基于维修程度的数控机床可靠性建模与分析
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
数控机床是技术密集的现代制造装备,是组成制造系统的基本单元。近年来,随着数控机床、特别是中高档数控机床功能的不断增强,先进功能的维持能力——可靠性问题越加重要。目前,国产数控机床的功能与国际先进水平的差距不断减小,但可靠性技术落后,差距明显,已经成为行业发展的瓶颈,同时也引起了行业和学术界的高度关注。可靠性建模是数控机床可靠性研究中的一项重要内容,正确建模是实现可靠性设计与可靠性增长的前提和保证。迄今为止,限于数据的积累和建模的方便,该领域多采用指数分布和威布尔分布对故障数据进行建模,是在“修复如新”的完全维修假设下进行研究的。事实上,对于数控机床这种复杂可修系统而言,在对故障进行维修时,大多为“修复如旧”的最小维修或不完全维修。因此,需要对数控机床的建模方法进行改进,考虑维修程度对它的不同影响,并进行相关研究。
     本文主要以整机和子系统不同的维修程度为前提基础,分别研究了基于非齐次泊松过程的整机可靠性建模和基于不完全维修的子系统的可靠性建模方法,并对不同维修程度下的故障模式和可用度进行了深入的研究,论文的工作主要涉及以下几个方面:
     (1)论文首先分析了数控机床可靠性方法的国内外研究现状,结合数控机床和关键功能部件可靠性设计课题的实际需要,提出了基于不同维修程度的数控机床可靠性建模新方法。
     (2)根据维修程度最小,即“最小维修”的假设前提,建立了数控机床整机的数学模型并对该模型进行了数学求解。在对可修系统进行了理论分析的基础上,提出了数控机床非齐次泊松过程的数学模型。同时,针对现场试验故障数据的多样本、随机截尾的特点,使用故障总时间法进行预处理。在此基础上,依据不同的故障趋势,提出基于威布尔过程和两重威布尔过程的可靠性模型,通过最大似然估计的求解算法,确定模型参数。文中对某机床厂的数控车床和加工中心的故障数据进行了可靠性建模,通过相关的检验,结果具有较好的拟合优度,更符合数控机床作为可修系统的工程实际情况。
     (3)数控机床子系统各子系统的可靠性是评价数控机床可靠性的关键环节之一。考虑到维修程度对各子系统的影响是介于“修复如新”和“最小修复”之间的情况,提出了基于不完全维修的子系统可靠性建模方法。该模型是建立在Kijima I不完全维修模型基础上的,所建立模型的故障率符合浴盆曲线形式。同时,综合使用最大似然法和遗传算法求出模型的参数估计值。结果表明,该模型不仅能够得出早期故障期和偶然期的临界值,还能评价出在偶然期和损耗期内的维修程度,可以真实的反应各子系统可靠性变化的真实情况。
     (4)在建立了数控机床整机和各子系统的可靠性模型的基础上,对数控机床的故障模式进行了研究,分析了FMECA方法未考虑维修程度的缺陷,提出了改进的FMECA方法。该方法综合了故障模式的定性分析和定量分析结果,建立了基于加权欧氏距离和影响因素空间图的求解算法,避免了传统方法中风险优先数的各组成因素相乘所导致的放大效应。同时,维修程度是影响故障模式的重要因素,因此,将其引入危害度定量分析中,有助于更加全面充分的研究故障模式。通过对数控车床主要故障模式的分析,可以找出数控车床的薄弱环节,为可靠性设计和可靠性增长提供关键的参考依据。
     (5)研究了计及维修程度的数控机床可用性数学模型,针对修复性维修的数据信息,提出了基于蒙特卡罗的可用度分析方法,对最小维修和不完全维修假设下的整机和子系统进行了研究。结果表明,利用本文所建立的可用度模型可以描绘出瞬时可用度曲线,并能得到数控机床的可用度规律,进一步完善了数控机床可用度分析方法。通过本文的研究,使数控机床整机和子系统的可靠性模型更加合理,丰富了数控机床可用度分析方法,提高了可靠性评价的准确性,为数控机床可靠性建模方法开辟了一种新的解决思路。纵观全文,本论文的创新性工作主要有以下几方面:
     (1)将最小维修的建模思想应用于数控机床整机的可靠性建模中,提出了基于非齐次泊松过程的建模方法,建立了“修复如旧”假设下的整机数学模型,该方法具有良好的拟合优度,为数控机床整机建模提供了一种新的解决思路。
     (2)建立了数控机床各子系统的不完全维修模型。该方法能够得到各子系统的早期故障期和偶然故障期的临界值和维修程度,计算结果能够反应出各子系统所处的寿命阶段特征。
     (3)将维修程度作为FMECA故障分析方法的影响因素之一,并将FMEA的定性分析指标RPN与定量分析的危害度有机结合,提出综合风险评估的数学方法,使数控机床的故障分析结果更符合实际情况。
     (4)针对数控机床可用度问题,研究了可用度函数,建立了不同维修程度下的基于蒙特卡罗的可用度数学模型。通过该方法能够得到不同维修程度下的可用度曲线,丰富了数控机床可用度分析方法。
As the basic unit of manufacturing system, CNC machine tools is a technology- integrated manufacturing equipment. With the function diversifies, especially for the high-speed &high-precision, the reliability problem which describes the sustainability of the function arised. Nowadays, the function gap of the domestic and the overseas CNC machine tools is decreased gradually. However, the reliability gap is obvious. The reliability problem has become the bottle-neck for the domestic CNC machine tools industry and attracted the attentions of the researchers and engineers. Reliability modeling is a significant work for the CNC machine tools reliability research, whether the model is accurate or not is the assurance and premise for the reliability design and growth. So far, limited to the convenience of the data accumulation and reliability modeling, the Weibull and Exponential distribution are common used statistical models and the as-good-as-new is the general assumption. However, the repair effect is between as-good-as-new and as-bad-as-old to the complex system such as CNC machine tools, So, the repair effect must be considered for the reliability modeling .
     The paper focuses on the repair degree to the CNC machine tools and its’features, and the reliability modeling method was proposed based on NHPP and incomplete repair circumstances. Then, the failure modes and the availability under different repair degree is was studied and the specific work include:
     (1) This paper first describes the domestic and international research progress, and presents a new modeling method of CNC machine tools based on various maintenance degrees considering the practical needs of the subject about reliability design of CNC machine tools and key components.
     (2) This work sets up the mathematical model of the whole CNC machine tools and solves it according to the assumption of the minimum maintenance, and put forward the mathematical model of the non-homogeneous Poisson process of CNC machine based on the theoretical analysis on repairable system. Aiming at failure data for the field test having traits such as multi-sample and random censoring, total failure time method is used to pre-treat, based on these, depending on the different fault tread, reliability model based on weibull process and twofold weibull process is put forward of which the model parameters are determined by maximum likelihood estimation. The paper establishes reliability modeling of the fault data for CNC lathes and machining centers in a machine tool plant, the result of which has better goodness of fit and is more suitable to engineering condition of the CNC machine tools as repairable system through the relevant inspection
     (3) The reliability assessment of the key components is one of the key links of assessing the reliability of CNC machine tools. A new method of modeling the reliability of the key components with incomplete maintenance is proposed considering that the maintenance degrees’impacts on the key components is between the two states“as-good-as-new”and“minimum maintenance”. This model is established based on the Kijima I incomplete maintenance model, of which the hazard rate curve had the“bathtub”form. Meanwhile, the model’s parameters are calculated using the method integrating maximum likelihood method and genetic algorithm. The results shows that: this model can not only get the critical values of early-failures period and random-failures period, but also the maintenance degrees within the random-failures period and wear out-failures period, reflecting the real states of each sub-system’s changes in reliability.
     (4) Based on the reliability models of the CNC machine tools and the key components, the failure modes of the CNC machine tools are researched, and the defects of the FMECA not considering maintenance degrees are analyzed, then the improved FMECA is proposed considering maintenance degrees. This method integrated the results of qualitative analysis and quantitative analysis, established the algorithm based on weighted Euclidean Distance and Risk Space Diagram, thus avoiding the amplification effect caused by the multiplication of the components of PRN in the traditional methods. Meanwhile, the maintenance degree is an important factor influencing the failure modes, thus it is helpful to introduce it into the quantitative analysis of criticality in order to fully research the failure modes. Through the failure mode analysis of CNC lathe, the weak link of the machine can be found out, thus providing critical references for reliability-design and reliability growth.
     (5) In the end,the availability mathematical model concerned the CNC machine maintenance is researched. According to the data of corrective maintenance, author puts forward the availability analysis method based on Monte Carlo. And the simulations of the whole machine and subsystem are made under the assumption of the minimum maintenance and imperfect maintenance. The results show that the availability model describes the instantaneous availability curve from which author gets the law of CNC machine availability. And it consummates the availability analysis method of CNC machine.
     The research rationalizes the reliability model of the CNC machine and subsystem, enriches the availability analysis method of CNC machine and improves the accuracy of the reliability evaluation. And it puts forward a new idea and method of the reliability analysis. The innovations of this thesis are mainly the following aspects:
     (1) The minimum maintenance modeling idea is used in the reliability modeling of CNC machine based on Non-Homogeneous Poisson Process. The mathematical model is made under the assumption of repair as-bad-as-old. The method has a good goodness-of-fit and provides a new solution for the whole machine modeling of CNC machine.
     (2) Establish the incomplete repair model of the key functional components. It gets the critical value and maintenance degree of the early failure period and occasional failure period. And the results can reflect the life stage characteristic of each key functional component.
     (3) In this paper, the maintenance degree is regarded as one of influence factors. Furthermore, the RPN obtained by qualitative analysis and criticality obtained by quantitative analysis are organically combined, and a mathematical method for comprehensive risk assessment is proposed, which could make the fault analysis results for CNC machine tools repairable systems accords with actual situation.
     (4) In view of the availability problem for CNC machine tools, the availability function is discussed, and a Monte-Carlo based availability mathematical model in different maintenance degree was built. In this way, we could get the availability curve in different maintenance degree, which make a new extension for available analysis methods of CNC machine tools.
引文
1. Gies, Frances and Joseph. Cathedral, Forge, and Waterwheel: Technology and Invention in the Middle Ages [M]. New York: Harper Collins, 1994.
    2. Leonardo da Vinci, Laura Gioppo. Il Codice atlantico di Leonardo da Vinci nell'edizione Hoepli 1894-1904 curata dall'Accademia dei Lincei [M]. [s.l.]: Anthelios, 2000.
    3.徐正平.世界机床发展简史[J].机械制造,1990 (8): 43-44.
    4. Pease, William. An automatic machine tool [J]. Scientific American, 1952, 187 (3): 101–115.
    5.陈循介. 2009年中国机床工业的运行特点、市场需供、问题和发展趋势[J].精密制造与自动化, 2010(2):1-5.
    6.张曙.加强基础研究,做大做强中国机床[J].制造技术与机床,2009(10): 18-19.
    7.刘强,李冬茹.国产数控机床及其关键技术发展现状及展望[J].航空制造技术, 2010(10): 26-30.
    8.王太勇,乔志峰,韩志国等.高档数控装备的发展趋势[J]. 2011,22(10): 1247-1252.
    9.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.
    10. 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.
    11.华中科技大学.机床颤振的非线性理论模型及在线临近系统[R].武汉:华中科技大学,2005.
    12.哈尔滨工程大学.数控机床故障预测系统[R].哈尔滨:哈尔滨工程大学, 2005.
    13.李雪,杨兆军等.基于主轴电动机电流的微孔钻削在线监测技术[J].农业机械学报, 2008, 39(9): 183-186.
    14.毕承恩,丁乃建等.现代数控机床[M]. 1991,北京:机械工业出版社.
    15. Pease, William. An automatic machine tool [J]. Scientific American, 1952, 187 (3): 101–115.
    16.吴祖育等.数控机床[M].上海:上海科学技术出版社, 2000.
    17.陈德全.数控机床实验[M].天津:天津大学出版社, 1997.
    18.盛伯浩.我国数控机床现状与技术发展策略[J].制造技术与机床,2005 (6): 38-44.
    19.林宋,张超英,陈世乐.现代数控机床[M].北京:化学工业出版社, 2011.
    20.陈循介.中国机床工业六十年发展状况及思考[J].精密制造与自动化. 2009 (4):1-3.
    21. Gardner Publication Company. 2011 world machine tool output & comsumption survey[EB/OL]. 2011-10-01]. http://www.gardnerweb.com/consump/analysis.html.
    22.中华人民共和国国务院.国家中长期科学和技术发展规划纲要(2006-2020年)[EB].新华社,(2006-2-9), [2011-10-01]. http://www.gov.cn/jrzg/2006-02/09/content_183787.htm
    23.郑国伟.机床工具进出口情况与需要关注的问题[J].制造技术与机床,2011(7): 17-18.
    24.曹晋华,程侃.可靠性数学引论[M].北京:高等教育出版社, 2006.
    25. A J Lotka. A contribution to the theory of self-renewing aggregates with special reference to industrial replacement[J]. The Annals of Mathematical Statistics, 1939, 10(1): 1-25.
    26. Barlow, R. E. Mathematical theory of reliability: a historical perspective[J]. IEEE Trans. Reliab.,1984(R33): 16-20.
    27. Advisory Group on the Reliability of Electronic Equipment. AGREE,Reliability of military electronic equipment[R]. Washington DC: 1957.
    28.牟致忠.可靠性设计[M].北京:机械工业出版社,1993.
    29. Rasmussen Norman. Reactor safety study. An assessment of accident risks in U. S. commercial nuclear power plants. Executive Summary[R]. USA: Federal Government of the United States, U.S. Nuclear Regulatory Commission, 1975.
    30. Kang W. Lee, et al. A Literature Survey of the Human Reliability Component in a Man-Machine System[J]. IEEE Trans. on Reliability,1988, 37(1): 24-34.
    31. N.R.S. Tait. The use of probability in engineering design---an historical survey [J]. Reliability Engineering and System Safety, 1993,40: 119-132.
    32. Tae-Jin Lim. A stochastic regime switching model for the failure process of a repairable system [J]. Reliability Engineering and System Safety, 1998, 59: 225-238.
    33. A.C.普罗尼科夫.机床的可靠性(二)[J].国外机械加工技术,1987(6): 55-58.
    34.四川机械工程协会设备维修专业委员会.机器可靠性[M].成都:四川人民出版社,1983.
    35. Sankar, T.S. A Reliability Estimate for Machine Tool Spindles Subjected to Random Forces [J]. Mechanism and Machine Theory, 1975,10:131-138.
    36. Mazzuchi, T.A, Soyer, R.Assessment of Machine Tool Reliability Using a Proportional Hazards Model [J]. Research Systems. 1989(13):405-424.
    37. Carmen E R, Gilberto F M. Reliability concepts applied to cutting tool change time [J]. Realiability Engineering and System Safety, 2010, 95: 866-873.
    38. Martin KF. A review by discussion of condition monitoring and fault diagnosis in machine tools [J]. International Journal of Machine Tools & Manufacture 1994, 34: 527-551.
    39. Ahmed, J.U, Bennett DJ. Reliability management of machine tool technology: a case study of current practice[J]. International Journal of Materials and Product Technology, 1996, 11:383-408.
    40. Angela Adamyan, David He. Analysis of sequential failures for assessment of reliability and safety of manufacturing systems[J]. Reliability Engineering and System Safety, 2002, 76, 227-236.
    41. Theodor Ira Freiheit. Reliability and productivity of recongifurable manufacturing systems [D]. Michigan: University of michigan,2003
    42. Gandhi, V.P.Agrawal, FMEA-A digraph and matrix approach [J], Journal of Reliability Engineering and System Safety, 1992, 35: 47-158.
    43. R.Sehal, O.P.Gandhi, S.Angra,.Reliability evaluation and selection of rolling element bearings[J]. Journal of Reliability Engineering and System Safety, 2000, 68: 39-52.
    44. R. Venkata Rao, O.P. Gandhi. Failure cause analysis of machine tools using digraph and matrix methods[J]. International Journal of Machine Tools & Manufacture, 2002, 42: 521-528.
    45. Komal, S.P.Sharma, Dinesh Kumar. Complex repairable industrial system reliability analysis using gablt technique [C]. [s.l.]: XXXII national systems conference, 2008.
    46. Rao, Ravipudi Venkata.Decision Making in the Manufacturing Environment: Using graph theory and fuzzy multiple atribute decision making methods[M]. [s.l.]:Springer, 2007.
    47. A.Z.Keller,A.R.R.Kamth. Reliability Analysis Of Cnc Machine Tools [J]. Reliability Engineering, 1982, 3(6): 449-473.
    48. Peter F. McGoldrick, Halil Kulluk. Machine tool reliability-A critical factor in manufacturing systems [J]. Reliability Engineering, 1986, 14(3): 205-221.
    49. NCSR 1988. AMTA Reliability Publications 1[M]. Risley:CN Machining Centre, National Centre for System Reliability, 1988.
    50. Society of Automotive Engineers. Reliability and Maintainability Guideline for Manufacturing Machinery and Equipment[R], [s.l.]: Society of Automotive Engineers, Inc. and National Center for Manufacturing Sciences, Inc. 1993,
    51. Jason R.W. Merrick, Refik Sover. A Bayesian Semiparametric Analysis of the reliability and maintenance of machine tools[J]. American Statistical Association and the American Society for Quality Technometrics, 2003, 45(1): 58-69.
    52. William F. Hagen. Effects of A Reliability Program on Machine Tools Reliability[C].[s.l.]: RAMS '06. Annual Reliability and Maintainability Symposium, 2006: 481-485.
    53. Marina Karyagina, Water Wong,Ljubica Vlacic. Reliability aspect of CNC machines- are we ready for integration? Emerging Technologies and Factory Automation[C]. INRIA/IEEE Symposium on, 1995..
    54. K. Das. A comparative study of exponential distribution vs Weibull distribution in machine reliability analysis in a CMS design [J]. Computers & Industrial Engineering, 2008, 54: 12-33.
    55. Abernethy, R. B.. The new Weibull Handbook[M]. North Palm Beach, Fla. : R.B. Abernethy, 2006.
    56. Hsu Bi-Min ; Shu Ming-Hung ,Reliability assessment and replacement for machine tools under wear deterioration[J]. International journal, advanced manufacturing technology,2010, 48: 355-365.
    57. G. Lanza, S. Niggeschmidt, P. Werner. Optimization of preventive maintenance and spare part provision for machine tools based on variable operational conditions[J]. CIRP Annals - Manufacturing Technology, 2009,58: 429-432.
    58. Lanza, G.; Niggeschmidt, S.; Werner, P.. Behavior of Dynamic Preventive Maintenance Optimization for Machine Tools[C]. Reliability and Maintainability Symposium, RAMS Annual,2009.
    59. Kazmierczak, J., Diagnostic Tools for the Needs of Analyzing the Reliability of Machines[J]. Applied Stochastic Models and Data Analysis, 1997, 13: 391-400.
    60. Kuo-shong Wang,Eang-hao Wan.Reliability Consideration of Flexible Manufacturing System from Fuzzy Information[C]. Proceedings of IMCC'93, 1993, (2).
    61.机械电子工业部机床工具司.加工中心可靠性评定方法(试行)[S]. //中华人民共和国机械行业标准.北京:机械工业出版社,1990 (内部使用).
    62.机械电子工业部机床工具司. JB/GQ1153-90数控车床可靠性的评定方法[S]//中华人民共和国机械行业标准.北京:机械工业出版社,1990.
    63. Zhou Guangwen, Jia Yazhou, Zhang Haibo, et al. A new single-sample failure model and its application to a special CNC system [J]. International Journal of Quality & Reliability Management, 2004, 22(4): 421-430.
    64. Wang Yiqiang, Jia Yazhou, et al. Field failure database of CNC lathes [J]. International Journal of Quality and Reliability Management, 1999, 16(4):330-340.
    65. Jia Yazhou, Wang Molin, Jia Zhixin. Probability distribution of machining center failures[J]. Reliability engineering & system safety, 1995, 50(1): 121-125.
    66. Wang Yiqiang, Jia Yazhou, Jiang Weiwei. Early failure analysis of machining centers: a case study[J]. Reliability Engineering and System Safety, 2001, 72: 91-97.
    67.刘学军,贾亚洲,张日明.数控机床可靠性智能网络系统控制模型及自动生成研究[J].机械工程学报,2003,39(9): 114-117.
    68. Wang Yiqiang, Jia Yazhou, et al. Field failure database of CNC lathes [J]. International Journal of Quality and Reliability Management, 1999, 16(4):330-340.
    69. Li Xiaobing, Yang Zhaojun, Li Guofa, et al. Posterior probability prediction on the key subsystem of machining center [C]. 2011 IEEE 18th international conference on industrial engineering and engineering management, 1123-1127.
    70.张英芝,申桂香,吴甦,等.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(工学版) 2009, 39(02) 378-381.
    71. Yang Zhaojun, Zhu Xiaocui,Jia Yazhou, et al. Fuzzy-Comprehensive Evaluation of Use Reliability of CNC Machine Tools[C]. Journal of Key Engineering Materials.2011, 464: 374-378.
    72.申桂香,张英芝,薛玉霞,陈炳锟,何宇.基于熵权法的数控机床可靠性综合评价[J].吉林大学学报(工学版), 2009, 39(5): 1208-1211
    73.申桂香,樊少华,张英芝,等.数控机床子系统可靠性影响度分析[J].吉林大学学报(工学版) 2010, 40: 266-269.
    74.薛玉霞,申桂香,张英芝,等.数控机床可用性信息分析系统的开发与应用[J].制造业自动化,2009,31(4): 34-36.
    75.张日明,贾亚洲,孙大文.数据挖掘技术在数控机床可靠性分析中的应用[J].吉林大学学报,2007,25(6): 650-654.
    76. Jia Y.Z., Cheng X.M., Jia Z.X.. Failure mode analysis of machining centers [C]. Proceedings of 3rd ISSAT international Conference Reliability and Quality in Design, CA, 74-76.
    77.王桂萍,贾亚洲,申桂香,乔巍巍.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: 119-122
    78.张英芝,申桂香,薛玉霞,贾亚洲,李研.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.
    79. Yang Zhaojun, Xu binbin, Chen Fei, etc. A new failure mode and effects analysis model of CNC machine tool using fuzzy theory[C]. Harbin: 2010 IEEE International Conference on Information and Automation, 2011.
    80.陈炳锟,申桂香,张英芝,王晓峰,高文礼,王志琼.随机模糊理论下的数控机床动态危害性[J].吉林大学学报(工学版), 2010,40: 262-265
    81. Jia Yazhou. Analysis and calculation of fatigue loading of machine tool gears[J]. International Journal of Fatigue, 1991, 13(6):483-487.
    82. Jia Yazhou, Zheng Di. A note on the rating of gears based on power and torque characteristics of machine tools[J]. International Journal of Machine Tools and Manufacture, 1992, 32(5): 659-669.
    83. Jia Yazhou, Shen Guixiang, Jia Zhixin, et al. Equivalent fatigue load in machine-tool probalistic reliability. Part II: Calculation methodology and practical applications [J]. International Journal of Fatigue, 1993, 15(6):479-484.
    84. Jia Yazhou, Shen Guixiang, Jia Zhixin. A reliability approach to machine tool bearings [J]. Reliability engineering & system safety, 1995, 50(1): 127-134.
    85. Wang, Y.Q., Yam, R.C.M. et al. A comprehensive reliability allocation method for design of CNC lathes [J]. Reliability Engineering and System Safety, 2001, 72(3): 247-252.
    86.杨兆军,郝庆波,陈菲,等.基于区间分析的数控机床可靠性模糊综合分配方法[J].北京工业大学学报, 2011, 37(3): 321-329.
    87. Hao Qingbo, Yang Zhaojun, Chen Fei, et al. A Fuzzy Maintainability Allocation Method for NC Machine Tools Based on Interval Analysis [C]. Guiyang : The 9th International Conference on Reliability, Maintainability and Safety, 889-896.
    88.卜波.数控机床可靠性增长技术的应用研究[D].长春:吉林大学,2008.
    89.魏领会.基于故障分析的数控车床可靠性最优分配研究[J].机床与液压,2010, 38(9): 621-624.
    90. Wang Yiqiang, Jia Yazhou, Qia Jia, et al. Load spectra of CNC machine tools [J]. Quality and reliability engineering international, 2000,16(3): 229-234.
    91.王义强,贾亚洲,于骏一,等.数控车床载荷谱数据库的建立[J].吉林工业大学学报, 1998, 1:34-38.
    92.许彬彬,杨兆军,陈菲,等.加工中心自动换刀系统可靠性试验台的研制[J].工程与试验,2010, 50(4): 50-53.
    93.杨兆军,陈菲,等.具有电液伺服加载装置的数控转塔刀架可靠性试验系统:中国, CN101963548A. [P/OL]. 2011-3.
    94.李善章.刀库运行的可靠性[J].机床,1988(3): 22-24.
    95.范继业.大扭矩谐波齿轮传动的动态分析[J].机械设计与制造,1991(4): 54.
    96.黄祖广,赵钦志,盛伯浩,等.加工中心可靠性试验载荷谱的研究[J].制造技术与机床,2008(2):60-65.
    97.黄祖广,赵钦志,盛伯浩.加工中心可靠性测试电磁环境应力仿真平台的构建[J].制造技术与机床,2008(3):82-87.
    98.中华人民共和国国家质量监督检验检疫局,中国国家标准化管理委员会. GB/T 23567.1-2009数控机床可靠性评定第1部分:总则[S].北京:中国标准出版社, 2009.
    99.中华人民共和国国家质量监督检验检疫局,中国国家标准化管理委员会. GB/T 23568.1-2009机床功能部件可靠性评定第1部分:总则[S].北京:中国标准出版社, 2009.
    100.中华人民共和国国家质量监督检验检疫局,中国国家标准化管理委员会. GB/Z 10962-2008机床电器可靠性通则[S].北京:中国标准出版社, 2008.
    101. Xie, M., Ho, S.L.. Analysis of repairable system failure data using time series models[J]. Journal of Quality in Maintenance Engineering, 1999, 5(1): 50-61.
    102. Zhang Q., Liu S.J, et al. The Architecture of Reliability Information System for CNC Machine Tool in Concurrent Engineering[J]. Materials Science Forum, 2004, 471: 603-607.
    103.张强,艾兴等.数控机床可靠性信息系统信息建模[J],山东大学学报(工学版), 2005, 35(4): 14-18.
    104. Zheng Wang, Liyang Xie, et. Time-dependent reliability models of systems with common cause failure[J]. International journal of performability engineering. 2007, 3(4): 419-432.
    105.王正.零部件与系统动态可靠性建模理论与方法[D].沈阳:东北大学,2007.
    106.吴军.基于性能参数的数控装备服役可靠性评估方法与应用[D].武汉:华中科技大学,2008.
    107.肖俊,数控机床可靠性技术的分析与研究[D].上海:上海交通大学,2007.
    108.王智明;杨建国;王国强,等.多台数控机床最小维修的可靠性评估[J].哈尔滨工业大学学报, 2011,43(7): 127-130.
    109.李星军.数控磨床整机系统可靠性分析与评价[D].杭州:浙江大学,2011.
    110. Barlow R E,Campo R. The Total Time on Test Processes and Applications to Failure Data Analysis[J]. Reliability and Fault Tree Analysing, 1975, 451-481.
    111. Crow L. H. Reliability analysis of complex repairable systems[J]. In Reliability and Biometry, F Proschan and R. J. Serfling, eds, SIAM, Philadelphia, 1974, 379-410.
    112. Klefsj? B, U Kumar. Goodness-of-fit tests for the power-law process based on the TTT-plot [J]. IEEE Transactions on Reliability, 1992, 41:593-598.
    113. Vesely, W. E., Incorporating aging effects into ptobabilistic risk analysis using a Taylor expansion approach[J]. Reliability Engineering and System Safety, 1991, 32(3):315-337.
    114. Atwood C L. Parametric estimation of time-dependent failure rates for probabilistic risk assessment[J]. Reliability Engineering and System Safety, 1992, 37:181-194.
    115. Cox D. R., P. A. Lewis. The Statistical Analysis of Series of Events[M]. London: Methuen, 1988.
    116. Jasper L. Coetzee. The role of NHPP models in the practical analysis of maintenance failure data[J]. Reliability Engineering and System Safety, 1997, 56: 161-138.
    117. Vasiliy V. Krivsov. Practical extensions to NHPP application in repairable system reliability analysis[J]. Reliability Engineering and System Safety, 2007, 92: 560-562.
    118. G. Pulcini. Modeling the failure data of a repairable equipment with bathtub type failure intensity[J].Reliability Engineering and System Safety, 2001, 71: 209-218.
    119. Maurizio Guida, Gianpaolo Pulcini. Reliability analysis of mechanical systems with bounded and bathtub shaped intensity function [J]. IEEE transactions on reliability, 2009, 58(3):432-443.
    120.陈凤腾,左洪福,王华伟,等.基于非齐次泊松过程的航空备件需求研究和应用[J].系统工程与电子技术, 2007, 29: 1585-1588.
    121.敖长林,李一军等.基于非齐次泊松过程的拖拉机发动机使用可靠性[J].机械工程学报, 2007, 43(10): 206-210.
    122. Müller A, Stoyan D. Comparison methods for stochastic models and risks[M]. New York: John Wiley & Sons, 2002.
    123. Vaurio JK. Identification of process and distribution characteristics by testing monotonic and non-monotonic trends in failure intensities and hazard rates[J]. Reliability Engineering and System Safety, 1999, 64(3):345-357.
    124. C. Caroni.“Failure limited”data and TTT-based trend tests in multiple repairable systems[J]. Reliability Engineering and System Safety. 2010, 95: 704-706.
    125. Barlow R E, Davis B.. Analysis of time between failures for repairable components[J]. Nuclear Systems Reliability Engineering and Risk Assessment, SIAM, Philadelphia, 1977, 543-561.
    126. Department of Denfence Washington D C. MIL-HDBK-189 Reliability growth management[S]. Washington D C, USA: Department of Denfense, 1981: 130-134.
    127.国防科学技术委员会. GJB/Z77-95可靠性增长管理手册[S].北京:国防科工委军标出版发行部, 1995.
    128. Crow LH. AMSAA discrete reliability grow model[R]. Army material systems analysis activity methodology office, 1983.
    129. Duane JT. Learning curve approach to reliability monitoring[J]. IEEE Transaction on Aerospace, 1964, 2(2): 563-566.
    130.周源泉,翁朝曦.可靠性增长[M].北京:科学出版社, 1992.
    131.王玉莹.多台同步可靠性增长模型存在的问题[J].航空学报, 2000, 21(5): 409-413.
    132.周源泉.关于AMSAA模型拟合优度检验的注[J].方法与应用,1997. 4(70): 25-26.
    133. Pham H, Wang H. Imperfect maintenance[J]. European Journal of Operational Research, 1996, 94(3):425-438.
    134. Lim T J. Estimating system reliability with fully masked data under brown-proschan imperfect repair model[J]. Reliability Engineering & System Safety, 1998, 59:277-289.
    135. Doyen L., and O. Gaudoin. 2002a. Modeling and assessment of maintenance efficiency for repairable systems[C]. Proceedings from ELREL Conference, 2002a, March, 19-21, Lon, France.
    136.高松,崔丽荣,杨亚坤.一类虚拟役龄不完全维修模型的统计模拟分析[J].数理统计与管理,2010,29:846-852
    137. Doyen L, and O. Gaudoin. 2002b. Modeling and assessment of maintenance efficiency[C].Proceedings from 3rd International Conference Mathematical Methods in Reliability, 2002, Trondheim, Norway.
    138. Kijima M, Sumita N. A useful generalization of renewal theory: counting process governed by non-negative Markovian increments[J]. J. Appl. Probab. 1986, 23:71–88.
    139. Kijima M. Some results for repairable systems with general repair [J], Journal of Applied Probability, 1989, 26: 89-102.
    140. Kijima M., Morimura H., and Suzuki Y.. Periodical replacement problem without assuming minimal repair[J]. European Journal of Operational Research, 1988, 37: 194-203.
    141. Chan, J.K. & Shaw, L.. Modeling repairable system with failure rate on age and maintenance [J]. IEEE Trans. Reliab., 1993, 42: 566-571.
    142. Malik M.A.K.. Reliable preventive maintenance policy[J]. AIIE Transactions, 1979, 11:221- 228.
    143. Yann Dijoux. A virtual age model based on a bathtub shaped initial intensity[J]. Reliability Engineering and System Safety 2009, 94: 982-989.
    144.任家君.可修复系统预防性维修策略的仿真与优化研究[D].长春:吉林大学,2007.
    145.胡海军,石化设备故障率分布参数和维修效果参数估计[J].西安交通大学学报, 2008(11): 1332-1335.
    146. Doyen L, Gaudoin O. Classes of imperfect repair models based on reduction of failure intensity of virtual age [J]. Reliability Engineering and System Safety 2004, 84(1):45-56.
    147.韩瑞峰.遗传算法原理与应用实例[M].北京:兵器工业出版社,2010.
    148. Reliasoft Corporation, 2005, Reliability Growth & Repairable System Analysis Reference[EP/OL] http://www.chinarel.com/onlinebook/RelGrowthWeb/whnjs.htm.
    149. Guo R., Love C. E. Statistical Analysis of an Age Model for Imperfectly Repaired Systems[J], Quality and Reliability Engineering International, 1992, 8(2): 133-146.
    150. Blanks H. R.. The Challenge of Quantitative Reliability [J]. Quality Reliability Engineering International, 1998, 14(3): 167–176.
    151. Crow, L. H.. Reliability Analysis for Complex, Repairable Systems AMSAA Technical Report No.
    138 [R]. U.S.:. Aberdeen Proving Ground, 1976.
    152. Price CJ, Pugh DR, Wilson MS, Snooke N. The flame system: automating electrical failure mode and effects analysis (FMEA)[C]. Proceddings of Annual Reliability and Maintainability Symposium, 1995: 90-95.
    153.徐凯,朱梅林.失效模式及影响分析中的模糊推理方法[J].华中理工大学学报, 1999, 27(9):23-26.
    154. Anand Pillay, Jin Wang. Modified failure mode and effects analysis using approximate reasoning[J]. Reliability Engineering and System Safety 2003, 79: 69-85.
    155.宋巍.柴油机设计阶段可靠性关键技术研究[D].成都:成都电子科技大学, 2008.
    156. Russomanno, D.J., Bonnell, R.D., Bowles, J.B.. A blackboard model of an expert system for failuremode and effects analysis[C]. Proceedings of Annual Reliability and Maintainability Symposium, 1992. : 483-486.
    157. Puneet Kukkal, John .B. Bowles & Ronald D. Bonnel,“Database Design for Failure Modes and Effects Analysis”, Proceedins of 1993 Annual Reliability and Maintainability Symposium., pp. 231-239, 1993
    158.张育维.范例库推论技术之改良及其在失效模式与效应分析上之应用[D].台湾:朝阳科技大学工业工程与管理研究所, 2001.
    159.张清亮,蔡志弘,魏秋建.失效模式与效应分析的评价方法[J].中国工业工程学刊, 2000, 17: 51-64.
    160. Ben-Day M, Raouf A. A revised failure mode and effects analysis mode[J]. Int J Quality Reliab Mgmt 1993, 3(1):43-50.
    161. Lee Wen-Kuei, Chang Yu-Hern. Applying the Fuzzy FMECA to Assess the Risks of Flight-Safety [J]. Civil Aviation Journal, 2005, 7(1): 37-58.
    162. A. C. F. Guimar?es, C. M. F. Lapa, Effects analysis fuzzy inference system in nuclear problems using approximate reasoning [J], Annals of Nuclear Energy, 2004,33: 107-115.
    163. Y.M. Wang, K.S. Chin, Gary K.K.P, and J.B. Yang, Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean [J], Expert Systems with Applications, 2009, 36: 1195-1207.
    164. Gionata Carmignani, An integrated structural framework to cost-based FMECA: The priority-cost FMECA [J], Reliability Engineering and System Safety, 2009, 94:861-871.
    165. Lee Wen-Kuei. Risk assessment modeling in aviation safety management[J]. Journal of Air Transport Management, 2006(12):267-273.
    166. Yang Zhaojun, Xu binbin, Chen Fei, etc. A new failure mode and effects analysis model of CNC machine tool using fuzzy theory[C]. 2010 IEEE International Conference on Information and Automation, Harbin.
    167. Z.H. Li, Evaluation of Academic Quality of Sci-tech Periodicals according to Euclid Distance with Weights [J], Journal of Library and Information Science in Agriculture, 2009, 21(11): 166-177.
    168. Gionata Carmignani, An integrated structural framework to cost-based FMECA: The priority-cost FMECA [J], Reliability Engineering and System Safety, 2009, 94:861-871.
    169. Chandrasekhar P, Natarajan R, Sujatha H S. Confidence limits for steady state availability of systems[J]. Microelectron Reliability, 1994, (34):1365-1367.
    170. Lim J.H., Shin S.W., Kim D.K., et al. Bootstrap confidence intervals for steady-state availability [J]. Asia-Pacific Journal of Operational Research, 2004, 21(3):407-419.
    171. Ananda, M.M.A.. Confidence intervals for steady state availability of a system with exponential operating time and lognormal repair time [J]. Applied Mathematics and Computation, 2003, 137:499-509.
    172.杜比.A著,胡卫军译.蒙特卡洛方法在系统工程中的应用[M].西安:西安交通大学出版社,2007.
    173.肖刚,李天柁.系统可靠性分析中的蒙特卡罗方法[M].北京:科学出版社,2003.

© 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