度分析方法研究[J].北京航空航天大学学报,2002,28(2):129-132.
[41]史定华.可修系统的运行特征[J].数学进展.1996,1:34-42.
[42]张英锋,蒋涛.用马尔柯夫过程分析可修复系统的稳态可用度[J].徐州师范大学学报,2000,18(2):27-28.
[43] Bris R, Chatelet E, Yalaoui F. New method to minimize the Preventive maintenancecost of series-Parallel systems[J]. Reliability Engineering&System Safety,2003.82(3):247-255.
[44]李剑涛,蒋里强,黄立坡.装备最佳预防性维修间隔时间研究[J].装备指挥技术学院学报,2004,15(3):26-29.
[45] Hassett T F, Dietrieh D L, Szidarovszky F. Time-varying failure rates in theavailability&reliability analysis of repairable systems[J]. IEEE Transaetion OnReliability.1995.44:155-160.
[46] SunH, HanJ J. Instantaneous availability and interval availability for systems withtime-varying failure rate: stai-step approximation[C].2001Paeific Rim InternationalSymposium on Dependable Computing (PRDC2001).2001,371-375.
[47]郑治华.故障影响忽略或滞后的单部件马尔可夫可修系统研究[D].北京:北京理工大学,2006.
[48] Sarkar J, Sarkar S. Availability of a Periodically inspected system supported by a spareunit，under perfect repair or Perfect upgrade[J]. Statisties&Probability Letters,2011,53:207-217.
[49] Cui L R, Xie M. Availability of a Periodieally inspected system with random repair orreplacement times[J]. Journal of Statistical Planning and Inference,2005,131:89-100.
[50] Cassady C R, Lyoob I M, Seheider K, etal. A Generic Model of EquipmentAvailability Under Imperfect Maintenance[J]. IEEE Transactions on reliability,2005,54(4):564-571.
[51]张元林.可修的环形相邻2/n(F)系统[J].系统工程理论与实践,1998(7):50-55.
[52]张元林,林辉能.具有马氏相依和维修有优先权的线性相邻2/n(F)可修系统[J].自动化学报,2000,26(3):317-323.
[53]蔡春娥. N个不同部件并联可修系统可用度的分析[J].湖南轻工业高等专科学校学报,2002,14(1):13-15.
[54]史定华.两部件串联系统的可靠性分析[J].福建师范大学学报,1982,2:13-22.
[55]史定华.两部件串联可修模型分析[J].自动化学报,1985,1:71-79.
[56]谢文祥.冗余可修系统可用度的随机Petri网建模与分析[J].系统工程学报,1998,13(4):93-97.
[57]谢文祥.基于随机Petri网可修控制系统的可用度计算[J].西安交通大学学报,1997,31(11):83-87.
[58]黄龙呈,洪延姬,金星等.可修复部件可用度计算的积分方程方法[J].机电产品开发与创新,2007,20(5):16-25.
[59]金星,洪延姬,文明等.服从任意分布的部件可用度快速计算方法[J].中国空间科学技术,2002(5):67-70.
[60]金星,洪延姬.系统可靠性与可用性分析方法[M].北京:国防工业出版社,2007.
[61]肖刚.评估复杂可维修系统可靠度与瞬态可用度的蒙特卡洛方法[J].兵工学报,2002,23(2):46-50.
[62] Mostafa Bacha, Gilles Celeux. Bayesian Estimation of a Weibull distribution in ahighly censored and small sample setting[R]. Institut National de RechercheInformatique et Automatique,1996.
[63]金光,冯静.长寿命卫星活动部件Bayes-Weibull可靠性评估方法[J].系统工程与电子技术,2009,31(8):2020-2025.
[64]金少华.电工产品可靠性评估方法与贝叶斯理论的应用[D].天津:河北工业大学,2002.
[65]刘海涛,张志华.威布尔分布无失效数据的Bayes可靠性分析[J].系统工程理论与实践,2008(11):103-106.
[66]张恒,张志华.基于极小样本的Weibull分布参数估计方法[J].船海工程,2010,39(5):177-179.
[67] Z.W.Birnbaum. On the importance of different component in a multi-componentsystem[J]. in Krisnaiah(Ed), Multi-variate Analysis II, Academic Press,1969:581-592.
[68] H.E.Lambert. Fault trees for decision making in systems analysis[J]. PhDthesis,UCRL-51829. University of California Liver more.1975.
[69] J.B. Fussell. How to hand-calculate systemre-liability and safety characteristics[J].IEEE Trans. Reliability,1975,24:101-106.
[70] Zhi-JiePan, Ya-ChuanTai. Variance importance of system components by MonteCarlo[J]. IEEE Trans.Reliability,1988,37(4).
[71] Patrik Hilber. Component Reliability Importance Indices for Electrical Networks[C].The Proceeding of8th International Power Engineering conference, July2007:258-262.
[72] J.S.Hong, C.H.Lie. Joint reliability importance of two edges in an undirectednetwork[J]. IEEE Truns. Reliability,1993,42:17-33.
[73] Xueli Gao, Lirong Cui. Analysis for joint importance of components in a coherentsystem[J]. European Journal of Operational Research,2007,182:282–299.
[74] Jose F. Espiritu, David W. Coit, Upyukt Prakash. Component criticality importancemeasures for the power industry[J]. Electric Power Systems Research,2007，77:407-420.
[75] Yong Ou. Joanne Bechta Dugan. Sensitiviyt Analysis of Modular Dynamic FaultTree[J]. IEEE,2000:35-43.
[76] Yong Ou. APProximat esensitivity analysis of acyclic Markov reliability models[J].IEEE TRANSACTIONS ON RELIABILITY,2003,52(2).
[77]李青,陆廷金.模糊重要度分析方法的研究[J].模糊系统与数学,2003,14(1):89-93.
[78]王永传,郁文贤,庄钊文.一种故障树模糊重要度分析的新方法[J].国防科技大学学报,1999,21(3):63-65.
[79]李常有,徐敏强.基于改进的topsis的设备重要度分析[J].震动与冲击,2009,28(6).
[80] Wendai Wang, James Loman,Pantelis Vassiliou. Reliability Importance of Componentsin a Complex System the Institute of Electrical and Electronic Engineers[J].PROCEEDINGS Annua RELIABILITY and MAINTAINABILITY Symposium,2004,1:26-29.
[81] Wendai Wang, James M. Loman. Reliability Block Diagram Simulation TechniquesApplied to the IEEE Std.493Standard Network[J]. IEEE TRANSACTIONS ONINDUSTRY APPLICATIONS,2004,40(3).
[82] Natvig. B, J. Gasemyr. New results on the Barlow-Proschan and Natvig measures ofcomponent importance in nonrepairable and repairable systems[J]. Methodol. Comput.Appl. Prob,2009,11.
[83] B. Natvig. The Natvig measures of component importance in repairable systemsapplied to an offshore oil and gas production system[J]. Safety, Reliability and RiskAnalysis: Theory, Methods and Applications,2008,3:2029-2035.
[84]康锐,石荣德. FMECA技术及其应用[M].北京:国防工业出版社,2006.
[85]戴云徽,韩之俊,朱海荣.故障模式及影响分析-FMEA-研究进展[J].中国质量,2007,(10):23-26.
[86] US MIL-STD-1629/notice2. Procedures for performing a failure mode effects andcriticality analysis. US Department of Defence, Washington DC, USA,1984.
[87] Ford Motor Company. Potential Failure Mode and Effects Analvsis In Design (DesignFMEA) and For Manufacturing and Assembly Processes (Process FMEA)Instruction Manual. Dearborn, Michigan,1988.
[88] ECSS-Q-30-02A. Failure modes, Effects and Criticality analysis,.EuropeanCooperation for Space Standardization,2001.
[89] Wang J, Ruxton T, Labrie CR. Design for safety of engineering systems with multiplefailure state variables[J]. Reliab Eng Syst Saf1995;50:271-84.
[90] Sankar NR, Prabhu B S. Modified approach for prioritization of failures in a systemfailure mode and effects analysis[J]. Int J Qual Reliab Manage2001;18(3):324-35.
[91] Bowles JB. Fuzzy logic prioritization of failures in a system failure mode, effects andcriticality analysis[J]. Reliability Engineering&System Safety1995;50:203-13.
[92] Anand Pillay, Jin Wang. Modified failure mode and effects analysis using approximatereasoning[J]. Reliability Engineering&System Safety2003,79:69-85.
[93] Seyed-Hosseini SM, Safaei N. Reprioritization of failures in a system failure mode andeffects analysis by decision making trial and evaluation laboratory technique[J]. ReliabEng Syst Safety2006,91:872-81.
[94]蒋仁言,左明建.可靠性模型与应用[M].北京:机械工业出版社,1999:14-54.
[95]潘吉安.可靠性维修性可用性评估手册[M].北京:国防工业出版社,1995:253.
[96]秦英孝,张耀文,江劲勇等.可靠性维修性保障性管理[M].北京:国防工业出版社,2003:63-99.
[97]贺国芳.可靠性数据的收集与分析[M].北京:国防工业出版社,1995.
[98] Abbasi, Babak, Rabelo, Luis.Hosseinkouchack, Mehdi. Estimating parameters of thethree-parameter Weibull distribution using a neural network[J]. European Journal ofIndustrial Engineering,2008(5):428-445.
[99]张英芝,申桂香.数控车床故障分布规律及可靠性[J].农业机械学报,2006,37(1):156-159.
[100]于捷,申桂香.基于三参数威布尔分布的数控机床的可靠性评价[J].现代制造工程,2007,(5):18-20.
[101] Jia Yazhou, Wang Molin. Probability distribution of machining center failures[J].Reliability Engineering and System Safety,1995,50:121-125.
[102]石瑞平.基于一元回归分析模型的研究[D].石家庄:河北科技大学.2009.
[103] White JS. The Moments of Log-Weibull Order Statistics[J]. Technometrics1969,1(2):65-72.
[104] Mann NR, Schafer RE, Singpurwalla ND. Methods for statistical analysis of reliabilityand life data[M]. New York: Wiley,1974.
[105] Ross R. Comparing Linear Regression and Maximum Likelihood Methods to EstimateWeibull Distributions on Limited Data Sets:Systematic and Random Errors[J].Conference on electrical insulation and dielectric phenomena,1999(1):170-173.
[106] Thoman DR, Bain LJ, Antle E. Inferences on the Parameters of the WeibullDistribution. Technometrics1969,11(3).
[107]张英芝,申桂香.随机截尾数控机床三参数威布尔分布模型[J].吉林大学学报,2009,39(2):378-381.
[108]赵德让,刘瑞元.三参数威布尔分布参数估计的合理性[J].青海师范大学学报(自然科学版),2002(3).
[109] STORN R, PRICE K.Differential Evolution-a Simple and Efficient Adaptive Schemefor Global Optimization over Continuous Spaces[J]. Technical Report, InternationalComputer Science Institute,1995(8):22-25.
[110] Price K, Storn R, Lampinen J. Differential Evolution-A Practical Approach to GlobalOptimization[M]. New York: Springer,2005.
[111]刘波,王凌.差分进化算法研究进展[J].控制与决策,2007(7):721-729.
[112]周艳平.差分进化算法研究进展[J].化工自动化及仪表,2007,34(3):1-5.
[113]吴亮红.差分进化算法及应用研究[D].长沙:湖南大学,2007.
[114]陈家鼎.序贯分析[M].北京:北京大学出版社,1995.
[115]朱一凡,赵新,李群.基于参数稳态判定的两阶段序贯仿真分析策略[J].国防科技大学学报,2011,33(1):120-124.
[116]姚李孝,崔杜武,伍利.基于时序模拟法和Petri网的电气主接线可靠性评估[J].西安理工大学学报,2005,21(1):42-46.
[117]宋晓通.基于蒙特卡罗方法的电力系统可靠性评估[D].济南:山东大学,2008.
[118]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985.
[119]段利民,曾凯尹. MATLAB在模拟法中的应用[J].技术与创新管理,2007,28(03):115-117.
[120]张仙风,吕志鹏.基于MATLAB的蒙特卡罗方法在可靠性设计中的应用[J].装备制造技术,2006,(4):76-78.
[121]欧宜贵,李志林,洪世煌.计算机模拟在数学建模中的应用[J].海南大学学报自然科学版,2004,22(1):89-95.
[122]魏领会.数控机床可用性耦合建模及影响度分析[D].长春:吉林大学,2011.
[123]茆诗松.贝叶斯统计[M].北京:中国统计出版社,1999,10.
[124]朱炜.小样本下正态分布可靠度评估方法[J].质量与可靠性,2006,(6):17-19.
[125]游学民.正态分布的小样本检验及可靠度评估[J].科技信息,2007,(20).
[126]赵选民.数理统计[M].北京:科学出版社,2002.
[127] L.A. Zadeh. The concept of a linguistic variable and its application to approximatereasoning[J]. Information Sciences,1975,8:199-249.
[128] Karnik, Mendel. Introduction to Type-2Fuzzy Logic Systems[C]. In:Proceedings ofIEEE FUZZ Conference, Anchorage AK,1998:915-920.
[129] Jerry M. Mendel. Interval Type-2Fuzzy Logic Systems Made Simple[C]. IEEE Trans.Fuzzy Syst,2006,14(12):808–810.
[130]李海. Type-2模糊系统的比较研究与仿真应用[D].镇江:江苏大学,2006.
[131]李霞.基于新距离测度的区间数排序[J].西华大学学报,2008,27(1):87-89.
[132] Ting-Yu Chen. The interval-valued fuzzy TOPSIS methodand experimental analysis[J].Fuzzy Sets and Systems,2008,159(11):1410-1428.
[133]陈殿生.数控机床可信性应用研究[D].长春:吉林大学,2002.
[134]魏领会.面向生命周期的加工中心可用性保证技术[D].长春:吉林大学,2007.
[135]薛玉霞.可用性关键技术研究[D].长春:吉林大学,2009.
[136]裴咏红.数控机床可靠性增长实用技术[D].长春:吉林大学,2004.
[137]卜波.数控车床可靠性增长技术的应用研究[D].长春:吉林大学,2008.
[138]楚文龙,李英,杰杨冬.基于卧式加工中心主轴箱的可靠性增长技术研究[J].现代制造技术与装备,2011,(2):17-18.
[139]王国强,王玉金.高速数控机床主轴用陶瓷轴承的研究[J].陶瓷,2004,(2):43-45.