协同环境下加工中心配套件可靠性分析
|
摘要
现代数控机床的研制是一个庞大的系统工程,必须走多企业联合研制的道路,以充分发挥各设计单位和制造单位的优势,共享产品开发的资源和经验。异地设计、制造、管理与协同工作是现在乃至未来数控机床工业发展的必然趋势。
配套件作为数控机床的重要组成之一,其可靠性水平的好坏直接影响到整机的质量和产品的声誉。因此,提高配套件的可靠性是关系到主机行业和配套件行业生存与发展的大问题,积极开展协同环境下的数控机床配套件可靠性分析技术的研究,对整机生产企业合理选择配套件,提高整机的可靠性水平具有十分重大的现实意义。
可靠性分析是可靠性设计、可靠性预计、故障诊断等可靠性研究的重要基础。本文采用随机过程、概率论与数理统计、可靠性工程等多学科相结合的方法,以某系列加工中心故障数据为例,通过统计分析,从整机上掌握因配套件原因引起的此系列加工中心的故障发生情况,并对故障频繁的部件或子系统进行深入分析,探寻可靠性改进设计的方向。同时,从“可修复”和“不可修复”的角度出发,以计算机为工具,进行加工中心配套件可靠性分析。
The reliability of CNC machine tools relate to many different departments of host-plant production, the supplier and cooperation manufacturers of corollary parts and purchased parts, operators, maintenance personnel and equipment management department. It is system engineering in the collaborative environment which needs the cooperation of various departments and enterprises. Corollary parts, as an important component of CNC machine tools, its reliability has a direct impact on the quality of whole machine as well as the reputation of its products. Therefore, enhancing corollary parts’reliability is an important issue related to the survival and development of host and corollary parts industry. Meanwhile, research on reliability analysis technique of corollary parts of CNC machine tools based on collaborative environment has a great practical significance on manufacturers in choosing reasonable corollary parts and to improve reliability of CNC machine tools. The reliability analysis technique of corollary parts has laid the foundation for achieving great breakthrough of national project“863”“Dependability modeling and analysis techniques of heavy CNC equipment facing life cycle”.
1. FMECA on corollary parts of machining center in collaborative environment
The paper had researched on a series of machining center; used time truncated testing by conducting on field test to 20 machining centers of this series in order to collect information of reliability test. Then I had elected 48 machining center failure data caused by corollary parts from 106 collected and accumulated machining center failure data. Afterwards, the paper conducted on failure mode and effect and criticality analysis to the 48 failure data. And researched on fault position, fault mode and ratio of fault causes due to corollary parts, then mastered fault occurrence condition of this series machining center on account of corollary parts from the whole machine. Furthermore, this paper made fault mode an effect analysis to the components or subsystems which had caused frequent failures to the whole machine, then explored the direction of reliability improvement design. After analysis, I got the conclusion that tool magazine was the main position to machining center failure and the main failure mode was tool imbalance, the main effect was parts damaged. Besides, the criticality of tool magazine was the highest. Therefore, tool magazine was the key corollary part affecting the reliability of machining center.
2. Reliability analysis on unrepairable corollary parts of machining center
Analysis of reliability data of unrepairable corollary parts of machining center, establish reliability data model with non-replacement random censoring according to the characteristic of unrepairable corollary parts’failure data. Based on the FMECA mentioned before, the majority failure of unrepairable corollary parts of machining center were abandoned after damaged and the parts being replaced. We assumed that the statistical data obey Weibull distribution as most of the mechanical products were subject to Weibull distribution. Then carrying out least squares parameter estimation, correlation coefficient test and d test. The results showed that the data were subject to two-parameter Weibull distribution. As scatter diagram of empirical distribution function was a convex function, besides, the shape parameter of Weibull close to 1, the failure data may also be subject to exponential distribution, then carrying out hypothesis testing to exponential distribution using the same method. Then plot the scatter diagram of empirical distribution function both of exponential distribution and Weibull distribution on the same figure. It was found that all these empirical distribution function’points were subject to Weibull model when the time was relatively small. On the country when the time of failure-free operation was large, the empirical distribution function closed to exponential distribution. So it was difficult to determine which distribution model was better only from these curves. Therefore, the paper used compared the distribution function curve method“correlation index”to optimize distribution types. Finally, is was proved that the lifetime data of unrepairable corollary parts of machining center obey two-parameter Weibull distribution through goodness-of-fit test. Moreover, I had figured out probability density function f (t ), distribution function , failure rate function F (t )λ( t) as well as reliability function R (t ).Then calculated the observed value and point estimate value of mean time to failure (MTTF).
3. Reliability analysis on repairable corollary parts of machining center
This paper carried out stochastic process theory to analysis failure process of repairable corollary parts of machining center. Then adopt the total time method for data pre-processing according to the characteristics of random censoring in reliability test. It was concluded that the failure process was in line with renewal process by trend test and correlation test. However, time between failures may be subject to exponential distribution, Weibull distribution, normal or lognormal distribution and so on. Therefore, the paper assumed the failure data obey the four distribution mentioned above which were commonly in engineering. And all these four distribution were allowed from correlation test and d test. So this paper adopt correlation index for goodness-of-fit test, then it was concluded that fault data obey two-parameter Weibull distribution. Finally, I had figured out observed value of mean time between failure (MTBF), mean time to repair (MTTR) and inherent availability Ai.
配套件作为数控机床的重要组成之一,其可靠性水平的好坏直接影响到整机的质量和产品的声誉。因此,提高配套件的可靠性是关系到主机行业和配套件行业生存与发展的大问题,积极开展协同环境下的数控机床配套件可靠性分析技术的研究,对整机生产企业合理选择配套件,提高整机的可靠性水平具有十分重大的现实意义。
可靠性分析是可靠性设计、可靠性预计、故障诊断等可靠性研究的重要基础。本文采用随机过程、概率论与数理统计、可靠性工程等多学科相结合的方法,以某系列加工中心故障数据为例,通过统计分析,从整机上掌握因配套件原因引起的此系列加工中心的故障发生情况,并对故障频繁的部件或子系统进行深入分析,探寻可靠性改进设计的方向。同时,从“可修复”和“不可修复”的角度出发,以计算机为工具,进行加工中心配套件可靠性分析。
The reliability of CNC machine tools relate to many different departments of host-plant production, the supplier and cooperation manufacturers of corollary parts and purchased parts, operators, maintenance personnel and equipment management department. It is system engineering in the collaborative environment which needs the cooperation of various departments and enterprises. Corollary parts, as an important component of CNC machine tools, its reliability has a direct impact on the quality of whole machine as well as the reputation of its products. Therefore, enhancing corollary parts’reliability is an important issue related to the survival and development of host and corollary parts industry. Meanwhile, research on reliability analysis technique of corollary parts of CNC machine tools based on collaborative environment has a great practical significance on manufacturers in choosing reasonable corollary parts and to improve reliability of CNC machine tools. The reliability analysis technique of corollary parts has laid the foundation for achieving great breakthrough of national project“863”“Dependability modeling and analysis techniques of heavy CNC equipment facing life cycle”.
1. FMECA on corollary parts of machining center in collaborative environment
The paper had researched on a series of machining center; used time truncated testing by conducting on field test to 20 machining centers of this series in order to collect information of reliability test. Then I had elected 48 machining center failure data caused by corollary parts from 106 collected and accumulated machining center failure data. Afterwards, the paper conducted on failure mode and effect and criticality analysis to the 48 failure data. And researched on fault position, fault mode and ratio of fault causes due to corollary parts, then mastered fault occurrence condition of this series machining center on account of corollary parts from the whole machine. Furthermore, this paper made fault mode an effect analysis to the components or subsystems which had caused frequent failures to the whole machine, then explored the direction of reliability improvement design. After analysis, I got the conclusion that tool magazine was the main position to machining center failure and the main failure mode was tool imbalance, the main effect was parts damaged. Besides, the criticality of tool magazine was the highest. Therefore, tool magazine was the key corollary part affecting the reliability of machining center.
2. Reliability analysis on unrepairable corollary parts of machining center
Analysis of reliability data of unrepairable corollary parts of machining center, establish reliability data model with non-replacement random censoring according to the characteristic of unrepairable corollary parts’failure data. Based on the FMECA mentioned before, the majority failure of unrepairable corollary parts of machining center were abandoned after damaged and the parts being replaced. We assumed that the statistical data obey Weibull distribution as most of the mechanical products were subject to Weibull distribution. Then carrying out least squares parameter estimation, correlation coefficient test and d test. The results showed that the data were subject to two-parameter Weibull distribution. As scatter diagram of empirical distribution function was a convex function, besides, the shape parameter of Weibull close to 1, the failure data may also be subject to exponential distribution, then carrying out hypothesis testing to exponential distribution using the same method. Then plot the scatter diagram of empirical distribution function both of exponential distribution and Weibull distribution on the same figure. It was found that all these empirical distribution function’points were subject to Weibull model when the time was relatively small. On the country when the time of failure-free operation was large, the empirical distribution function closed to exponential distribution. So it was difficult to determine which distribution model was better only from these curves. Therefore, the paper used compared the distribution function curve method“correlation index”to optimize distribution types. Finally, is was proved that the lifetime data of unrepairable corollary parts of machining center obey two-parameter Weibull distribution through goodness-of-fit test. Moreover, I had figured out probability density function f (t ), distribution function , failure rate function F (t )λ( t) as well as reliability function R (t ).Then calculated the observed value and point estimate value of mean time to failure (MTTF).
3. Reliability analysis on repairable corollary parts of machining center
This paper carried out stochastic process theory to analysis failure process of repairable corollary parts of machining center. Then adopt the total time method for data pre-processing according to the characteristics of random censoring in reliability test. It was concluded that the failure process was in line with renewal process by trend test and correlation test. However, time between failures may be subject to exponential distribution, Weibull distribution, normal or lognormal distribution and so on. Therefore, the paper assumed the failure data obey the four distribution mentioned above which were commonly in engineering. And all these four distribution were allowed from correlation test and d test. So this paper adopt correlation index for goodness-of-fit test, then it was concluded that fault data obey two-parameter Weibull distribution. Finally, I had figured out observed value of mean time between failure (MTBF), mean time to repair (MTTR) and inherent availability Ai.
引文
[1]汪林海,孟庆国.当代世界机床工业的五个特点和发展我国机床工业的对策[J].管理现代化,2006(2):45-47.
[2]许郁生.中国机床行业的发展和市场需求[J].世界制造技术与装备市场,2007(2):50-58.
[3]贾亚洲.提高数控机床可靠性加快振兴装备制造业的关键[J].中国制造业信息化,2006,6:42-43.
[4]温汝贤.我国数控机床的发展现状与展望[J].中国设备工程,1994(11):35-36.
[5]中国机械工业联合会科技工作部.“十五”我国数控技术及装备发展情况[J].中国制造业信息化,2005,3:40-43.
[6]贾亚洲.提高国产数控机床可靠性水平[J].数控机床市场,2006,5:92-99.
[7]刘英,易红,倪中华等.机床产品协同数字化设计与制造关键技术的研究[J].组合机床与自动化加工技术,2005(7):108-111.
[8]于海斌,朱云龙.协同制造——e时代的制造策略与解决方案[M].北京:清华大学出版社,2004.
[9]蒋明炜.协同制造,全民提高机床制造业水平[J].微型机与应用,2008(3):44-47.
[10]胡仲翔,贾志成,赖利国.协同制造系统可靠性信息化及其策略[J].中国制造业信息化,2006,35(17):29-32.
[11]武学军.数控机床网络化协同制造和服务系统[J].航空制造技术,2005(10):46-49.
[12]胡仲翔,贾志成,陶俐言.协同制造系统可靠性分析[J].装甲兵工程学院学报,2007,21(1):84-88.
[13]李昌琪译,AC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.
[14]А.В.Кудинов.Информационно-вероятностнаямодельформированиятребуёмогоиспользованиястанков[J].Станкииинструмент,1994,5:3-7.
[15]А.В.Кудинов.Оценкакоэффициентаисследованияметаллорежущихстанк-овнаосновеобобщеннойциклограммы[J].Станкииинструмент,1994,9:6-9.
[16]Е.А.Чернов.Медотыповышениянадёжностисистемуправленияпоискоминструмента[J].Станкииинструмент,1994,11:15-18.
[17]В.В.Барабанов.Расчётнадёжностипроектируемогостанка[J].Станкииинструмент,1995,3:3-5.
[18]余仲裕.数控机床维修[M].北京:机械工业出版社,2001. 51
[19] Yash P. Gupta & Toni M. Somers. Availability of CNC Machines: Multiple-Input Transfer-Function Modeling[C].IEEE TRANSACTION ON RELIABILITY,1989,38(3):285-295.
[20] Rudi H.P.M.Arts, Anuj Saxena, Gerald M.Knapp. Estimation of distribution parameters of mixed failure mode data[J].Journal of quality in maintenance engineering,1997,3(2):79-83.
[21] J. I. Ansell & M. J. Phillips. Practical Reliability Data Analysis[J]. Reliability Engineering and System Safety,1990(28):337-356.
[22] J.A.jones &J.A.Hayes.Use of a field failure database for improvement of product reliability[J]. Reliability Engineering and System Safety,1997(55):131-134.
[23]郑玉华,王义强.CNC车床维修性及其特征量的统计与分析[J].系统工程理论与实践.1999(8):133-139.
[24]王义强,贾亚洲,于骏一等.数控车床的故障和维修性分布模型[J].数理统计与管理,1999,18(2):9-12.
[25]舒赜,贾亚洲,钟秉林.加工中心的维修度与有效度数学模型及分析[J].中国机械工程,1999,10(4):418-420.
[26] Yiqiang Wang,Guixiang Shen,Yazhou Jia.Multidimensional force spectra of CNC machine tools and their applications, part two: Reliability design of elements[J]. International Journal of Fatigue,2003,25:447-452.
[27] Chen Diansheng,Yazhou Jia,Guixiang Shen.Probability distribution of the early failures of machining centers.6th International Conference Proceedingss of Machining Technology,2002[C],Xi'an,China:445-449
[28] Yiqiang Wang,Yazhou Jia,Guixiang Shen.Multidimensional force spectra of CNC machine tools and their applications, part one: force spectra[J]. International Journal of Fatigue,2002,24:1037-1046.
[29]戴怡,周云飞,贾亚洲,申桂香.主轴交流伺服系统稳定性控制策略研究[J].农业机械学报,2004(2):110-112.
[30] Yazhou Jia,Lihui Zhu,Guixiang Shen.Fault Mode and Effect Analysis for CNC Machine Centers. Proceedings of International IEEE Conference on the Business of Electronic Product Reliability and Liability,2003[C],Hong Gong:295-300.
[31] Yazhou Jia,Weiwei Jiang, Guixiang Shen.Research and application on the management information system of the NC equipment reliability.IEEE Conference on the Business of Electronic Product Reliability and Liability,2003[C],Hong Gong:313-319.
[32]贾亚洲,姜巍巍,申桂香.面向网络的数控机床可靠性计算机辅助分析技术.中国机械工程分会可靠性分会论文集[C].北京:科学出版社,2002.
[33]张英芝,申桂香,薛玉霞.随机截尾数控车床刀架系统故障过程[J].吉林大学学报(工学版),2008(S2):113-116.
[34]张英芝,申桂香,薛玉霞等.随机截尾数控机床故障过程[J].吉林大学学报(工学版),2007,37(06):1346-1348.
[35]张英芝,申桂香,薛玉霞等.数控车床主轴模糊故障树分析[J].吉林大学学报(工学版),2006,36(S2):65-68.
[36]张英芝,申桂香,贾亚洲等.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.
[37]张英芝,贾亚洲,申桂香等.基于随机截尾的数控机床故障分布模型研究[J].系统工程理论与实践,2005,25(2):134-138.
[38]张英芝,贾亚洲,张学文等.数控冲床的故障概率分布模型[J].吉林大学学报(工学版),2004,34(2):264-267.
[39]申桂香,陈炳锟,张英芝,薛玉霞.基于熵值—模糊综合评判的可靠性模型优选[J].吉林大学学报(工学版),2008(S2):117-121.
[40]薛玉霞,申桂香,张英芝.基于模糊逻辑的数控机床故障分析[J].吉林大学学报(工学版),2008,38(S1):0115-0118.
[41]申桂香,王桂萍,贾亚洲.面向网络的数控装备可靠性分析技术[J].中国机械工程,2005,16(1):33-35.
[42]于捷,申桂香,贾亚洲.数控机床可靠性评价方法的研究[J].机床与液压,2007(11):174-176.
[43]于捷,申桂香.基于推广的L-M法的数控机床的可靠性评价[J].机床与液压,2008(1):171-173.
[44]贾志成,申桂香,胡仲翔等.基于生命周期的数控车床寿命分布模型及控制[J].机床与液压,2008(1):164-167.
[45]王桂萍,贾亚洲,申桂香等.加工中心冷却系统故障模式危害性模糊评价分析[J].农业机械学报,2008(3):171-174.
[46]王桂萍,贾亚洲,申桂香等.基于故障比重比的加工中心整机故障分析及可靠性改进措施[J].吉林大学学报(工学版),2008,38(S1):119-122.
[47]教育部科技发展中心.机床可靠性研究取得重要成果[N].一周科技与经济要闻,2002年3月第4周;《科技时报》,2002-03-20
[48]佟璞玮.“九五”期间我国机械工业科学技术取得重大进展[J].世界制造技术与装备市场,2001,6:5-7.
[49]桑书林.数控机床通用配套产品行业现状与“九五”发展建议[J].机床与液压,1994(6):311-318.
[50]茆诗松,王玲玲.可靠性统计[M].上海:华东师范大学出版社,1984.
[51]马传伟.工程机械配套件行业任重而道远[J].工程机械与维修,2001(S):1.
[52]贡凯军.加入WTO与中国装载机行业的发展[J].工程机械,2004(4):1.
[53]孙进康等.可修复系统故障数据分析模型与方法研究[J].解放军理工大学学报,2000,2:57-61.
[54] Vaurio J K. Identification of process and distribution characteristics by testing monotonic and nonmonotonic trends in failure intensities and hazard rates[J].Reliability Engineering and System Safety,1999,64: 345-357.
[55]曹晋华,程侃著.可靠性数学引论[M].北京:科学出版社,1986.
[56]蒋仁言,左明健.可靠性模型与应用[M].北京:机械工业出版社,1999.
[57]王超,王金.机械可靠性工程[M].北京:冶金工业出版社,1992.
[2]许郁生.中国机床行业的发展和市场需求[J].世界制造技术与装备市场,2007(2):50-58.
[3]贾亚洲.提高数控机床可靠性加快振兴装备制造业的关键[J].中国制造业信息化,2006,6:42-43.
[4]温汝贤.我国数控机床的发展现状与展望[J].中国设备工程,1994(11):35-36.
[5]中国机械工业联合会科技工作部.“十五”我国数控技术及装备发展情况[J].中国制造业信息化,2005,3:40-43.
[6]贾亚洲.提高国产数控机床可靠性水平[J].数控机床市场,2006,5:92-99.
[7]刘英,易红,倪中华等.机床产品协同数字化设计与制造关键技术的研究[J].组合机床与自动化加工技术,2005(7):108-111.
[8]于海斌,朱云龙.协同制造——e时代的制造策略与解决方案[M].北京:清华大学出版社,2004.
[9]蒋明炜.协同制造,全民提高机床制造业水平[J].微型机与应用,2008(3):44-47.
[10]胡仲翔,贾志成,赖利国.协同制造系统可靠性信息化及其策略[J].中国制造业信息化,2006,35(17):29-32.
[11]武学军.数控机床网络化协同制造和服务系统[J].航空制造技术,2005(10):46-49.
[12]胡仲翔,贾志成,陶俐言.协同制造系统可靠性分析[J].装甲兵工程学院学报,2007,21(1):84-88.
[13]李昌琪译,AC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.
[14]А.В.Кудинов.Информационно-вероятностнаямодельформированиятребуёмогоиспользованиястанков[J].Станкииинструмент,1994,5:3-7.
[15]А.В.Кудинов.Оценкакоэффициентаисследованияметаллорежущихстанк-овнаосновеобобщеннойциклограммы[J].Станкииинструмент,1994,9:6-9.
[16]Е.А.Чернов.Медотыповышениянадёжностисистемуправленияпоискоминструмента[J].Станкииинструмент,1994,11:15-18.
[17]В.В.Барабанов.Расчётнадёжностипроектируемогостанка[J].Станкииинструмент,1995,3:3-5.
[18]余仲裕.数控机床维修[M].北京:机械工业出版社,2001. 51
[19] Yash P. Gupta & Toni M. Somers. Availability of CNC Machines: Multiple-Input Transfer-Function Modeling[C].IEEE TRANSACTION ON RELIABILITY,1989,38(3):285-295.
[20] Rudi H.P.M.Arts, Anuj Saxena, Gerald M.Knapp. Estimation of distribution parameters of mixed failure mode data[J].Journal of quality in maintenance engineering,1997,3(2):79-83.
[21] J. I. Ansell & M. J. Phillips. Practical Reliability Data Analysis[J]. Reliability Engineering and System Safety,1990(28):337-356.
[22] J.A.jones &J.A.Hayes.Use of a field failure database for improvement of product reliability[J]. Reliability Engineering and System Safety,1997(55):131-134.
[23]郑玉华,王义强.CNC车床维修性及其特征量的统计与分析[J].系统工程理论与实践.1999(8):133-139.
[24]王义强,贾亚洲,于骏一等.数控车床的故障和维修性分布模型[J].数理统计与管理,1999,18(2):9-12.
[25]舒赜,贾亚洲,钟秉林.加工中心的维修度与有效度数学模型及分析[J].中国机械工程,1999,10(4):418-420.
[26] Yiqiang Wang,Guixiang Shen,Yazhou Jia.Multidimensional force spectra of CNC machine tools and their applications, part two: Reliability design of elements[J]. International Journal of Fatigue,2003,25:447-452.
[27] Chen Diansheng,Yazhou Jia,Guixiang Shen.Probability distribution of the early failures of machining centers.6th International Conference Proceedingss of Machining Technology,2002[C],Xi'an,China:445-449
[28] Yiqiang Wang,Yazhou Jia,Guixiang Shen.Multidimensional force spectra of CNC machine tools and their applications, part one: force spectra[J]. International Journal of Fatigue,2002,24:1037-1046.
[29]戴怡,周云飞,贾亚洲,申桂香.主轴交流伺服系统稳定性控制策略研究[J].农业机械学报,2004(2):110-112.
[30] Yazhou Jia,Lihui Zhu,Guixiang Shen.Fault Mode and Effect Analysis for CNC Machine Centers. Proceedings of International IEEE Conference on the Business of Electronic Product Reliability and Liability,2003[C],Hong Gong:295-300.
[31] Yazhou Jia,Weiwei Jiang, Guixiang Shen.Research and application on the management information system of the NC equipment reliability.IEEE Conference on the Business of Electronic Product Reliability and Liability,2003[C],Hong Gong:313-319.
[32]贾亚洲,姜巍巍,申桂香.面向网络的数控机床可靠性计算机辅助分析技术.中国机械工程分会可靠性分会论文集[C].北京:科学出版社,2002.
[33]张英芝,申桂香,薛玉霞.随机截尾数控车床刀架系统故障过程[J].吉林大学学报(工学版),2008(S2):113-116.
[34]张英芝,申桂香,薛玉霞等.随机截尾数控机床故障过程[J].吉林大学学报(工学版),2007,37(06):1346-1348.
[35]张英芝,申桂香,薛玉霞等.数控车床主轴模糊故障树分析[J].吉林大学学报(工学版),2006,36(S2):65-68.
[36]张英芝,申桂香,贾亚洲等.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.
[37]张英芝,贾亚洲,申桂香等.基于随机截尾的数控机床故障分布模型研究[J].系统工程理论与实践,2005,25(2):134-138.
[38]张英芝,贾亚洲,张学文等.数控冲床的故障概率分布模型[J].吉林大学学报(工学版),2004,34(2):264-267.
[39]申桂香,陈炳锟,张英芝,薛玉霞.基于熵值—模糊综合评判的可靠性模型优选[J].吉林大学学报(工学版),2008(S2):117-121.
[40]薛玉霞,申桂香,张英芝.基于模糊逻辑的数控机床故障分析[J].吉林大学学报(工学版),2008,38(S1):0115-0118.
[41]申桂香,王桂萍,贾亚洲.面向网络的数控装备可靠性分析技术[J].中国机械工程,2005,16(1):33-35.
[42]于捷,申桂香,贾亚洲.数控机床可靠性评价方法的研究[J].机床与液压,2007(11):174-176.
[43]于捷,申桂香.基于推广的L-M法的数控机床的可靠性评价[J].机床与液压,2008(1):171-173.
[44]贾志成,申桂香,胡仲翔等.基于生命周期的数控车床寿命分布模型及控制[J].机床与液压,2008(1):164-167.
[45]王桂萍,贾亚洲,申桂香等.加工中心冷却系统故障模式危害性模糊评价分析[J].农业机械学报,2008(3):171-174.
[46]王桂萍,贾亚洲,申桂香等.基于故障比重比的加工中心整机故障分析及可靠性改进措施[J].吉林大学学报(工学版),2008,38(S1):119-122.
[47]教育部科技发展中心.机床可靠性研究取得重要成果[N].一周科技与经济要闻,2002年3月第4周;《科技时报》,2002-03-20
[48]佟璞玮.“九五”期间我国机械工业科学技术取得重大进展[J].世界制造技术与装备市场,2001,6:5-7.
[49]桑书林.数控机床通用配套产品行业现状与“九五”发展建议[J].机床与液压,1994(6):311-318.
[50]茆诗松,王玲玲.可靠性统计[M].上海:华东师范大学出版社,1984.
[51]马传伟.工程机械配套件行业任重而道远[J].工程机械与维修,2001(S):1.
[52]贡凯军.加入WTO与中国装载机行业的发展[J].工程机械,2004(4):1.
[53]孙进康等.可修复系统故障数据分析模型与方法研究[J].解放军理工大学学报,2000,2:57-61.
[54] Vaurio J K. Identification of process and distribution characteristics by testing monotonic and nonmonotonic trends in failure intensities and hazard rates[J].Reliability Engineering and System Safety,1999,64: 345-357.
[55]曹晋华,程侃著.可靠性数学引论[M].北京:科学出版社,1986.
[56]蒋仁言,左明健.可靠性模型与应用[M].北京:机械工业出版社,1999.
[57]王超,王金.机械可靠性工程[M].北京:冶金工业出版社,1992.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |