基于灰色理论的数控机床可靠性及维修性分析技术
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摘要
可靠性及维修性的评估与预测是数控机床可靠性分析的基础工作，也是数控机床可靠性设计、可靠性增长、维护及维修的前提。数控机床属于机、电、液混合的复杂系统，其结构的复杂性导致其故障机理较复杂，故障的微观表象不确定，其宏观表征较明确。数控机床及其子系统属于部分故障信息已知的系统，这样的系统常常可以看作灰色系统。因此，本文将数控机床及其子系统看作灰色系统对其进行可靠性及维修性研究。
由于现有的可靠性指标评估方法的局限性，本文提出了利用函数增减性来进行可靠性及维修性指标的评估；鉴于单一指标不能够全面地衡量出数控机床整体的可靠性水平，本文选择多个评估指标，采用灰色理论中的白化权函数实施可靠性及维修性的综合聚类评估；考虑数控机床可靠性预测及维修性预测对于数控机床可靠性的提高及日常维修和维护的重要性，根据故障数据和维修数据的不同特点，本文提出应用灰色理论建立灰色预测模型和利用指数平滑法建立维修性预测模型；同时，经验建模方法存在忽略故障发生顺序的不足，本文采用二维可靠性模型来研究故障的变化趋势，从而形成了一套数控机床可靠性及维修性评估与预测的方法体系。论文具体开展了以下几方面的研究：
1.数控机床可靠性评估
(1)在对国内、外数控机床发展现状及其可靠性研究现状分析的基础上，结合以往的经验建模方法建立了可靠性模型，针对以往在服从指数分布假设下对指标进行评估而导致评估结果与工程实际不符的问题，本文提出了服从威布尔分布的MTBF的区间估计方法，为数控机床可靠性综合评估奠定了基础。
(2)由于单一指标的估计结果较片面，不能够从整体上衡量数控机床可靠性水平的问题，因此本文提出了选择多个可靠性指标和基于灰色理论中的白化权函数实现可靠性的综合聚类评估的方法。评估的过程中，本文应用前述的区间估计结果来划分聚类区间，避免了以往综合评估中依赖专家打分的不足，使评估结果更符合实际情况。
2.数控机床维修性评估
(1)根据现有的维修数据建立维修性函数，本文借助于维修性函数获得维修性指标的点估计和区间估计，以此对数控机床的维修性做初步的估计，同时也为维修性的综合评估准备了条件。
(2)同样，本文采用灰色理论中的白化权函数方法实现维修性的综合聚类评估，以维修性指标的区间估计结果划分聚类区间，使综合聚类评估结果能更真实地反映数控机床的维修性水平。
(3)考虑子系统维修性对整机维修性的影响，本文应用信息熵理论结合白化权函数实现子系统的维修性综合评估。
3.数控机床子系统风险评估
(1)鉴于数控机床子系统之间的微观关系没有明确的表征，本文提出应用灰色理论中的灰关联理论，对数控机床子系统进行可靠性及维修性关联分析。结果表明：该方法能有效地实现子系统之间“灰量”的“白化”。
(2)基于不同的子系统对数控机床整机可靠性影响的不同，本文提出应用贝叶斯（Bayes）理论建立可靠性及维修性的后验概率模型，借助于该模型判定对数控机床影响大的关键子系统。实例分析表明：建立的后验概率模型能够快捷、准确地寻找出数控机床关键子系统，实例中数控车床的关键子系统为主轴系统、进给系统、数控系统和刀架。
(3)本文提出利用主次图和故障模式影响及危害性（Failure Mode Effects andCriticality Analysis-FMECA）分析方法对关键子系统的可靠性进行风险评估，找到发生故障的主要原因和主要故障模式，针对分析结果对故障提出相应的维修措施。分析结果表明：关键子系统故障的主要原因为电子元器件损坏，主要故障模式为元器件损坏和几何精度超差。
4.数控机床可靠性及维修性预测
(1)建立机床整机及子系统的可靠性模型，以此预测故障在考核期内的变化趋势。分析结果表明：实例中的数控车床的故障间隔时间服从两参数威布尔分布，维修时间服从对数正态分布。
(2)提出应用灰色理论建立灰色预测模型，借助于预测模型预测后续故障的发生时间；基于维修时间的特点，提出采用指数平滑法建立维修性的预测模型，应用该模型预测后续故障的维修时间。结果表明：建立的预测模型能够准确地预测后续故障的发生时间和维修时间，建模方法是可行的、有效的。
(3)鉴于经验建模方法存在忽略故障发生顺序的不足，本文利用后续预测的故障时间，将累积故障时间作为机床使用年限，故障间隔时间作为机床运行时间，以这两个变量作为建模变量建立数控机床二维可靠性模型，来具体研究故障的变化趋势。
Evaluation and prediction of reliability and maintainability are the basis of CNCmachine tool reliability research, and which are also the premise of reliability design,reliability growth and maintenance. CNC machine tools is a complex system of mechanical,electrical and hydraulic hybrid. The complexity of its structure led to more intricate failuremechanism, uncertain failure microscopic appearance, and more clear macroscopiccharacterization. So the part of the fault information of CNC machine tools and itssubsystem is known. Such kind of systems are called Grey Systems. In this paper, the CNCmachine tools and its subsystems are regarded as a grey system, and its reliability andmaintainability should be studied.
Due to the limitations of the existing reliability index assessment methods, this paperproposes to use function increase and decrease to assess reliability and maintainability index.In view of a single indicator can not fully measure the overall level of reliability of CNCmachine tools, this paper selects multiple evaluation indicators, and the grey whiteningweight function theory of reliability and maintainability of the implementation ofcomprehensive clustering evaluation. Considering the importance of prediction of reliabilityand maintainability to the reliability improvement, the routine repair and maintenance ofCNC machine tools, this paper presents the reliability prediction model established by usinggrey theory and the maintainability prediction established by using the exponentialsmoothing. Meanwhile, in order to make up for the deficiency of ignoring the failuresequence of empirical modeling methods, the two-dimensional reliability model of CNCmachine tools model is raised to get the changing tendency of the fault. Finally, a set ofmethodologies for reliability and maintenance assessment and prediction of CNC machinetools are proposed. The specific work of this paper is illustrated as follows:
1. Reliability evaluation of CNC machine tools
(1)Combined with previous experience modeling method, this paper established areliability model based on domestic and foreign development status of CNC machine toolsand research status of CNC machine tools reliability. In order to solve the problem that theevaluating results do not tally with the practical engineering under the exponentialdistribution, this paper proposes the adopted interval estimation method of MTBF obeysWeibull distribution, which lays the foundation for the comprehensive assessment of thereliability.
(2) Due to the single index is not able to measure the reliability of the whole system,the method of multiple reliability index and combined with whitening weight function of comprehensive clustering evaluation is raised. This paper uses previous interval estimationresults to partition clustering which can avoid the shortcomings of expert scoring and makethe evaluation results more consistent with practical.
2. Maintainability evaluation of CNC machine tools
(1) With the help of the existing maintenance data and function of maintainability, weobtain the point estimation and interval estimation of maintainability index, get thepreliminary evaluation of maintainability of CNC machine tools.
(2)In this paper we adopt whitening weight function to realize comprehensiveclustering evaluation of maintainability and use interval estimation results onmaintainability index to partition clustering interval. The method makes comprehensiveclustering evaluation results more truly which can reflect the maintainability of CNCmachine tools.
(3) Considering the influence of the subsystem maintainability on the whole machinemaintainability, the method of comprehensive evaluation of subsystem maintainability byusing the information entropy theory and the whitening weight function is proposed.
3. Subsystem risk assessment of CNC machine tools
(1) In view of the micro relations of CNC machine tool subsystems without explicitcharacterization, grey relational theory in grey theory is proposed to make correlationanalysis of reliability and maintainability. According to the results of the experiments, thismethod can effectively achieve the “whitening” of “grey” between sub systems.
(2) Based on the influence of different subsystems on the reliability of CNC machinetools, this paper applies Bayes theory to establish the posteriori probability model ofreliability and maintainability, and uses the model to determine the key subsystem. The casestudies show that: the establishment of the posterior probability model can quickly andaccurately find out the key subsystems of CNC machine tools. The critical subsystems ofCNC lathe are the spindle system, feeding system, numerical control systems and turret.
(3) This paper presents the use of primary and secondary diagram and FMECAanalysis for the risk assessment of critical subsystems’ reliability, finds out the main failurereason, and proposes appropriate maintenance measures of the failures according to theanalysis results. The analysis shows that: the main reason of the failure of criticalsubsystems is component damage, the primary failure mode are component damage andgeometric precision tolerance.
4. Prediction of reliability and maintainability
(1) This paper establishes the reliability models of machine tools and subsystems which can predict the changing tendency of the fault in the assessment periods. The resultsshow that: the fault intervals of CNC lathes accord with the two-parameter Weibulldistribution, and the maintenance time accords with the logarithmic normal distribution.
(2) Grey theory is applied to establish grey fault prediction model. Based on thecharacteristics of maintenance time, the exponential smoothing method is adopted toestablish the prediction model of maintainability. The results indicate that: the predictionmodel can accurately predict subsequent fault time and maintenance time.
(3) In the light of existing empirical modeling methods ignore the fault sequence, thispaper puts forward using subsequent forecast fault time to establish the two-dimensionalreliability model of CNC machine tools to get the changing tendency of the fault; the modeladopts the cumulative fault time and fault interval as variables.
由于现有的可靠性指标评估方法的局限性，本文提出了利用函数增减性来进行可靠性及维修性指标的评估；鉴于单一指标不能够全面地衡量出数控机床整体的可靠性水平，本文选择多个评估指标，采用灰色理论中的白化权函数实施可靠性及维修性的综合聚类评估；考虑数控机床可靠性预测及维修性预测对于数控机床可靠性的提高及日常维修和维护的重要性，根据故障数据和维修数据的不同特点，本文提出应用灰色理论建立灰色预测模型和利用指数平滑法建立维修性预测模型；同时，经验建模方法存在忽略故障发生顺序的不足，本文采用二维可靠性模型来研究故障的变化趋势，从而形成了一套数控机床可靠性及维修性评估与预测的方法体系。论文具体开展了以下几方面的研究：
1.数控机床可靠性评估
(1)在对国内、外数控机床发展现状及其可靠性研究现状分析的基础上，结合以往的经验建模方法建立了可靠性模型，针对以往在服从指数分布假设下对指标进行评估而导致评估结果与工程实际不符的问题，本文提出了服从威布尔分布的MTBF的区间估计方法，为数控机床可靠性综合评估奠定了基础。
(2)由于单一指标的估计结果较片面，不能够从整体上衡量数控机床可靠性水平的问题，因此本文提出了选择多个可靠性指标和基于灰色理论中的白化权函数实现可靠性的综合聚类评估的方法。评估的过程中，本文应用前述的区间估计结果来划分聚类区间，避免了以往综合评估中依赖专家打分的不足，使评估结果更符合实际情况。
2.数控机床维修性评估
(1)根据现有的维修数据建立维修性函数，本文借助于维修性函数获得维修性指标的点估计和区间估计，以此对数控机床的维修性做初步的估计，同时也为维修性的综合评估准备了条件。
(2)同样，本文采用灰色理论中的白化权函数方法实现维修性的综合聚类评估，以维修性指标的区间估计结果划分聚类区间，使综合聚类评估结果能更真实地反映数控机床的维修性水平。
(3)考虑子系统维修性对整机维修性的影响，本文应用信息熵理论结合白化权函数实现子系统的维修性综合评估。
3.数控机床子系统风险评估
(1)鉴于数控机床子系统之间的微观关系没有明确的表征，本文提出应用灰色理论中的灰关联理论，对数控机床子系统进行可靠性及维修性关联分析。结果表明：该方法能有效地实现子系统之间“灰量”的“白化”。
(2)基于不同的子系统对数控机床整机可靠性影响的不同，本文提出应用贝叶斯（Bayes）理论建立可靠性及维修性的后验概率模型，借助于该模型判定对数控机床影响大的关键子系统。实例分析表明：建立的后验概率模型能够快捷、准确地寻找出数控机床关键子系统，实例中数控车床的关键子系统为主轴系统、进给系统、数控系统和刀架。
(3)本文提出利用主次图和故障模式影响及危害性（Failure Mode Effects andCriticality Analysis-FMECA）分析方法对关键子系统的可靠性进行风险评估，找到发生故障的主要原因和主要故障模式，针对分析结果对故障提出相应的维修措施。分析结果表明：关键子系统故障的主要原因为电子元器件损坏，主要故障模式为元器件损坏和几何精度超差。
4.数控机床可靠性及维修性预测
(1)建立机床整机及子系统的可靠性模型，以此预测故障在考核期内的变化趋势。分析结果表明：实例中的数控车床的故障间隔时间服从两参数威布尔分布，维修时间服从对数正态分布。
(2)提出应用灰色理论建立灰色预测模型，借助于预测模型预测后续故障的发生时间；基于维修时间的特点，提出采用指数平滑法建立维修性的预测模型，应用该模型预测后续故障的维修时间。结果表明：建立的预测模型能够准确地预测后续故障的发生时间和维修时间，建模方法是可行的、有效的。
(3)鉴于经验建模方法存在忽略故障发生顺序的不足，本文利用后续预测的故障时间，将累积故障时间作为机床使用年限，故障间隔时间作为机床运行时间，以这两个变量作为建模变量建立数控机床二维可靠性模型，来具体研究故障的变化趋势。
Evaluation and prediction of reliability and maintainability are the basis of CNCmachine tool reliability research, and which are also the premise of reliability design,reliability growth and maintenance. CNC machine tools is a complex system of mechanical,electrical and hydraulic hybrid. The complexity of its structure led to more intricate failuremechanism, uncertain failure microscopic appearance, and more clear macroscopiccharacterization. So the part of the fault information of CNC machine tools and itssubsystem is known. Such kind of systems are called Grey Systems. In this paper, the CNCmachine tools and its subsystems are regarded as a grey system, and its reliability andmaintainability should be studied.
Due to the limitations of the existing reliability index assessment methods, this paperproposes to use function increase and decrease to assess reliability and maintainability index.In view of a single indicator can not fully measure the overall level of reliability of CNCmachine tools, this paper selects multiple evaluation indicators, and the grey whiteningweight function theory of reliability and maintainability of the implementation ofcomprehensive clustering evaluation. Considering the importance of prediction of reliabilityand maintainability to the reliability improvement, the routine repair and maintenance ofCNC machine tools, this paper presents the reliability prediction model established by usinggrey theory and the maintainability prediction established by using the exponentialsmoothing. Meanwhile, in order to make up for the deficiency of ignoring the failuresequence of empirical modeling methods, the two-dimensional reliability model of CNCmachine tools model is raised to get the changing tendency of the fault. Finally, a set ofmethodologies for reliability and maintenance assessment and prediction of CNC machinetools are proposed. The specific work of this paper is illustrated as follows:
1. Reliability evaluation of CNC machine tools
(1)Combined with previous experience modeling method, this paper established areliability model based on domestic and foreign development status of CNC machine toolsand research status of CNC machine tools reliability. In order to solve the problem that theevaluating results do not tally with the practical engineering under the exponentialdistribution, this paper proposes the adopted interval estimation method of MTBF obeysWeibull distribution, which lays the foundation for the comprehensive assessment of thereliability.
(2) Due to the single index is not able to measure the reliability of the whole system,the method of multiple reliability index and combined with whitening weight function of comprehensive clustering evaluation is raised. This paper uses previous interval estimationresults to partition clustering which can avoid the shortcomings of expert scoring and makethe evaluation results more consistent with practical.
2. Maintainability evaluation of CNC machine tools
(1) With the help of the existing maintenance data and function of maintainability, weobtain the point estimation and interval estimation of maintainability index, get thepreliminary evaluation of maintainability of CNC machine tools.
(2)In this paper we adopt whitening weight function to realize comprehensiveclustering evaluation of maintainability and use interval estimation results onmaintainability index to partition clustering interval. The method makes comprehensiveclustering evaluation results more truly which can reflect the maintainability of CNCmachine tools.
(3) Considering the influence of the subsystem maintainability on the whole machinemaintainability, the method of comprehensive evaluation of subsystem maintainability byusing the information entropy theory and the whitening weight function is proposed.
3. Subsystem risk assessment of CNC machine tools
(1) In view of the micro relations of CNC machine tool subsystems without explicitcharacterization, grey relational theory in grey theory is proposed to make correlationanalysis of reliability and maintainability. According to the results of the experiments, thismethod can effectively achieve the “whitening” of “grey” between sub systems.
(2) Based on the influence of different subsystems on the reliability of CNC machinetools, this paper applies Bayes theory to establish the posteriori probability model ofreliability and maintainability, and uses the model to determine the key subsystem. The casestudies show that: the establishment of the posterior probability model can quickly andaccurately find out the key subsystems of CNC machine tools. The critical subsystems ofCNC lathe are the spindle system, feeding system, numerical control systems and turret.
(3) This paper presents the use of primary and secondary diagram and FMECAanalysis for the risk assessment of critical subsystems’ reliability, finds out the main failurereason, and proposes appropriate maintenance measures of the failures according to theanalysis results. The analysis shows that: the main reason of the failure of criticalsubsystems is component damage, the primary failure mode are component damage andgeometric precision tolerance.
4. Prediction of reliability and maintainability
(1) This paper establishes the reliability models of machine tools and subsystems which can predict the changing tendency of the fault in the assessment periods. The resultsshow that: the fault intervals of CNC lathes accord with the two-parameter Weibulldistribution, and the maintenance time accords with the logarithmic normal distribution.
(2) Grey theory is applied to establish grey fault prediction model. Based on thecharacteristics of maintenance time, the exponential smoothing method is adopted toestablish the prediction model of maintainability. The results indicate that: the predictionmodel can accurately predict subsequent fault time and maintenance time.
(3) In the light of existing empirical modeling methods ignore the fault sequence, thispaper puts forward using subsequent forecast fault time to establish the two-dimensionalreliability model of CNC machine tools to get the changing tendency of the fault; the modeladopts the cumulative fault time and fault interval as variables.
引文
[1]2008中国制造业高峰论坛NOVEMBER5，2008商务周刊:86-87.
[2]吴柏林.机遇前所未有，挑战更加严峻[J].机械工程师,2008(1):5-7.
[3]王先逵.我国数控机床的创新和发展[J].金属加工,2012(4):25-28.
[4]任国梁,石毅等.我国数控机床工业50年[C].全国机械装备先进制造技术高峰论坛，2009：458-466.
[5]胡亚波,吴玉文.我国数控机床的状况与发展[J].机床与液压,2004(7)：4-6.
[6]盛伯浩.我国数控机床现况与技术发展策略[J].数控技术,2006(2):17-21.
[7]李冬茹.我国数控机床技术发展与展望[J].世界制造技术与装备市场,2006(2):30-35.
[8]杨兆军,陈传海,陈菲,李国发.数控机床可靠性技术的研究进展[J].机械工程学报,2013:.http://www.cnki.net/kcms/detail/11.2187.TH.20130910.1138.026.html.
[9] Pease, William. An automatic machine tool[J].Scientific American,1952,187(3)：101-115.
[10]数控机床发展现状及发展趋势.《金属加工世界》2012(1).
[11]曹伟.我国数控机床发展的现状与对策[J].现代经济信息.
[12]林宋,张超英等.现代数控机床[M].北京:化学工业出版社,2011.
[13]张玉峰.浅谈我国数控机床的现状与发展趋势[J].金属加工,2010（21）:53-56.
[14]陈循介.中国机床工业六十年发展状况几思考[J].精密制造与自动化,2009(4):1-3.
[15]周延佑.数控机床发展新趋势与我国相应的对策[J].机械工艺师,2001(2):5-7.
[16]盛伯浩,唐华.数控机床技术发展浅析[J].航空制造技术,2002(6):12-17.
[17]刘小敏,王元生.我国数控机床的现状与发展[J].机械研究与应用,2006(4):22-25.
[18]朱同兴.国内外数控技术发展动向[J].数控技术与应用,2001(3):10-12.
[19]李宏胜.现代数控系统的技术特点与发展趋势[J].制造业自动化,2002(11):1-3.
[20]曹晋华,程侃.可靠性数学引论[M].北京:高等教育出版社,2006.
[21]居滋培.可靠性程[M].原子能出版社,2003:3,71.
[22]马运义等.可靠性技术的应用[M].北京:国防工业出版社,1996,4:28-32.
[23]可靠性设计大全[M].中国标准出版社,2008,2.
[24] Advisory Group on the Reliability of Electronic Equipment. AGREE, Reliability of militaryelectronic equipment[R].Washington DC:1957.
[25] 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.
[26] 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.
[27]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.
[28]张英芝,申桂香,吴甦,薛玉霞,何宇.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(2):378-381.
[29] W. Nelson. An Application of Graphical Analysis of Repair Data[J].Quality and ReliabilityEngineering International,1998,14:49-52.
[30]贾志成,杨兆军,赵宏安,张英芝.国产加工中心寿命分布及可靠性评估研究[J].2010International Conference on Broadcast Technology and Multimedia Communication,2010,4:391-394.
[31]罗巍.数控机床故障分析与可靠性评价技术的研究[D].长春:吉林大学,2011.
[32] O’connor PDT，Newton D， Bromley R. Practical reliability engineering.ed. England：Wiley；2002.
[33]许彬彬,杨兆军,陈菲等.非齐次泊松过程的数控机床可靠性建模[J].吉林大学学报(工学版),2011,41(2):210-214.
[34] Ebling CE. An introduction to reliability and maintainability engineering. New Delhi：TataMcGraw Hill Publishing Company Limited;1977.
[35] Li Xiaobing, Yang Zhaojun, Li Guofa, et al. Posterior probability prediction on the key subsystemof machining center [C].2011IEEE18th international conference on industrial engineering andengineering management:1123-1127.
[36] A.Z.Keller,A.R.R.Kamath, et al. Reliability analysis of CNC machine tools[J].ReliabilityEngineering,1982,3(6):449-473.
[37] Dai Yi，Zhou Yun-fei and Jia Ya-zhou. Distribution of time between failures of machining centerbased on type I censored data[J].Reliability Engineering and System Safety,2003，79(3):377-379．
[38] K. Das. A comparative study of exponential distribution vs Weibull distribution in machinereliability analysis in a CMS design[J].Computers&Industrial Engineering,2008,54:12-33
[39] Abernethy, R.B.The new Weibull Handbook[M].North Palm Beach, Fla.: R.B.Abernethy,2006.
[40] A.J. Watkins, D.J. Leech. Towards Automatic Assessment of Reliability for Data from a WeibullDistribution[J].Reliability Engineering&System Safety,1989,24(4):343-350.
[41] D.M. Brki. Interval estimation of the parameters β and η of the two-parameter Weibulldistribution[J].Microelectronics Reliability,1990,30(1):39-42.
[42] T.Bucar, M.Nagode. Reliability approximation using finite Weibull mixturedistributions[J].Reliability Engineering and System Safety,84(2004):241-251.
[43] Marko Nagode, Matija Fajdiga. An improved algorithm for parameter estimation suitable formixed Weibull distributions[J].International Journal of Fatigue,2000,22(1):75-80.
[44] M.T. Todinov. Is Weibull distribution the correct model for predicting probability of failureinitiated by non-interacting flaws?[J].International Journal of Solids and Structures,2009,46(3):887-901.
[45] Jean-Francois Castet, Joseph H.Saleh. Satellite and satellite subsystems reliability: Statistical dataanalysis[J].Reliability Engineering and System Safety,94(2009):1718-1728.
[46] Saralees Nadarajah. On the moments of the modified Weibull distribution[J].ReliabilityEngineering&System Safety,2005,90(1):114-117.
[47] Zhejun Gong. Estimation of mixed Weibull distribution parameters using the SCEM-UAalgorithm：Application and comparison with MLE in automotive reliability analysis[J]. ReliabilityEngineering&System Safety,2006,91(8):915-922.
[48] R.Jiang, D.N.P.Murthy. Reliability modeling involving two Weibull distributions[J]. ReliabilityEngineering and System Safety,47(1995):187-198.
[49] Satoru Matsushita, Kouichi Hagiwara et al. Lifetime data analysis of disease and aging by theweibull probability distribution [J].Journal of Clinical Epidemiology,1992,45(10):1165-1175.
[50] Hongzhu Qiao, Chris P.Tsokos. Estimation of the three parameter Weibull probabilitydistribution[J]. Mathematics and Computers in Simulation,1995,39(1):173-185.
[51]倪中新,费鹤良.威布尔分布无失效数据的统计分析[J].应用数学学报,2003,26(3)：533-543．
[52] E.F. Chace. Right-censored, grouped life test data analysis assuming a two-parameter Weibulldistribution function[J].Microelectronics Reliability,1976,15(5):497-499.
[53] S.Tanaka, M.Ichikawa. Approximate formula of coefficient of variation for Weibull distribution[J].Reliability Engineering,1983,4(3):141-143.
[54]盛骤,谢式千,潘承毅.概率论与数理统计[M].北京:高等教育出版社,2001.
[55] Shoji Kuchii, Naoto Kaio, Shunji Osaki. Simulation comparisons of point estimation methods inthe2-parameter weibull distribution[J].Microelectronics Reliability,1979,19(4):333-336.
[56] D.M. Brkich. One method for interval estimation of Weibull shape parameter[J]. MicroelectronicsReliability,1984,24(4):659-660.
[57] Nakagawa T. Periodic and sequential preventive maintenance policies[J].Journal of AppliedProbability1986;23,536-542.
[58] Wu S M, Derek C C. Preventive maintenance models with random maintenance quality[J].Reliability Engineering and System Safety2005;90,99-105.
[59] Zhou X J, Xi L F, Lee J.Reliability-centered predictive maintenance scheduling for a Continuouslymonitored system subject to degradation[J].Reliability Engineering and System Safety2007；92,530-534.
[60] T.S Sankar. A reliability estimate for machine tool spindles subjected to random forces[J].Mechanism and Machine Theory,1975,10(2):131-138.
[61] Lee, Seung-Woo, Han, Seung-Woo, Lee, Husang. Reliability evaluation of an oil cooler for ahigh-precision machining center[J].INTERNATIONAL JOURNAL OF PRECISIONENGINEERING AND MANUFACTURING,2007(3):50-53.
[62] Gisela Lanza, Stephan Niggeschmidt. Behavior of Dynamic Preventive Maintenance Optimizationfor Machine Tools[J].Reliability and Maintainability Symposiam,2009:315-320.
[63] Gisela Lanza, Stephan Niggeschmidt. Optimization of preventive maintenance and spare partprovision for machine tools based on variable operational conditions[J].CIRP Annals-Manufacturing Technology,2009(58):429-432.
[64] Fleischer.J, Niggeschmidt.S,Wawerla.M. Optimizing the Life-Cycle-Performance of machinetools by reliability and availability prognosis[J]. Advances in Life Cycle Engineering forSustainable Manufacturing.2007:329-334.
[65] Yang, M.; Lee, J.. Measurement and prediction of thermal errors of a CNC machine center usingtwo spherical balls[J].Journal of Materials Processing Technology,1998：180-189.
[66] Yang, S.; Yuan, J.; Ni, J. The Improvement of thermal error modeling and compensation onmachine tools by CMAC neural network [J].International Journal of Machine Tools andManufacture,36(4),1996.
[67] Fleischer, J. Increasing the reliability of machine tools by use of an FMEA-based conditionmonitoring system[J].VDI Berichte,2007:55-64.
[68]庞林花.我国数控机床市场分析[J].机电产品市场，2003:4-7.
[69]邬俊奇.数控机床可靠性时间模型与维修性指标评估[D].长春:吉林大学,2003.
[70]郝庆波.数控机床可靠性及维修性的模糊综合分配与预计[D].长春:吉林大学,2012.
[71] Hao Qingbo, Yang Zhaojun, Chen Fei, et al. A Fuzzy Maintainability Allocation Method for NCMachine Tools Based on Interval Analysis [C].Guiyang: The9th International Conference onReliability, Maintainability and Safety,889-896.
[72]韩爱国.数控车床维修性的分析与研究[D].长春:吉林大学,2004.
[73]王珊.面向寿命周期的加工中心可信性动态模型研究[D].长春:吉林大学,2009.
[74]杨兆军,郝庆波,陈菲,许彬彬,李小兵,赵宏伟.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)：321-328.
[75]樊少华.基于模糊理论的数控车床可靠性分配[D].长春:吉林大学,2011.
[76] Jia Yazhou, Shen Guixiang, Jia Zhixin. A reliability approach to machine tool bearings[J].Reliability engineering&system safety,1995,50(1):127-134.
[77] Jia Yazhou, Zheng Di. A note on the rating of gears based on power and torque characteristics ofmachine tools[J].International Journal of Machine Tools and Manufacture,1992,32(5):659-669.
[78] Jia Yazhou. Analysis and calculation of fatigue loading of machine tool gears[J].InternationalJournal of Fatigue,1991,13(6):483-487.
[79] Jia Yazhou, Shen Guixiang, Jia Zhixin, et al. Equivalent fatigue load in machine-tool probalisticreliability. Part II：Calculation methodology and practical applications [J]. International Journal ofFatigue,1993,15(6):479-484.
[80]郝庆波,杨兆军,陈传海,陈菲,李国发.基于区间层次分析法的数控机床可靠性预计[J].吉林大学学报(工学版),42(4):845-850.
[81]王义强，贾亚洲，于均一等.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.
[82] Wang Yiqiang, Jia Yazhou, Qia Jia, et al. Load spectra of CNC machine tools [J]. Quality andreliability engineering international,2000,16(3):229-234.
[83]邱成. CK7815数控车床可靠性分析与研究[D].长春:吉林工业大学,1999.
[84]张英芝.基于模糊与灰色理论的数控车床可靠性分析[D].长春:吉林大学,2005.
[85]于捷,贾亚洲.数控车床故障模式影响与致命性分析[J].哈尔滨工业大学学报,2005（12）:1725-1727.
[86]韩烨.基于FTA的数控冲床重要度分析[D].长春:吉林大学,2008.
[87] Y.M.Wang, K.S.Chin, Gary K.K.P. Risk evaluation in failure mode and effects analysis usingfuzzy weighted geometric mean[J].Expert Systems with Applications,2009,36:1195-1207.
[88]王桂萍.面向绿色的加工中心故障模式影响分析[J].吉林工程技术师范学院学报,2011(6)：56-59.
[89] Zhaojun Yang，Chuanhai Chen，Fei Chen，et al.Reliability Analysis of Machining Center Basedon the Field Data，Maintenance and Reliability,2013,15(2):147-155.
[90] Wang Yiqiang, Jia Yazhou, et al. Field failure database of CNC lathes [J].International Journal ofQuality and Reliability Management,1999,16(4):330-340.
[91]王继利,杨兆军,李国发,朱晓翠.基于改进EM算法的多重威布尔可靠性建模方法[J].吉林大学学报（工学版），2013.http://www.cnki.net/kcms/detail/22.1341.T.20130912.0445.007.html.
[92] Chuanhai Chen,Zhaojun Yang,Fei Chen，et al.The Study of Reliability Modeling Using locksimand Weibull++Software for Machining Center,AMM,2013,274(49)：49-52.
[93]许彬彬.基于维修程度的数控机床可靠性建模与分析[D].长春:吉林大学,2011.
[94]李小兵.加工中心时间动态可靠性建模[D].长春:吉林大学,2012.
[95]杨兆军,李小兵,许彬彬,陈菲,朱晓翠,郝庆波.加工中心时间动态可靠性建模[J].机械工程学报,2012,48(2)：16-22.
[96]许彬彬,杨兆军等.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.
[97]咸博.圆盘式刀库机械手自动换刀系统可靠性试验台研究[D].长春：吉林大学，2011.
[98]杨兆军等.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.
[99]张忠松.加工中心可靠性评价指标的扩展及评价方法研究[D].长春:吉林大学,2003.
[100]盛菲.数控系统软件可靠性研究及应用[D].长春:吉林大学,2004.
[101]杜庆林.数控系统可靠性评价体系[D].长春:吉林大学,2004.
[102]于捷,申桂香.数控机床可靠性评价方法的研究[J].机床与液压,2007(11)：174-176.
[103]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,2008,36:171-174.
[104]张学文,贾亚洲,江旭东.基于VB的数控机床可靠性评价系统[J].机械制造,2008,46(2):27-29.
[105]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.
[106]申桂香,张英芝等.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）,2009,38（5）:1208-1211.
[107]徐洪福.考虑故障危害的国产数控车床可靠性综合评价[D].长春:吉林大学,2011.
[108]陈炳琨.数控机床可靠性试验设计及评估方法研究[D].长春:吉林大学,2011.
[109] Yang Zhaojun, Zhu Xiaocui, Jia Yazhou, et al. Fuzzy-Comprehensive valuation of Use Reliabilityof CNC Machine Tools[C].Journal of Key Engineering Materials.2011,464:374-378.
[110] Xiaocui Zhu,Fei chen et al. Subsystems risk assessment of the CNC machine tools [C]. AppliedMechanics and Materials,2013,432:310-315.
[111] Xiaocui Zhu, Fei Chen, Xiaobing Li et al. Key subsystem identification of the CNC machinetools[C].Applied Mechanics and Materials,2013,329:157-162.
[112]李善章.刀库运行的可靠性[J].机床,1988(3):22-24.
[113]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.
[114]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/T23567.1-2009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.
[115]黄祖广,赵钦志,盛伯浩,张维.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.
[116]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.
[117]关贺.数控车床可靠性研究与应用[D].上海:上海交通大学,2007.
[118]王智明.数控机床的可靠性评估与不完全预防维修及其应用[D].上海:上海交通大学，2011.
[119]王智明,杨建国等.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.
[120]杨建国,王智明等.数控机床可靠性指标的似然比检验区间估计[J].机械工程学报，2012(1):9-22.
[121]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.
[122] Xie, M, Ho, S.L. Analysis of repairable system failure data using time series models[J]. Journal ofQuality in Maintenance Engineering,1999,5(1)：50-60.
[123]迟向磊.我国数控机床可靠性研究概况[J].机械工艺师,2001(3):22-23.
[124]王桂萍,贾亚洲,周广文.基于模糊可拓层次分析法的数控机床绿色度评价方法及应用[J].机械工程学报，2010.2，46(3):141-147.
[125]陈云雷.面向并行工程的数控车床可靠性研究[D].长春:吉林大学，2004.
[126]乔薇薇.数控系统可靠性建模及熵权模糊综合评价[D].长春:吉林大学，2008.
[127]申桂香，邵娜，张英芝等.基于可拓学理论的数控机床可靠性评价[J].吉林大学学报(工学版),2011,41(1):106-109.
[128]王桂萍,贾亚洲等.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.
[129]郑锐.基于可靠性分析的数控机床维修策略研究[D].长春:吉林大学，2011.
[130]张建华,王占林.基于模糊神经网络的故障诊断方法的研究[J].北京航空航天大学学报，1997(4):502-506.
[131]方敏,陈雁翔.基于模糊神经网络的机械故障诊断方法的研究[J].控制理论与应用，1998(3):460-464.
[132]王润孝,高利辉等.基于故障层次模型的集成神经网络诊断[J].控制与检测,2005(3)：44-46.
[133]余香梅,罗良玲.基于神经网络的数控机床可靠性预测的研究与实现[J].现代机械，2007(2):47-49.
[134]杨兆军,王继利,李国发,张新戈.冲压机床可靠性增长的模糊层次分析预测方法[J].吉林大学学报（工学版）2013.http://www.cnki.net/kcms/detail/22.1341.T.20130814.1756.006.html.
[135]薛丹.基于模糊神经网络评判方法的加工中心故障分析研究[D].长春:吉林大学，2005.
[136]贾志新,张宏斌等.基于径向基函数神经网络的电火花线切割机床可靠性数据模拟生成[J].机械工程学报,2010(2):145-149.
[137]贾志新,张宏斌,郗安民.利用神经网络扩充数控机床可靠性数据[J].吉林大学学报(工学版),2011,41(2):403-407.
[138] Y. Zhaojun, L. Wei, C. Yanhong. Study on increasing micro-drill reliability by vibrating drilling[J].Reliability Engineering&System Safety,1998,61(3):229–233.
[139]李雪.基于粗糙集模糊神经网络的微孔钻削在线监测研究[D].长春:吉林大学,2008.
[140]杨兆军,李雪等.微细钻孔的模糊神经网络在线监测[J].吉林大学学报（工学版），2007(6):1336-1340.
[141]苗夺谦,李道国.粗糙集理论、算法与应用[M].北京:清华大学出版社,2000.
[142]杨林娟,沈士明.基于粗糙集理论的故障树重要度分析[J].南京工业大学学报，2007(1):60-64.
[143]杨兆军,李雪等.基于粗糙集模糊控制的微孔钻削在线监测[J].光学精密工程，2008(7):1230-1235.
[144] ZhaoJun Yang, Xue Li et al. Development of a rough set-based fuzzy neural network for onlinemonitoring of micro-drilling[J].International Journal of Advanced Manufacturing Technology,(2009)41:219-225.
[145] Ashutosh Gupta, Bhaswati Mukherjee, S.K. Upadhyay. Weibull extension model: A Bayes studyusing Markov chain Monte Carlo simulation[J].Reliability Engineering&System Safety,2008,93(10):1434-1443.
[146]陈乐心.汽车零部件可靠性评估的小样本方法[D].南京:南京航空航天大学,2008.
[147] Al Omari Mohammed Ahmed, Noor Akma Ibrahim. Bayesian Estimator for Weibull Distributionwith Censored Data using Extension of Jeffrey Prior Information[J]. Procedia-Social andBehavioral Sciences,2010,(8):663-669.
[148]刘义,王国玉等.一种基于灰色距离测度的小样本数据区间估计方法[J].系统工程与电子技术,2008(1):116-119.
[149] A.A. Abdel-Wahid, A. Winter bottom. Approximate Bayesian estimates for the weibull reliabilityfunction and hazard rate from censored data[J].Journal of Statistical Planning and Inference,1987,(16):277-283.
[150] Ahmed A. Soliman, Ahmed H. Abd-Ellah, Naser A.Abou-Elheggag, Essam A. Ahmed. ModifiedWeibull model： A Bayes study using MCMC approach based on progressive censoringdata[J].Reliability Engineering&System Safety,2012,100:48-57.
[151] Ozge Doguc, Jose Emmanuel Ramirez-Marquez. A generic method for estimating systemreliability using Bayesian networks[J].Reliability Engineering&System Safety,2009,94(2):542-550.
[152] Sankaran Mahadevan, Ramesh Rebba. Validation of reliability computational models using Bayesnetworks[J].Reliability Engineering&System Safety,2005,87(2):223-232.
[153] H.Akman, L.Huwang. Bayes Computation for Reliability Estimation. IEEE Trans. onReliability,1997,46(1):52-55.
[154] S. Kumar, Ram C. Tiwari. Bayes estimation of the reliability of a system under a randomenvironment governed by a Dirichlet prior[J].Microelectronics Reliability,1989,29(2):255-265.
[155] Latife G rkemli, Selda Kapan Ulusoy. Fuzzy Bayesian reliability and availability analysis ofproduction systems[J].Computers and Industry Engineering,2010,59:690-696.
[156] Martin Shaw. Use of Bayes' theorem and the beta distribution for reliability estimation purposes[J].Reliability Engineering&System Safety,1991,31(2):145-153.
[157] Wu Hsien-chung. Fuzzy Bayesian system reliability assessment based on exponential distribution[J]. Applied Mathematical Modeling,2006,30(6):509-530.
[158]王燕萍，吕震宙.一种基于Gibbs抽样的可靠性增长Bayes方法[J].西北工业大学学报，2007,25(6):784-788．
[159] Geman Stuart，Geman Donald. Stochastic Relaxation，Gibbs distributions，and the Bayesianrestoration of images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，1984，PAMI-6(6):721-741．
[160]李洪双.小子样可靠性分析方法及应用研究[D].西安：西北工业大学,2006．
[161] Noren Niklas G，Orre Roland．Case based imprecision estimates for Bayes classifiers with theBayesian Bootstrap[J].Machine Learning,2005,58(1):79-84．
[162]刘晗.基于Bayes理论的小子样可靠性评定方法研究[D].国防科学技术大学,2006.
[163]刘贵昌.小子样条件下舰船可靠性评估方法及评估数据采集与分析[D].上海交通大学,2008.
[164]汤银才,侯道燕.三参数Weibull分布参数的Bayes估计[J].系统科学与数学,2009，29(1)：109-115.
[165]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.
[166]刘思峰,党耀国等.灰色系统理论及其应用[M].北京:科学出版社,2010.
[167]陈举华,于奎刚,史岩彬.基于灰色理论的机电系统全寿命小子样系统研究[J].机械设计,2005(9):7-9.
[168]张彦.基于灰色理论的汽车产品可靠性工程研究[D].合肥:合肥工业大学,2006.
[169]宋立涛.基于灰色理论的（重型）汽车系统可靠性研究[D].西安:西安电子科技大学，2011.
[170]江龙平,徐可君.叶片振动的灰色可靠性研究.汽轮机技术,2002,5:285-286.
[171]陈举华,秦惠芳.小子样机械系统故障分析与预测研究.农业机械学报,1999,6:31-35.
[172]张英芝.基于模糊与灰色理论的数控车床可靠性分析[D].长春:吉林大学,2005.
[173]张英芝,贾亚洲等.数控机床故障分布的灰关联分析法[J].农业机械学报,2004(6)：195-197.
[174]邓聚龙.灰色系统理论教程.武汉:华中理工大学出版社,1990.
[175]陈举华,郭毅之.GM模糊优化方法在机械系统故障预测中的应用.中国机械工程，2002,19:1658-1660.
[176]吕艳辉,潘成胜.基于AHP的灰色评估模型及其应用[J].火力与指挥控制,30(8),2005.
[177]侯丰伟,杨育等.基于灰色理论和模糊综合评价的设备评标方法研究[J].机械,37(3),2010.
[178]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.
[179]茆诗松,汤银才,王玲玲.可靠性统计[M].北京:高等教育出版社,2008.
[180]章国栋.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.
[181]张英芝,申桂香等.数控车床故障分布规律及可靠性[J].农业机械学报,2006(1)：156-159.
[182]李忠范,高文森.数理统计与随机过程[M].吉林:吉林大学出版社,2000.
[183]中华人民共和国国家标准,GB5080.4-85设备可靠性试验可靠性测定试验的点估计和区间估计方法（指数分布）.北京:国家标准局,1985.
[184]王秉刚.汽车可靠性工程[M].北京:机械工业出版设,1991.
[185]杨伟民.可靠性.维修性.保障性总论[M].北京:国防工业出版社,1995.
[186]甘茂治.维修性设计与验证[M].北京:国防工业出版社,1995.
[187] G. Carmignani. An integrated structural framework to cost-based FMECA：The priority-costFMECA. Reliability Engineering and System Safety,2009(4)：861-871, April2009.
[188] E.P. Zafiropoulos, E.N. Dialynas,“Reliability prediction and failure mode effects and criticalityanalysis of electronic devices using fuzzy logic,” International Journal of Quality&ReliabilityManagement,22(2):183-200,2005.
[189] M.Braglia, M.Frosolini, R.Montanri.Fuzzy TOPSIS approach for failure mode, effects andcriticality analysis[J].Quality and Reliability Engineering International，2003,19：425-443.
[190] A.Pillay. J.Wang. Modified failure mode and effects analysis using approximate reasoning[J].Reliability Engineering&System Safety,2003,79:69-85.
[191] Zhaojun Yang, Binbin Xu, Fei Chen et al. A New Failure Mode and Effects Analysis Model ofCNC Machine Tool using Fuzzy Theory[J].2010IEEE International Conference onInformation and Automation, ICIA2010.
[192] Rao R Venkata, Gandhi O P. Failure cause analysis of machine tools using digraph and mat rixmethods[J].International Journal of Machine Tools&Manufacture,2002,42:521-528.
[193]李时.预测与决策技术[M].吉林科学技术出版社,2000.
[194]蒋仁言,左明建.可靠性模型及应用[M].北京:机械工业出版社,1999.
[195] Jaiwook Baik, D.N.P.Murthy et al. Two-dimensional Failure Modeling[C].13th Informs Appliedprobability conference July6-8,2005, Ottewa, Canada.
[196] J.Baik, D.N.P.Murthy et al.Two-Dimensional Failure Modeling with Minimal Repair[J]346-362.
[197] S.-C.Yang and J.A.Nachlas. Bivariate reliability and availability modeling. IEEE Trans Reliability50(2001):26-35.
[198] D.N.P.Murthy, B.P.Iskandar, and R.L.Wilson. Two-dimensional failure free warranties:Two-dimensional point process models. Oper Res43(1995):356-366.
[199] B.P.Iskandar and D.N.P.Murthy. Repair replace strategies for two-dimensional warranty. Proc FirstWestern Pacific Third Australia-Japan Workshop Stochastic Models in Engineering Technologyand Management, Christchurch,New Zealand,1999:206-213.
[200] B.P.Iskandar and D.N.P.Murthy et al. A new repair-replace strategy for items sold with atwo-dimensional warranty[J].Computers&Operations Research,2005(32):669-682.
[201] N.Jack and D.N.P.Murthy et al. Comments on “Maintenance policies with two-dimensionalwarranty”[J].Reliability Engineering and System Safety,2003(82):105-109.
[2]吴柏林.机遇前所未有，挑战更加严峻[J].机械工程师,2008(1):5-7.
[3]王先逵.我国数控机床的创新和发展[J].金属加工,2012(4):25-28.
[4]任国梁,石毅等.我国数控机床工业50年[C].全国机械装备先进制造技术高峰论坛，2009：458-466.
[5]胡亚波,吴玉文.我国数控机床的状况与发展[J].机床与液压,2004(7)：4-6.
[6]盛伯浩.我国数控机床现况与技术发展策略[J].数控技术,2006(2):17-21.
[7]李冬茹.我国数控机床技术发展与展望[J].世界制造技术与装备市场,2006(2):30-35.
[8]杨兆军,陈传海,陈菲,李国发.数控机床可靠性技术的研究进展[J].机械工程学报,2013:.http://www.cnki.net/kcms/detail/11.2187.TH.20130910.1138.026.html.
[9] Pease, William. An automatic machine tool[J].Scientific American,1952,187(3)：101-115.
[10]数控机床发展现状及发展趋势.《金属加工世界》2012(1).
[11]曹伟.我国数控机床发展的现状与对策[J].现代经济信息.
[12]林宋,张超英等.现代数控机床[M].北京:化学工业出版社,2011.
[13]张玉峰.浅谈我国数控机床的现状与发展趋势[J].金属加工,2010（21）:53-56.
[14]陈循介.中国机床工业六十年发展状况几思考[J].精密制造与自动化,2009(4):1-3.
[15]周延佑.数控机床发展新趋势与我国相应的对策[J].机械工艺师,2001(2):5-7.
[16]盛伯浩,唐华.数控机床技术发展浅析[J].航空制造技术,2002(6):12-17.
[17]刘小敏,王元生.我国数控机床的现状与发展[J].机械研究与应用,2006(4):22-25.
[18]朱同兴.国内外数控技术发展动向[J].数控技术与应用,2001(3):10-12.
[19]李宏胜.现代数控系统的技术特点与发展趋势[J].制造业自动化,2002(11):1-3.
[20]曹晋华,程侃.可靠性数学引论[M].北京:高等教育出版社,2006.
[21]居滋培.可靠性程[M].原子能出版社,2003:3,71.
[22]马运义等.可靠性技术的应用[M].北京:国防工业出版社,1996,4:28-32.
[23]可靠性设计大全[M].中国标准出版社,2008,2.
[24] Advisory Group on the Reliability of Electronic Equipment. AGREE, Reliability of militaryelectronic equipment[R].Washington DC:1957.
[25] 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.
[26] 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.
[27]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.
[28]张英芝,申桂香,吴甦,薛玉霞,何宇.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(2):378-381.
[29] W. Nelson. An Application of Graphical Analysis of Repair Data[J].Quality and ReliabilityEngineering International,1998,14:49-52.
[30]贾志成,杨兆军,赵宏安,张英芝.国产加工中心寿命分布及可靠性评估研究[J].2010International Conference on Broadcast Technology and Multimedia Communication,2010,4:391-394.
[31]罗巍.数控机床故障分析与可靠性评价技术的研究[D].长春:吉林大学,2011.
[32] O’connor PDT，Newton D， Bromley R. Practical reliability engineering.ed. England：Wiley；2002.
[33]许彬彬,杨兆军,陈菲等.非齐次泊松过程的数控机床可靠性建模[J].吉林大学学报(工学版),2011,41(2):210-214.
[34] Ebling CE. An introduction to reliability and maintainability engineering. New Delhi：TataMcGraw Hill Publishing Company Limited;1977.
[35] Li Xiaobing, Yang Zhaojun, Li Guofa, et al. Posterior probability prediction on the key subsystemof machining center [C].2011IEEE18th international conference on industrial engineering andengineering management:1123-1127.
[36] A.Z.Keller,A.R.R.Kamath, et al. Reliability analysis of CNC machine tools[J].ReliabilityEngineering,1982,3(6):449-473.
[37] Dai Yi，Zhou Yun-fei and Jia Ya-zhou. Distribution of time between failures of machining centerbased on type I censored data[J].Reliability Engineering and System Safety,2003，79(3):377-379．
[38] K. Das. A comparative study of exponential distribution vs Weibull distribution in machinereliability analysis in a CMS design[J].Computers&Industrial Engineering,2008,54:12-33
[39] Abernethy, R.B.The new Weibull Handbook[M].North Palm Beach, Fla.: R.B.Abernethy,2006.
[40] A.J. Watkins, D.J. Leech. Towards Automatic Assessment of Reliability for Data from a WeibullDistribution[J].Reliability Engineering&System Safety,1989,24(4):343-350.
[41] D.M. Brki. Interval estimation of the parameters β and η of the two-parameter Weibulldistribution[J].Microelectronics Reliability,1990,30(1):39-42.
[42] T.Bucar, M.Nagode. Reliability approximation using finite Weibull mixturedistributions[J].Reliability Engineering and System Safety,84(2004):241-251.
[43] Marko Nagode, Matija Fajdiga. An improved algorithm for parameter estimation suitable formixed Weibull distributions[J].International Journal of Fatigue,2000,22(1):75-80.
[44] M.T. Todinov. Is Weibull distribution the correct model for predicting probability of failureinitiated by non-interacting flaws?[J].International Journal of Solids and Structures,2009,46(3):887-901.
[45] Jean-Francois Castet, Joseph H.Saleh. Satellite and satellite subsystems reliability: Statistical dataanalysis[J].Reliability Engineering and System Safety,94(2009):1718-1728.
[46] Saralees Nadarajah. On the moments of the modified Weibull distribution[J].ReliabilityEngineering&System Safety,2005,90(1):114-117.
[47] Zhejun Gong. Estimation of mixed Weibull distribution parameters using the SCEM-UAalgorithm：Application and comparison with MLE in automotive reliability analysis[J]. ReliabilityEngineering&System Safety,2006,91(8):915-922.
[48] R.Jiang, D.N.P.Murthy. Reliability modeling involving two Weibull distributions[J]. ReliabilityEngineering and System Safety,47(1995):187-198.
[49] Satoru Matsushita, Kouichi Hagiwara et al. Lifetime data analysis of disease and aging by theweibull probability distribution [J].Journal of Clinical Epidemiology,1992,45(10):1165-1175.
[50] Hongzhu Qiao, Chris P.Tsokos. Estimation of the three parameter Weibull probabilitydistribution[J]. Mathematics and Computers in Simulation,1995,39(1):173-185.
[51]倪中新,费鹤良.威布尔分布无失效数据的统计分析[J].应用数学学报,2003,26(3)：533-543．
[52] E.F. Chace. Right-censored, grouped life test data analysis assuming a two-parameter Weibulldistribution function[J].Microelectronics Reliability,1976,15(5):497-499.
[53] S.Tanaka, M.Ichikawa. Approximate formula of coefficient of variation for Weibull distribution[J].Reliability Engineering,1983,4(3):141-143.
[54]盛骤,谢式千,潘承毅.概率论与数理统计[M].北京:高等教育出版社,2001.
[55] Shoji Kuchii, Naoto Kaio, Shunji Osaki. Simulation comparisons of point estimation methods inthe2-parameter weibull distribution[J].Microelectronics Reliability,1979,19(4):333-336.
[56] D.M. Brkich. One method for interval estimation of Weibull shape parameter[J]. MicroelectronicsReliability,1984,24(4):659-660.
[57] Nakagawa T. Periodic and sequential preventive maintenance policies[J].Journal of AppliedProbability1986;23,536-542.
[58] Wu S M, Derek C C. Preventive maintenance models with random maintenance quality[J].Reliability Engineering and System Safety2005;90,99-105.
[59] Zhou X J, Xi L F, Lee J.Reliability-centered predictive maintenance scheduling for a Continuouslymonitored system subject to degradation[J].Reliability Engineering and System Safety2007；92,530-534.
[60] T.S Sankar. A reliability estimate for machine tool spindles subjected to random forces[J].Mechanism and Machine Theory,1975,10(2):131-138.
[61] Lee, Seung-Woo, Han, Seung-Woo, Lee, Husang. Reliability evaluation of an oil cooler for ahigh-precision machining center[J].INTERNATIONAL JOURNAL OF PRECISIONENGINEERING AND MANUFACTURING,2007(3):50-53.
[62] Gisela Lanza, Stephan Niggeschmidt. Behavior of Dynamic Preventive Maintenance Optimizationfor Machine Tools[J].Reliability and Maintainability Symposiam,2009:315-320.
[63] Gisela Lanza, Stephan Niggeschmidt. Optimization of preventive maintenance and spare partprovision for machine tools based on variable operational conditions[J].CIRP Annals-Manufacturing Technology,2009(58):429-432.
[64] Fleischer.J, Niggeschmidt.S,Wawerla.M. Optimizing the Life-Cycle-Performance of machinetools by reliability and availability prognosis[J]. Advances in Life Cycle Engineering forSustainable Manufacturing.2007:329-334.
[65] Yang, M.; Lee, J.. Measurement and prediction of thermal errors of a CNC machine center usingtwo spherical balls[J].Journal of Materials Processing Technology,1998：180-189.
[66] Yang, S.; Yuan, J.; Ni, J. The Improvement of thermal error modeling and compensation onmachine tools by CMAC neural network [J].International Journal of Machine Tools andManufacture,36(4),1996.
[67] Fleischer, J. Increasing the reliability of machine tools by use of an FMEA-based conditionmonitoring system[J].VDI Berichte,2007:55-64.
[68]庞林花.我国数控机床市场分析[J].机电产品市场，2003:4-7.
[69]邬俊奇.数控机床可靠性时间模型与维修性指标评估[D].长春:吉林大学,2003.
[70]郝庆波.数控机床可靠性及维修性的模糊综合分配与预计[D].长春:吉林大学,2012.
[71] Hao Qingbo, Yang Zhaojun, Chen Fei, et al. A Fuzzy Maintainability Allocation Method for NCMachine Tools Based on Interval Analysis [C].Guiyang: The9th International Conference onReliability, Maintainability and Safety,889-896.
[72]韩爱国.数控车床维修性的分析与研究[D].长春:吉林大学,2004.
[73]王珊.面向寿命周期的加工中心可信性动态模型研究[D].长春:吉林大学,2009.
[74]杨兆军,郝庆波,陈菲,许彬彬,李小兵,赵宏伟.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)：321-328.
[75]樊少华.基于模糊理论的数控车床可靠性分配[D].长春:吉林大学,2011.
[76] Jia Yazhou, Shen Guixiang, Jia Zhixin. A reliability approach to machine tool bearings[J].Reliability engineering&system safety,1995,50(1):127-134.
[77] Jia Yazhou, Zheng Di. A note on the rating of gears based on power and torque characteristics ofmachine tools[J].International Journal of Machine Tools and Manufacture,1992,32(5):659-669.
[78] Jia Yazhou. Analysis and calculation of fatigue loading of machine tool gears[J].InternationalJournal of Fatigue,1991,13(6):483-487.
[79] Jia Yazhou, Shen Guixiang, Jia Zhixin, et al. Equivalent fatigue load in machine-tool probalisticreliability. Part II：Calculation methodology and practical applications [J]. International Journal ofFatigue,1993,15(6):479-484.
[80]郝庆波,杨兆军,陈传海,陈菲,李国发.基于区间层次分析法的数控机床可靠性预计[J].吉林大学学报(工学版),42(4):845-850.
[81]王义强，贾亚洲，于均一等.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.
[82] Wang Yiqiang, Jia Yazhou, Qia Jia, et al. Load spectra of CNC machine tools [J]. Quality andreliability engineering international,2000,16(3):229-234.
[83]邱成. CK7815数控车床可靠性分析与研究[D].长春:吉林工业大学,1999.
[84]张英芝.基于模糊与灰色理论的数控车床可靠性分析[D].长春:吉林大学,2005.
[85]于捷,贾亚洲.数控车床故障模式影响与致命性分析[J].哈尔滨工业大学学报,2005（12）:1725-1727.
[86]韩烨.基于FTA的数控冲床重要度分析[D].长春:吉林大学,2008.
[87] Y.M.Wang, K.S.Chin, Gary K.K.P. Risk evaluation in failure mode and effects analysis usingfuzzy weighted geometric mean[J].Expert Systems with Applications,2009,36:1195-1207.
[88]王桂萍.面向绿色的加工中心故障模式影响分析[J].吉林工程技术师范学院学报,2011(6)：56-59.
[89] Zhaojun Yang，Chuanhai Chen，Fei Chen，et al.Reliability Analysis of Machining Center Basedon the Field Data，Maintenance and Reliability,2013,15(2):147-155.
[90] Wang Yiqiang, Jia Yazhou, et al. Field failure database of CNC lathes [J].International Journal ofQuality and Reliability Management,1999,16(4):330-340.
[91]王继利,杨兆军,李国发,朱晓翠.基于改进EM算法的多重威布尔可靠性建模方法[J].吉林大学学报（工学版），2013.http://www.cnki.net/kcms/detail/22.1341.T.20130912.0445.007.html.
[92] Chuanhai Chen,Zhaojun Yang,Fei Chen，et al.The Study of Reliability Modeling Using locksimand Weibull++Software for Machining Center,AMM,2013,274(49)：49-52.
[93]许彬彬.基于维修程度的数控机床可靠性建模与分析[D].长春:吉林大学,2011.
[94]李小兵.加工中心时间动态可靠性建模[D].长春:吉林大学,2012.
[95]杨兆军,李小兵,许彬彬,陈菲,朱晓翠,郝庆波.加工中心时间动态可靠性建模[J].机械工程学报,2012,48(2)：16-22.
[96]许彬彬,杨兆军等.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.
[97]咸博.圆盘式刀库机械手自动换刀系统可靠性试验台研究[D].长春：吉林大学，2011.
[98]杨兆军等.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.
[99]张忠松.加工中心可靠性评价指标的扩展及评价方法研究[D].长春:吉林大学,2003.
[100]盛菲.数控系统软件可靠性研究及应用[D].长春:吉林大学,2004.
[101]杜庆林.数控系统可靠性评价体系[D].长春:吉林大学,2004.
[102]于捷,申桂香.数控机床可靠性评价方法的研究[J].机床与液压,2007(11)：174-176.
[103]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,2008,36:171-174.
[104]张学文,贾亚洲,江旭东.基于VB的数控机床可靠性评价系统[J].机械制造,2008,46(2):27-29.
[105]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.
[106]申桂香,张英芝等.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）,2009,38（5）:1208-1211.
[107]徐洪福.考虑故障危害的国产数控车床可靠性综合评价[D].长春:吉林大学,2011.
[108]陈炳琨.数控机床可靠性试验设计及评估方法研究[D].长春:吉林大学,2011.
[109] Yang Zhaojun, Zhu Xiaocui, Jia Yazhou, et al. Fuzzy-Comprehensive valuation of Use Reliabilityof CNC Machine Tools[C].Journal of Key Engineering Materials.2011,464:374-378.
[110] Xiaocui Zhu,Fei chen et al. Subsystems risk assessment of the CNC machine tools [C]. AppliedMechanics and Materials,2013,432:310-315.
[111] Xiaocui Zhu, Fei Chen, Xiaobing Li et al. Key subsystem identification of the CNC machinetools[C].Applied Mechanics and Materials,2013,329:157-162.
[112]李善章.刀库运行的可靠性[J].机床,1988(3):22-24.
[113]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.
[114]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/T23567.1-2009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.
[115]黄祖广,赵钦志,盛伯浩,张维.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.
[116]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.
[117]关贺.数控车床可靠性研究与应用[D].上海:上海交通大学,2007.
[118]王智明.数控机床的可靠性评估与不完全预防维修及其应用[D].上海:上海交通大学，2011.
[119]王智明,杨建国等.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.
[120]杨建国,王智明等.数控机床可靠性指标的似然比检验区间估计[J].机械工程学报，2012(1):9-22.
[121]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.
[122] Xie, M, Ho, S.L. Analysis of repairable system failure data using time series models[J]. Journal ofQuality in Maintenance Engineering,1999,5(1)：50-60.
[123]迟向磊.我国数控机床可靠性研究概况[J].机械工艺师,2001(3):22-23.
[124]王桂萍,贾亚洲,周广文.基于模糊可拓层次分析法的数控机床绿色度评价方法及应用[J].机械工程学报，2010.2，46(3):141-147.
[125]陈云雷.面向并行工程的数控车床可靠性研究[D].长春:吉林大学，2004.
[126]乔薇薇.数控系统可靠性建模及熵权模糊综合评价[D].长春:吉林大学，2008.
[127]申桂香，邵娜，张英芝等.基于可拓学理论的数控机床可靠性评价[J].吉林大学学报(工学版),2011,41(1):106-109.
[128]王桂萍,贾亚洲等.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.
[129]郑锐.基于可靠性分析的数控机床维修策略研究[D].长春:吉林大学，2011.
[130]张建华,王占林.基于模糊神经网络的故障诊断方法的研究[J].北京航空航天大学学报，1997(4):502-506.
[131]方敏,陈雁翔.基于模糊神经网络的机械故障诊断方法的研究[J].控制理论与应用，1998(3):460-464.
[132]王润孝,高利辉等.基于故障层次模型的集成神经网络诊断[J].控制与检测,2005(3)：44-46.
[133]余香梅,罗良玲.基于神经网络的数控机床可靠性预测的研究与实现[J].现代机械，2007(2):47-49.
[134]杨兆军,王继利,李国发,张新戈.冲压机床可靠性增长的模糊层次分析预测方法[J].吉林大学学报（工学版）2013.http://www.cnki.net/kcms/detail/22.1341.T.20130814.1756.006.html.
[135]薛丹.基于模糊神经网络评判方法的加工中心故障分析研究[D].长春:吉林大学，2005.
[136]贾志新,张宏斌等.基于径向基函数神经网络的电火花线切割机床可靠性数据模拟生成[J].机械工程学报,2010(2):145-149.
[137]贾志新,张宏斌,郗安民.利用神经网络扩充数控机床可靠性数据[J].吉林大学学报(工学版),2011,41(2):403-407.
[138] Y. Zhaojun, L. Wei, C. Yanhong. Study on increasing micro-drill reliability by vibrating drilling[J].Reliability Engineering&System Safety,1998,61(3):229–233.
[139]李雪.基于粗糙集模糊神经网络的微孔钻削在线监测研究[D].长春:吉林大学,2008.
[140]杨兆军,李雪等.微细钻孔的模糊神经网络在线监测[J].吉林大学学报（工学版），2007(6):1336-1340.
[141]苗夺谦,李道国.粗糙集理论、算法与应用[M].北京:清华大学出版社,2000.
[142]杨林娟,沈士明.基于粗糙集理论的故障树重要度分析[J].南京工业大学学报，2007(1):60-64.
[143]杨兆军,李雪等.基于粗糙集模糊控制的微孔钻削在线监测[J].光学精密工程，2008(7):1230-1235.
[144] ZhaoJun Yang, Xue Li et al. Development of a rough set-based fuzzy neural network for onlinemonitoring of micro-drilling[J].International Journal of Advanced Manufacturing Technology,(2009)41:219-225.
[145] Ashutosh Gupta, Bhaswati Mukherjee, S.K. Upadhyay. Weibull extension model: A Bayes studyusing Markov chain Monte Carlo simulation[J].Reliability Engineering&System Safety,2008,93(10):1434-1443.
[146]陈乐心.汽车零部件可靠性评估的小样本方法[D].南京:南京航空航天大学,2008.
[147] Al Omari Mohammed Ahmed, Noor Akma Ibrahim. Bayesian Estimator for Weibull Distributionwith Censored Data using Extension of Jeffrey Prior Information[J]. Procedia-Social andBehavioral Sciences,2010,(8):663-669.
[148]刘义,王国玉等.一种基于灰色距离测度的小样本数据区间估计方法[J].系统工程与电子技术,2008(1):116-119.
[149] A.A. Abdel-Wahid, A. Winter bottom. Approximate Bayesian estimates for the weibull reliabilityfunction and hazard rate from censored data[J].Journal of Statistical Planning and Inference,1987,(16):277-283.
[150] Ahmed A. Soliman, Ahmed H. Abd-Ellah, Naser A.Abou-Elheggag, Essam A. Ahmed. ModifiedWeibull model： A Bayes study using MCMC approach based on progressive censoringdata[J].Reliability Engineering&System Safety,2012,100:48-57.
[151] Ozge Doguc, Jose Emmanuel Ramirez-Marquez. A generic method for estimating systemreliability using Bayesian networks[J].Reliability Engineering&System Safety,2009,94(2):542-550.
[152] Sankaran Mahadevan, Ramesh Rebba. Validation of reliability computational models using Bayesnetworks[J].Reliability Engineering&System Safety,2005,87(2):223-232.
[153] H.Akman, L.Huwang. Bayes Computation for Reliability Estimation. IEEE Trans. onReliability,1997,46(1):52-55.
[154] S. Kumar, Ram C. Tiwari. Bayes estimation of the reliability of a system under a randomenvironment governed by a Dirichlet prior[J].Microelectronics Reliability,1989,29(2):255-265.
[155] Latife G rkemli, Selda Kapan Ulusoy. Fuzzy Bayesian reliability and availability analysis ofproduction systems[J].Computers and Industry Engineering,2010,59:690-696.
[156] Martin Shaw. Use of Bayes' theorem and the beta distribution for reliability estimation purposes[J].Reliability Engineering&System Safety,1991,31(2):145-153.
[157] Wu Hsien-chung. Fuzzy Bayesian system reliability assessment based on exponential distribution[J]. Applied Mathematical Modeling,2006,30(6):509-530.
[158]王燕萍，吕震宙.一种基于Gibbs抽样的可靠性增长Bayes方法[J].西北工业大学学报，2007,25(6):784-788．
[159] Geman Stuart，Geman Donald. Stochastic Relaxation，Gibbs distributions，and the Bayesianrestoration of images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，1984，PAMI-6(6):721-741．
[160]李洪双.小子样可靠性分析方法及应用研究[D].西安：西北工业大学,2006．
[161] Noren Niklas G，Orre Roland．Case based imprecision estimates for Bayes classifiers with theBayesian Bootstrap[J].Machine Learning,2005,58(1):79-84．
[162]刘晗.基于Bayes理论的小子样可靠性评定方法研究[D].国防科学技术大学,2006.
[163]刘贵昌.小子样条件下舰船可靠性评估方法及评估数据采集与分析[D].上海交通大学,2008.
[164]汤银才,侯道燕.三参数Weibull分布参数的Bayes估计[J].系统科学与数学,2009，29(1)：109-115.
[165]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.
[166]刘思峰,党耀国等.灰色系统理论及其应用[M].北京:科学出版社,2010.
[167]陈举华,于奎刚,史岩彬.基于灰色理论的机电系统全寿命小子样系统研究[J].机械设计,2005(9):7-9.
[168]张彦.基于灰色理论的汽车产品可靠性工程研究[D].合肥:合肥工业大学,2006.
[169]宋立涛.基于灰色理论的（重型）汽车系统可靠性研究[D].西安:西安电子科技大学，2011.
[170]江龙平,徐可君.叶片振动的灰色可靠性研究.汽轮机技术,2002,5:285-286.
[171]陈举华,秦惠芳.小子样机械系统故障分析与预测研究.农业机械学报,1999,6:31-35.
[172]张英芝.基于模糊与灰色理论的数控车床可靠性分析[D].长春:吉林大学,2005.
[173]张英芝,贾亚洲等.数控机床故障分布的灰关联分析法[J].农业机械学报,2004(6)：195-197.
[174]邓聚龙.灰色系统理论教程.武汉:华中理工大学出版社,1990.
[175]陈举华,郭毅之.GM模糊优化方法在机械系统故障预测中的应用.中国机械工程，2002,19:1658-1660.
[176]吕艳辉,潘成胜.基于AHP的灰色评估模型及其应用[J].火力与指挥控制,30(8),2005.
[177]侯丰伟,杨育等.基于灰色理论和模糊综合评价的设备评标方法研究[J].机械,37(3),2010.
[178]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.
[179]茆诗松,汤银才,王玲玲.可靠性统计[M].北京:高等教育出版社,2008.
[180]章国栋.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.
[181]张英芝,申桂香等.数控车床故障分布规律及可靠性[J].农业机械学报,2006(1)：156-159.
[182]李忠范,高文森.数理统计与随机过程[M].吉林:吉林大学出版社,2000.
[183]中华人民共和国国家标准,GB5080.4-85设备可靠性试验可靠性测定试验的点估计和区间估计方法（指数分布）.北京:国家标准局,1985.
[184]王秉刚.汽车可靠性工程[M].北京:机械工业出版设,1991.
[185]杨伟民.可靠性.维修性.保障性总论[M].北京:国防工业出版社,1995.
[186]甘茂治.维修性设计与验证[M].北京:国防工业出版社,1995.
[187] G. Carmignani. An integrated structural framework to cost-based FMECA：The priority-costFMECA. Reliability Engineering and System Safety,2009(4)：861-871, April2009.
[188] E.P. Zafiropoulos, E.N. Dialynas,“Reliability prediction and failure mode effects and criticalityanalysis of electronic devices using fuzzy logic,” International Journal of Quality&ReliabilityManagement,22(2):183-200,2005.
[189] M.Braglia, M.Frosolini, R.Montanri.Fuzzy TOPSIS approach for failure mode, effects andcriticality analysis[J].Quality and Reliability Engineering International，2003,19：425-443.
[190] A.Pillay. J.Wang. Modified failure mode and effects analysis using approximate reasoning[J].Reliability Engineering&System Safety,2003,79:69-85.
[191] Zhaojun Yang, Binbin Xu, Fei Chen et al. A New Failure Mode and Effects Analysis Model ofCNC Machine Tool using Fuzzy Theory[J].2010IEEE International Conference onInformation and Automation, ICIA2010.
[192] Rao R Venkata, Gandhi O P. Failure cause analysis of machine tools using digraph and mat rixmethods[J].International Journal of Machine Tools&Manufacture,2002,42:521-528.
[193]李时.预测与决策技术[M].吉林科学技术出版社,2000.
[194]蒋仁言,左明建.可靠性模型及应用[M].北京:机械工业出版社,1999.
[195] Jaiwook Baik, D.N.P.Murthy et al. Two-dimensional Failure Modeling[C].13th Informs Appliedprobability conference July6-8,2005, Ottewa, Canada.
[196] J.Baik, D.N.P.Murthy et al.Two-Dimensional Failure Modeling with Minimal Repair[J]346-362.
[197] S.-C.Yang and J.A.Nachlas. Bivariate reliability and availability modeling. IEEE Trans Reliability50(2001):26-35.
[198] D.N.P.Murthy, B.P.Iskandar, and R.L.Wilson. Two-dimensional failure free warranties:Two-dimensional point process models. Oper Res43(1995):356-366.
[199] B.P.Iskandar and D.N.P.Murthy. Repair replace strategies for two-dimensional warranty. Proc FirstWestern Pacific Third Australia-Japan Workshop Stochastic Models in Engineering Technologyand Management, Christchurch,New Zealand,1999:206-213.
[200] B.P.Iskandar and D.N.P.Murthy et al. A new repair-replace strategy for items sold with atwo-dimensional warranty[J].Computers&Operations Research,2005(32):669-682.
[201] N.Jack and D.N.P.Murthy et al. Comments on “Maintenance policies with two-dimensionalwarranty”[J].Reliability Engineering and System Safety,2003(82):105-109.