机械制造的工艺可靠性研究
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摘要
产品的可靠性是设计出来、制造出来、管理出来的。产品在设计阶段确定的可靠性要求需要通过制造过程予以实现和保障,如果在制造过程中不充分考虑各种因素对产品可靠性的影响并加以控制,加工完成后产品的可靠性指标往往达不到设计的要求。因此,在制造过程中保障产品的可靠性是一个必须解决的问题。论文针对制造过程中最常见的机械制造过程开展产品可靠性的保障研究。
在当前快速响应制造的需求背景下,制造企业面临研制时间短,技术上的不确定因素,技术改造的滞后,产品可靠性缺乏实践考验,管理缺乏经验等问题。深入研究和解决这些问题,必须要贯彻以可靠性工程为重点,积极开展工艺可靠性研究,强化技术基础、加强管理,实施技术与管理的有机结合,使工艺可靠性工作最终达到保障产品可靠性的目的。因此,论文以此需求为牵引,在总结相关研究的基础上提出了机械制造工艺可靠性的基本概念和分析方法,并建立相应的模型来指导对机械制造过程的有效控制,为机械制造过程保障产品可靠性的最终目标提供技术支持。
论文主要在以下几个方面开展了研究:
1)机械制造工艺可靠性的概念和评价指标
根据分析和控制机械制造过程保障产品可靠性能力的研究需求,论文在系统总结已有研究的基础上,提出了机械制造工艺可靠性的基本概念。为全面评价机械制造的工艺可靠性,提出了实用的评价指标体系,如工艺可靠度、工艺故障发生率、工艺故障平均维修时间、工艺稳定性、工艺自修正性能、工艺遗传性等,并给出了相应的计算方法和选择原则。
2)工艺可靠性建模
由于产品的可靠性指标是在孔位特征的加工过程中逐渐形成的,所以为了保障产品的可靠性,需要首先确定那些决定产品可靠性指标的关键孔位特征。论文通过Bayes方法整合多个专家的判断,来确定那些影响产品可靠性指标的关键孔位特征。在确定关键孔位特征之后,论文根据关键孔位特征加工过程之间的相互关系以及它们对工艺可靠性的作用,建立了工艺可靠性的各类模型。
3)工艺可靠性影响因素的分析和控制
由于关键孔位特征的加工过程往往决定了产品固有可靠性水平,因此论文将孔位特征的加工过程作为工艺可靠性的主要影响因素。只有对这些影响因素加强分析和控制才能使机械制造的工艺可靠性达到保障产品可靠性的要求。因此,论文首先提出了关键孔位特征加工过程影响因素的模糊评价方法,然后从产品孔位特征的测量数据分析入手,分别针对多个孔位特征的控制和多工序加工过程的单个孔位特征的控制需求,运用多元统计分析方法来实现分析和控制。为减少加工过程中工艺故障的发生,从而保证工艺可靠性的指标符合要求,论文在充分考虑工艺故障的损失和预防性维修费用的前提下,基于比例故障模型提出了预防性维修的决策方法,能够有效降低机械制造过程的运行费用并保证工艺故障发生率不超过要求的水平。
4)工艺可靠性的评定
论文将机械制造工艺可靠性的评定分为系统级和指标级。在系统级的工艺可靠性评定中,从保障产品可靠性的效果评价出发,通过试生产的产品可靠性指标与目标值之比来评定工艺可靠性,这就要求准确估计产品的可靠性。但是在产品制造完成交付用户之前,因为受时间或经费的限制,不允许投入大量产品进行可靠性试验或者试验时间很短没有失效数据,难以直接预计产品的可靠性。因此,论文以比较常见的寿命服从威布尔分布的产品为研究对象,提出了在产品可靠性小样本试验零失效的情况下,根据类似产品的可靠性数据和关键孔位特征数据的比较来评定产品可靠性,从而能够较准确地通过其可靠性指标与目标值之比来评定机械制造的工艺可靠性;在指标级的工艺可靠性指标评定中,论文提出结合试生产的数据来评定工艺可靠性指标的方法:基于Bayes融合的工艺故障发生率评定方法和验证工艺故障平均维修时间的序贯验后加权检验方法。论文所提方法与试生产相结合,在小样本条件下能够较准确地验证工艺可靠性指标,从而为及时改进相关工艺和设备提供理论与技术支持。
论文从实践需求出发,初步探讨了机械制造工艺可靠性的理论和方法,能够为设计和改进工艺路线、缩短开发周期、生产符合可靠性要求的产品提供有效参考。
Product reliability is produced by design, by manufacture and management. The target value of product reliability that was determined in product design stage must be implemented and ensured by manufacturing process, for if the impacts of various factors in manufacturing are underestimated and are out of control, reliability indices of finished products will more likely fall short of requirement. Therefore, ensuring product reliability in manufacturing process becomes an essential problem to be solved. This dissertation focuses its research on ensuring product reliability in mechanical manufacturing process which is most familiar in manufacuting domain.
In the context of fast response manufacturing, manufacturing enterprises have to deal with problems such as short developing time, uncertain technical factors, delay of technical reform, short of validation for product reliability, and insufficient management experiences. In order to solve these problems, we should emphasize on the application of reliability engineering, research on process reliability, enhance technical foundation and management, and combine techniques with management. Ensuring product reliability is the research target of process reliability. Therefore, motivated by the requirement, this dissertation summarizes related research, based on which, the concept of mechanical manufacturing process reliability and its analysis methods are proposed. Corresponding models are constructed to instruct effective control of mechanical manufacturing process. The purpose of this dissertation is to provide technical support for ensuring product reliability in mechanical manufacturing process.
This dissertation researches on following issues:
1) Concept of mechanical manufacturing process reliability and evaluating indices
According to the requirement of analysis and control of the capability to ensure product reliability for mechanical manufacturing process, this dissertation summarizes related researches and proposes the concept of mechanical manufacturing process reliability. To completely evaluate mechanical manufacturing process reliability, the dissertation also presents applicable evaluating indices, such as process reliability, frequency of process faults, mean time to repair process fault, process stability, process capability of self-correction, process transmissibility, etc. Corresponding calculating methods for these indices and their selecting principles are also proposed.
2) Modeling of mechanical manufacturing process reliability
As product reliability indices are gradually shaped during the manufacturing of holes and positions, to ensure product reliability, the key holes and positions, whose manufacturing determine the product reliability indices should be confirmed first. This dissertation synthesizes judgments of experts by means of Bayes theory, to identify the key holes and positions. After the key holes and positions are identified, this dissertation constructs models of process reliability, according to the relations between the machining processes of the key holes and positions and their impacts on process reliability.
3) Analysis and control of impacting factors for process reliability
As machining processes of key holes and positions usually determine the inherent reliability of product, this dissertation regards the machining processes as main impacting factors for process reliability. Hence, process reliability can ensure product reliability only by enhancing analysis and control of these impacting factors. Therefore, this dissertation first presents a fuzzy method to evaluat factors that affect key holes and positions. Then, based on measure data of product’s holes and positions, it applies multivariate analysis methods to implement analysis and control, according to the control requirements of multiple holes and positions and single hole or position in multiple processes, respectively. In order to reduce the chance of process fault occurrence and hence ensure that indices of process reliability meet requirement, the dissertation takes the loss of process faults and cost of preventive maintenance into accounts, presents a decision approach for preventive maintenance, based on proportional hazards model. The proposed approach can effectively reduce the cost of operation for mechanical manufacturing process and guarantee the frequency of process faults will not exceed requirement.
4) Process reliability evaluation
This dissertation classifies evaluation of mechanical manufacturing process reliability as system level and index level. In the system level of process reliability evaluation, from the poitview of evaluating process reliability’s performs on ensuring product reliability, it is implemented by compare the reliability index of manufactured products with target value, which requires precise estimating of product reliability. However, before the products are handed over to customers, the number of products that can be put into reliability test is small or zero failure is obtained when test time is short, due to limts of time or cost. It is hard to estimate product reliability in such situation. Therefore, this dissertation researches on product whose lifetime follows Weibull distribution, which is popular in practice. It presents the method to estimate product reliability by use of reliability data of similar product and comparison of key holes and positions data between the two kinds of products, in the case of zero failure result obtained in product reliability test with small sample size. It can precisely evaluate mechanical manufacturing process reliability by comparing product reliability index with target value. In the index level of process reliability evaluation, this dissertation proposes that process reliability index evaluation should be combined with data from trial manufacturing. It presents an evaluation approach for frequency of process faults based on Bayes fusion method and a sequential posterior odd test method to evaluate mean time to repair process faults. The proposed methods are incorporated with trial manufacturing and hence are capable to precisely evaluate process reliability indices and offer effective support for improving processes and equipments.
This dissertation, motivated by manufacturing requirements, explores theory and methods for mechanical manufacturing process reliability and obtains initial research results. It is grateful to provide references for designing and improving process route, shortening developing period and producing products that match the reliability requirements.
在当前快速响应制造的需求背景下,制造企业面临研制时间短,技术上的不确定因素,技术改造的滞后,产品可靠性缺乏实践考验,管理缺乏经验等问题。深入研究和解决这些问题,必须要贯彻以可靠性工程为重点,积极开展工艺可靠性研究,强化技术基础、加强管理,实施技术与管理的有机结合,使工艺可靠性工作最终达到保障产品可靠性的目的。因此,论文以此需求为牵引,在总结相关研究的基础上提出了机械制造工艺可靠性的基本概念和分析方法,并建立相应的模型来指导对机械制造过程的有效控制,为机械制造过程保障产品可靠性的最终目标提供技术支持。
论文主要在以下几个方面开展了研究:
1)机械制造工艺可靠性的概念和评价指标
根据分析和控制机械制造过程保障产品可靠性能力的研究需求,论文在系统总结已有研究的基础上,提出了机械制造工艺可靠性的基本概念。为全面评价机械制造的工艺可靠性,提出了实用的评价指标体系,如工艺可靠度、工艺故障发生率、工艺故障平均维修时间、工艺稳定性、工艺自修正性能、工艺遗传性等,并给出了相应的计算方法和选择原则。
2)工艺可靠性建模
由于产品的可靠性指标是在孔位特征的加工过程中逐渐形成的,所以为了保障产品的可靠性,需要首先确定那些决定产品可靠性指标的关键孔位特征。论文通过Bayes方法整合多个专家的判断,来确定那些影响产品可靠性指标的关键孔位特征。在确定关键孔位特征之后,论文根据关键孔位特征加工过程之间的相互关系以及它们对工艺可靠性的作用,建立了工艺可靠性的各类模型。
3)工艺可靠性影响因素的分析和控制
由于关键孔位特征的加工过程往往决定了产品固有可靠性水平,因此论文将孔位特征的加工过程作为工艺可靠性的主要影响因素。只有对这些影响因素加强分析和控制才能使机械制造的工艺可靠性达到保障产品可靠性的要求。因此,论文首先提出了关键孔位特征加工过程影响因素的模糊评价方法,然后从产品孔位特征的测量数据分析入手,分别针对多个孔位特征的控制和多工序加工过程的单个孔位特征的控制需求,运用多元统计分析方法来实现分析和控制。为减少加工过程中工艺故障的发生,从而保证工艺可靠性的指标符合要求,论文在充分考虑工艺故障的损失和预防性维修费用的前提下,基于比例故障模型提出了预防性维修的决策方法,能够有效降低机械制造过程的运行费用并保证工艺故障发生率不超过要求的水平。
4)工艺可靠性的评定
论文将机械制造工艺可靠性的评定分为系统级和指标级。在系统级的工艺可靠性评定中,从保障产品可靠性的效果评价出发,通过试生产的产品可靠性指标与目标值之比来评定工艺可靠性,这就要求准确估计产品的可靠性。但是在产品制造完成交付用户之前,因为受时间或经费的限制,不允许投入大量产品进行可靠性试验或者试验时间很短没有失效数据,难以直接预计产品的可靠性。因此,论文以比较常见的寿命服从威布尔分布的产品为研究对象,提出了在产品可靠性小样本试验零失效的情况下,根据类似产品的可靠性数据和关键孔位特征数据的比较来评定产品可靠性,从而能够较准确地通过其可靠性指标与目标值之比来评定机械制造的工艺可靠性;在指标级的工艺可靠性指标评定中,论文提出结合试生产的数据来评定工艺可靠性指标的方法:基于Bayes融合的工艺故障发生率评定方法和验证工艺故障平均维修时间的序贯验后加权检验方法。论文所提方法与试生产相结合,在小样本条件下能够较准确地验证工艺可靠性指标,从而为及时改进相关工艺和设备提供理论与技术支持。
论文从实践需求出发,初步探讨了机械制造工艺可靠性的理论和方法,能够为设计和改进工艺路线、缩短开发周期、生产符合可靠性要求的产品提供有效参考。
Product reliability is produced by design, by manufacture and management. The target value of product reliability that was determined in product design stage must be implemented and ensured by manufacturing process, for if the impacts of various factors in manufacturing are underestimated and are out of control, reliability indices of finished products will more likely fall short of requirement. Therefore, ensuring product reliability in manufacturing process becomes an essential problem to be solved. This dissertation focuses its research on ensuring product reliability in mechanical manufacturing process which is most familiar in manufacuting domain.
In the context of fast response manufacturing, manufacturing enterprises have to deal with problems such as short developing time, uncertain technical factors, delay of technical reform, short of validation for product reliability, and insufficient management experiences. In order to solve these problems, we should emphasize on the application of reliability engineering, research on process reliability, enhance technical foundation and management, and combine techniques with management. Ensuring product reliability is the research target of process reliability. Therefore, motivated by the requirement, this dissertation summarizes related research, based on which, the concept of mechanical manufacturing process reliability and its analysis methods are proposed. Corresponding models are constructed to instruct effective control of mechanical manufacturing process. The purpose of this dissertation is to provide technical support for ensuring product reliability in mechanical manufacturing process.
This dissertation researches on following issues:
1) Concept of mechanical manufacturing process reliability and evaluating indices
According to the requirement of analysis and control of the capability to ensure product reliability for mechanical manufacturing process, this dissertation summarizes related researches and proposes the concept of mechanical manufacturing process reliability. To completely evaluate mechanical manufacturing process reliability, the dissertation also presents applicable evaluating indices, such as process reliability, frequency of process faults, mean time to repair process fault, process stability, process capability of self-correction, process transmissibility, etc. Corresponding calculating methods for these indices and their selecting principles are also proposed.
2) Modeling of mechanical manufacturing process reliability
As product reliability indices are gradually shaped during the manufacturing of holes and positions, to ensure product reliability, the key holes and positions, whose manufacturing determine the product reliability indices should be confirmed first. This dissertation synthesizes judgments of experts by means of Bayes theory, to identify the key holes and positions. After the key holes and positions are identified, this dissertation constructs models of process reliability, according to the relations between the machining processes of the key holes and positions and their impacts on process reliability.
3) Analysis and control of impacting factors for process reliability
As machining processes of key holes and positions usually determine the inherent reliability of product, this dissertation regards the machining processes as main impacting factors for process reliability. Hence, process reliability can ensure product reliability only by enhancing analysis and control of these impacting factors. Therefore, this dissertation first presents a fuzzy method to evaluat factors that affect key holes and positions. Then, based on measure data of product’s holes and positions, it applies multivariate analysis methods to implement analysis and control, according to the control requirements of multiple holes and positions and single hole or position in multiple processes, respectively. In order to reduce the chance of process fault occurrence and hence ensure that indices of process reliability meet requirement, the dissertation takes the loss of process faults and cost of preventive maintenance into accounts, presents a decision approach for preventive maintenance, based on proportional hazards model. The proposed approach can effectively reduce the cost of operation for mechanical manufacturing process and guarantee the frequency of process faults will not exceed requirement.
4) Process reliability evaluation
This dissertation classifies evaluation of mechanical manufacturing process reliability as system level and index level. In the system level of process reliability evaluation, from the poitview of evaluating process reliability’s performs on ensuring product reliability, it is implemented by compare the reliability index of manufactured products with target value, which requires precise estimating of product reliability. However, before the products are handed over to customers, the number of products that can be put into reliability test is small or zero failure is obtained when test time is short, due to limts of time or cost. It is hard to estimate product reliability in such situation. Therefore, this dissertation researches on product whose lifetime follows Weibull distribution, which is popular in practice. It presents the method to estimate product reliability by use of reliability data of similar product and comparison of key holes and positions data between the two kinds of products, in the case of zero failure result obtained in product reliability test with small sample size. It can precisely evaluate mechanical manufacturing process reliability by comparing product reliability index with target value. In the index level of process reliability evaluation, this dissertation proposes that process reliability index evaluation should be combined with data from trial manufacturing. It presents an evaluation approach for frequency of process faults based on Bayes fusion method and a sequential posterior odd test method to evaluate mean time to repair process faults. The proposed methods are incorporated with trial manufacturing and hence are capable to precisely evaluate process reliability indices and offer effective support for improving processes and equipments.
This dissertation, motivated by manufacturing requirements, explores theory and methods for mechanical manufacturing process reliability and obtains initial research results. It is grateful to provide references for designing and improving process route, shortening developing period and producing products that match the reliability requirements.
引文
[1] J.S. Kim. Search for a new manufacturing paradigm: Executive Summary of the 1996 US Manufacturing Futures Survey. Boston: Research Report of the Boston University School of Management Manufacturing Roundtable, 1996.
[2] M. A. Karim, A. J. R. Smith, S. Halgamuge. Empirical relationships between some manufacturing practices and performance. International Journal of Production Research, 2008, 46 (13): 3583–3613.
[3] Josim U. Ahmed. Modern approaches to product reliability improvement. International Journal of Quality & Reliability Management, 1996, 13 (3): 27-41.
[4] Kanchan Kumar Das. Modeling Reliability Consideration in the Design and Analysis of Cellular Manufacturing Systems. University of Windsor, 2006.
[5] Milena Krasich, John Quigley, Lesley Walls. Modeling Reliability Growth in the Product Design Process. Annual Reliability and Maintainability Symposium(RAMS'04). 2004.424-430.
[6]朱文冰.航天产品电子元器件的可靠性控制.电子工程师, 2006, 32 (3): 11-13.
[7]蔡光起,原所先,高航.机械制造技术基础.沈阳:东北大学出版社, 2002.
[8] Yuan Lu, H.T.Loh, Y. Ibrahim, P. C. Sander. Reliability in a Time-Driven Product Development Process. Quality and Reliability Engineering International, 1999, 15: 427-430.
[9] Joseph Talavage, Roger G. Hannam. Flexible Manufacturing Systems in Practice. New York: Marcel Dekker, 1988.
[10]高利辉,王润孝,薛俊峰.先进制造系统的发展与故障诊断系统设计.制造技术与机床, 2003, 1: 30-33.
[11]可靠性维修性术语. 1990.
[12]刘衍平,李林.机械制造工艺过程的可靠性研究.机械设计, 2000, 8: 186-188.
[13]蒋平.柔性制造系统的工艺可靠性分析.长沙:国防科学技术大学, 2002.
[14]林国湘,彭如恕,刘迪荣.工艺方案的可靠性优化设计.组合机床与自动化加工技术, 2001, 3: 31-33.
[15]普罗尼科夫,机器可靠性.(译著);四川人民出版社:成都, 1983; 'Vol.' p.
[16] Serope Kalpakjian, Steven R. Schmid. Manufacturing Engineering and Technology (Fifth Edition). Pearson Education South Asia Pte Ltd, 2006.
[17]贾新章.工艺可靠性及其关键技术.半导体技术, 2000, 3 (25): 13-17.
[18]质量管理和质量保证标准. 1994.
[19] Marlin U. Thomas. Reliability And Warranties: Methods for Product Development and Quality Improvement. New York: Taylor & Francis, 2006.
[20] L.W. Condra. Reliability Improvement With Design of Experiments. NewYork: Marcel Dekker, 1993.
[21]Bruno Bosacchi. Fuzzy Logic Application to Quality & Reliability in Microelectronics. International Society for Optical Engineering, 1996.
[22] William Q. Meeker, Luis A. Escobar. Reliability: The Other Dimension of Quality. Quality Technnology & Quantitative Management, 2004, 1 (1): 1-25.
[23] Finn Jensen, Yield, quality and reliability—A natural correlation? Elsevier Applied Science: London, 1991: 739–750.
[24]赵天绪,段旭朝,郝跃.基于制造成品率模型的集成电路早期可靠性估计.电子学报, 2005, 33 (11): 1965-1968.
[25] Michael Pecht, Abhijit Dasgupta. Physics-of-failure: an approach to reliable product development. Journal of the Institute of Environmental Sciences, 1995, 38 (5): 30-34.
[26] Taeho Kim, Way Kuo. Modeling Manufacturing Yield and Reliability. IEEE Transactions on Semiconductor Manufacturing, 1999, 12 (4): 485-492.
[27] Way Kuo, Taeho Kim. An Overview of Manufacturing Yield and Reliability Modeling for Semiconductor Products. Proceedings of the IEEE. 1999.1329-1344.
[28] Manus Rungtusanatham. Beyond improved quality: the motivational effects of statistical process control. Journal of Operations Management, 2001, 19: 653–673.
[29]田学民,曹玉苹.统计过程控制的研究现状及展望.中国石油大学学报(自然科学版), 2008, 32 (4): 175-180.
[30] Connie M. Borror. The Certified Quality Engineer Handbook (Third Edition). Milwaukee: 2008.
[31]胡兴才,叶文华.小批量生产条件下的统计过程控制研究.机械研究与应用, 2006, 19 (1): 36-38.
[32]余忠华,吴昭同.面向多品种、小批量加工过程的统计质量控制应用策略与方法的探讨.系统工程理论与实践, 1999, 7: 128-131.
[33]杨倬.一元累积和控制图与多指标统计过程控制图研究.北京:北京工业大学, 2007.
[34]王海宇.过程质量控制的性能评价与改进方法研究.西安:西北工业大学, 2006.
[35]杨世元,吴德会,苏海涛.基于小批量制造过程的动态质量控制限及其简便计算方法.中国机械工程, 2006, 17 (14): 1476-1479.
[36]余忠华,吴昭同.面向小批量制造过程的质量控制方法研究.机械工程学报, 2001, 37 (8): 60-64.
[37]苗瑞,孙小明,李树刚,杨东.基于小批量生产的统计过程质量控制研究.计算机集成制造系统, 2005, 11 (11): 1633-1635.
[38] J.F. MacGregor, T. Kourtl. Statistical Process Control of Multivariate Processes. Control Engineering Practice, 1995, 3 (3): 403-414.
[39] Ian. T. Jolliffe. Principal Component Analysis. New York: Springer-Verlag, 2002.
[40]王惠文,吴载斌,孟洁.偏最小二乘回归的线性与非线性方法.北京:国防工业出版社, 2006.
[41] Leo H. Chiang, Lloyd F. Colegrove. Industrial implementation of on-line multivariate quality control. Chemometrics and Intelligent Laboratory Systems, 2007, 88: 143–153.
[42] Chien-Wei Wu, W.L. Pearn, Samuel Kotz. An overview of theory and practice on process capability indices for quality assurance. International Journal of Production Economics, 2009, 117: 338–359.
[43] Bharatwaj Ramakrishnan, Peter Sandborn, Michael Pecht. Process capability indices and product reliability. Microelectronics Reliability, 2001, 41 (1): 2067-2070.
[44] Y.C. Chang. Interval estimation of capability index Cpmk for manufacturing processes with asymmetric tolerances. Computers & Industrial Engineering, 2009, 56: 312–322.
[45] A. Parchami, M. Mashinchi. Fuzzy estimation for process capability indices. Information Sciences, 2007, 177: 1452–1462.
[46] K. S. Chen, M. L. Huang, R. K. Li. Process capability analysis for an entire product. International Journal of Production Research, 2001, 39 (17): 4077-4087.
[47] W.L. Pearn, Ming-Hung Shu. Manufacturing capability control for multiple power-distribution switch processes based on modified Cpk MPPAC. MicroelectronicsReliability, 2003, 43: 963-975.
[48] F. K. Wang, Norma F. Hubele, Frederick P. Lawrence, John D. Miskulin. Comparison of Three Multivariate Process Capability Indices. Journal of Quality Technology, 2000, 32 (3): 263-275.
[49] Qiang Huang, Shiyu Zhou, Jianjun Shi. Diagnosis of multi-operational machining processes through variation propagation analysis. Robotics and Computer Integrated Manufacturing, 2002, 18: 233–239.
[50] Shiyu Zhou, Qiang Huang, Jianjun Shi. State Space Modeling of Dimensional Variation Propagation in Multistage Machining Process Using Differential Motion Vectors. IEEE Transactions on Robotics and Automation, 2003, 19 (2): 296-309.
[51] Jianjun Shi, Shiyu Zhou. Quality control and improvement for multistage systems: A survey. IIE Transactions, 2009, 41: 744-753.
[52] S. Charles Liu, S. Jack Hu. Variation Simulation for Deformable Sheet Metal Assemblies Using Finite Element Methods. Journal of Manufacturing Science and Engineering, 1997, 119: 368-374.
[53] Jaime Camelio, S. Jack Hu, Dariusz Ceglarek. Modeling Variation Propagation of Multi-Station Assembly Systems With Compliant Parts. Journal of Mechanical Design, 2003, 125: 673-681.
[54] Yong Chen, Jionghua Jin. Quality-Reliability Chain Modeling for System-Reliability Analysis of Complex Manufacturing Processes. IEEE Transactions on Reliability, 2005, 54 (3): 475-488.
[55] J. Sun, L. Xi, E. Pan, S. Du. Integration of product quality and tool degradation for reliability modeling and analysis of multi-station manufacturing systems. International Journal of Computer Integrated Manufacturing, 2009, 22 (3): 267–279.
[56]孙继文,奚立峰,潘尔顺,杜世昌.面向产品尺寸质量的制造系统可靠性建模与分析.上海交通大学学报, 2008, 42 (7): 1100-1104.
[57] Angus Jeang, Chien-Ping Chung. Process mean, process tolerance, and use time determination for product life application under deteriorating process. International Journal of Advanced Manufacturing Technology, 2008, 36: 97-113.
[58] Biren Prasad. A model for optimizing performance based on reliability, life-cycle costs and other measurements. Production Planning and Control, 1999, 10 (3): 286– 300.
[59] Jingshan Li, Semyon M. Meerkov. Production variability in manufacturing systems: Bernoulli reliability case. Annals of Operations Research, 2000, 93: 299-324.
[60] Seiichi Nakajima. Introduction to Total Productive Maintenance(TPM). Cambridge: Productivity Press, 1988.
[61] Kathleen E. Mckone, Roger G. Schroeder, Kristy O. Cua. Total Productive Maintenance: a contextual view. Journal of Operations Management, 1999, 17: 123-144.
[62] Bo Tang Han, Cai Bo Zhang, Chang Sen Sun, Chun Jie Xu. Reliability Analysis of Flexible Manufacturing Cells Based on Triangular Fuzzy Number. Communications in Statistics—Theory and Methods, 2006, 35: 1897–1907.
[63] B. M. Hsu, M. H. shu. Fuzzy Testing and Selecting the Better Manufacturing Process Using Loss-based Capability Index. Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06). 2006.
[64] J. M. Bernardo, A. F. M. Smith. Bayesian Theory. Chichester: Wiley, 1994.
[65] Om Prakash Yadav, Nanua Singh, Parveen S. Goel, Rachel Itabashi-Campbell. A Framework for Reliability Prediction During Product Development ProcessIncorporating Engineering Judgments. Quality Engineering, 2003, 15 (4): 649-662.
[66] Lesley Walls, John Quigley, Jane Marshall. Modeling to Support Reliability Enhancement During Product Development With Applications in the U.K. Aerospace Industry. IEEE Transactions on Engineering Management, 2006, 53 (2): 263-274.
[67] Tetsurou Seki, Shin-ichiro Yokoyama. Robust Parameter-Estimation Using the Bootstrap Method for the 2-Parameter Weibull Distribution. IEEE Transactions on Reliability, 1996, 45 (1): 34-41.
[68] Linda Lee Ho, Aldy Fernandes Da Selva. Bias Correction for Mean Time to Failure and p-Quantiles in a Weibull Distribution by Bootstrap Procedure. Communications in Statistics—Simulation and Computation, 2005, 34: 617–629.
[69] X. Zwingmann, D. Ait-Kadi, A. Coulibaly, B. Mutel. Product reliability assessment using virtual samples. 2002 IEEE International Conference on Systems, Man and Cybernetics. 2002:624-629.
[70] D. Venkatesan, K. Kannan, R. Saravanan. A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing & Applications, 2009, 19: 135–140.
[71] M.I. Mazhar, S. Kara, H. Kaebernick. Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks. Journal of Operations Management, 2007, 25: 1184–1193.
[72]李勇,段正澄.基于支持向量机的机械加工误差预测与补偿模型的研究.机床与液压, 2007, 35 (1): 173-176.
[73] P. A. Tobias, D. C. Trindade. Applied Reliability. New York: Van Nostrand Reinhold, 1986.
[74] Shankara Prasad. Improving Manufacturing Reliability in IC Package Assembly Using the FMEA Technique. IEEE Transactions on Component, Hybrids, and Manufacturing Technology, 1991, 14 (3): 452-456.
[75] Randolph G. Phillips, Sameer Vittal. Product Reliability Validation and Continuous Improvement Program at GE Energy. Annual Reliability and Maintainability Symposium(RAMS'06). 2006:13-20.
[76] Carlos Adrino Rigo Teixeira, Katia Lucchesi Cavalca. Reliability as an Added-value Factor in an Automotive Clutch System. Quality and Reliability Engineering International, 2008, 24: 229-248.
[77]郭波,武小悦.系统可靠性分析.长沙:国防科技大学出版社, 2002.
[78] Patrick D.T. O'Connor, Practical Reliability Engineering (Fourth Edition). Publishing House of Electronics Industry: Beijing, 2005.
[79]孙进康,郦正能.可修复系统故障率分析.北京航空航天大学学报, 2001, 27 (5): 578-581.
[80]郑锐,张英芝,申桂香,何宇.一种新的可修系统故障率模型.吉林大学学报(工学版), 2009, 39 (2): 324-327.
[81]陆廷孝,郑鹏洲.可靠性设计与分析.北京:国防工业出版社, 1995.
[82]徐安,乔向明.基于更新理论的复杂设备故障率表达.吉林大学学报(工学版), 2006, 36 (3): 359-362.
[83]李云刚.基于移动平均法的改进.统计与决策, 2009, 19: 158-159.
[84]武小悦,刘琦.应用统计学.长沙:国防科技大学出版社, 2009.
[85] Wolfgang Hardle, Leopold Simar. Applied Multivariate Statistical Analysis (Second Edition). Berlin: Springer, 2007.
[86] F.T.S. Chan, H.C.W. Lau, R.W.L. Ip, H.K. Chan. Implementation of total productivemaintenance: A case study. International Journal of Production Economics, 2005, 95: 71-94.
[87] William Denson. The History of Reliability Prediction. IEEE Transactions on Reliability, 1998, 47 (3): 321-328.
[88] D.J. White. Decision Methodology. New York: John Wiley & Sons, 1975.
[89] Huaiqing Wang. Knowledge Level Analysis of Group Decision Support Systems. Expert Systems with Applications, 1997, 13 (2): 155-162.
[90] Chin-Chun Lo, Ping Wang, Kuo-Ming Chao. A fuzzy group-preferences analysis method for new-product development. Expert Systems with Applications, 2006, 31: 826-834.
[91] Xianfeng Fan, M.J Zuo. Fault diagnosis of machines based on D-S evidence theory, Part 1. D-S evidence theory and its improvement. Pattern Recognition Letters, 2006, 27: 366-376.
[92] Xianfeng Fan, M.J Zuo. Fault diagnosis of machines based on D-S evidence theory, Part 2. D-S evidence theory and its improvement. Pattern Recognition Letters, 2006, 27: 377-385.
[93]朱美娴,张志鸿,贺泽膏.《工程中的可靠性》系列标准译文集.北京:全国军事技术装备质量管理标准化委员会, 1999.
[94] Patricia L. Cooper, Gordon J. Savage. Manufacturing Systems Estimation Using Neural Network Models. World Scientific, 2001.
[95] M Perzyk, R Biernacki, J Kozlowski. Data mining in manufacturing: significance analysis of process parameters. Journal of Engineering Manufacturing, 2008, 222: 1503-1515.
[96] Dov Dvir, Arie Ben-David, Arik Sadeh, Aaron J. Shenhars. Critical managerial factors affecting defense projects success: A comparison between neural network and regression analysis. Engineering Applications of Artificial Intelligence, 2006, 19: 535-543.
[97] Genichi Taguchi, Subir Chowdhury, Shin Taguchi. Robust Engineering. NewYork: McGraw-Hill, 2000.
[98] R. Jeyapaul, P. Shahabudeen, K. Krishnaiah. Quality management research by considering multi-response problems in the Taguchi method-a review. International Journal of Advanced Manufacturing Technology, 2005, 26: 1331-1337.
[99] T. Gordon, H. Haywood. Initial experiments with the cross impact matrix method of forecasting. Futures, 1968, 1 (2): 100-116.
[100] G.H. Jeong, S.H. Kim. A qualitative cross-impact approach to find the key technology. Technological. Forecasting & Social Change, 1997, 55 (3): 203-214.
[101] Umut Asan, Cafer Erhan Bozdag, Seckin Polat. A fuzzy approach to qualitative cross impact analysis. Omega, 2004, 32: 443-458.
[102] Changwoo Choi, Seungkyum Kim, Yongtae Park. A patent-based cross impact analysis for quantitative estimation of technological impact: The case of information and communication technology. Technological Forecasting & Social Change, 2007, 74: 1296-1314.
[103] Tillmann Sachs, Robert L. K. Tiong. Quantifying Qualitative Information on Risks: Development of the QQIR Method. Journal of Construction Engineering and Management, 2009, (1): 56-71.
[104] T. Sachs, R. Tiong, D. Wagner. The Quantification and Financial Impact of Political Risk Perceptions on Infrastructure Projects in Asia. Journal of Structured Finance, 2008: 80-104.
[105] Laura A. Nabors, Matthew W. Reynolds, Mark D. Weist. Qualitative Evaluation of a High School Mental Health Program. Journal of Youth and Adolescence, 2000, 29 (1): 1-13.
[106] Kimlin T. Ashing-Giwa, Marjorie Kagawa-Singer, Geraline V. Padilia, Judith S. Tejero. The Impact of cervical Cancer and Dysplasia: A Qualitative, Multiethnic Study. Psychooncology, 2004, 13 (10): 709-728.
[107] H.A. Reijers, S. Liman Mansar. Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega, 2005, 33: 283-306.
[108] Sabine Garbarino, Jeremy Holland. Quantitative and Qualitative Methods in Impact Evaluation and Measuring Results, 2009.
[109] Y. Chen, R. Y. K. Fung, J. Tang. Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD. International Journal of Production Research, 2005, 43 (17): 3583-3604.
[110] Bi-Min Hsu, Ming-Hung Shu. Fuzzy inference to assess manufacturing process capability with imprecise data. European Journal of Operational Research, 2008, 186: 652-670.
[111] George J. Klir. Uncertainty and Information: Foundations of Generalized Information Theory. New York: Wiley, Hoboken, 2006.
[112] S. M. Chen, M. S. Yeh, P. Y. Hsiao. A comparison of similarity measures of fuzzy values. Fuzzy Sets and Systems, 1995, 72: 79-89.
[113] Kanliang Wang, Hui Min Liu. A fuzzy aggregation approach to group decision-making based on centroid measurement. Expert Systems, 2006, 23 (5): 313-322.
[114] C. H. Cheng. A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 1998, 95: 307-317.
[115]张发平,孙厚芳,袁光明,陈光明.工-夹系统刚度计算及变形加工误差模型.北京理工大学学报, 2004, 24 (12): 1041-1044.
[116]吴彤峰,向宇.磨削加工的动力学模型与误差修复.金刚石与磨料磨具工程, 1999, 112 (4): 23-26.
[117]周艳红,周济,周云飞,詹泳.五坐标数控加工的理论误差分析与控制.机械工程学报, 1999, 35 (10): 54-57.
[118]刘启东,徐春广.基于多体系统理论的车铣中心空间误差模型分析.组合机床与自动化加工技术, 2005, 5: 55-58.
[119] Yan-Kwang Chen, Kun-Lin Hsieh. Hotelling's T2 charts with variable sample size and control limit. European Journal of Operational Research, 2007, 182: 1251-1262.
[120] S.F. Yang, M.A. Rahim. Economic statistical process control for multivariate quality characteristics under Weibull shock model. International Journal of Production Economics, 2005, 98: 215-226.
[121]郑联语,唐晓青,汪叔淳.基于QFD的数控加工工艺质量优化规划方法.机械工程学报, 2001, 37 (8): 38-42.
[122] Baibing Li, Julian Morris, Elaine B. Martin. Model selection for partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 2002, (64): 79-89.
[123]任若恩,王惠文.多元统计数据分析——理论、方法、实例.北京:国防工业出版社, 1997.
[124]张润楚.多元统计分析.北京:科学出版社, 2006.
[125] Jian Liu, Jianjun Shi, S.Jack Hu. Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes. IIE Transactions, 2009, 41: 323-334.
[126] R.W. Bagshaw, S.T. Newman. Manufacturing data analysis of machine tool errors within a contemporary small manufacturing enterprise. International Journal of Machine Tools & Manufacture, 2002, 42: 1065-1080.
[127] Jianming Li, Theodor Freiheit, S. Jack Hu, Yoram Koren. A Quality Prediction Framework for Multistage Machining Processes Driven by an Engineering Model and Variation Propagation Model. Transactions of the ASME, Journal of Manufacturing Science and Engineering, 2007, 129: 1088-1100.
[128] H. Hotelling. Multivariate Quality Control—Techniques of Statistical Analysis. New York: MacGraw-Hill, 1947.
[129] S.H. Chen, C.C. Yang, W.T. Lin, T.M. Yeh. Performance evaluation for introducing statistical process control to the liquid crystal display industry. International Journal of Production Economics, 2008, 111: 80-92.
[130] Qiang Huang, Nairong Zhou, Jianjun Shi. Stream of Variation modeling and Diagnosis of Multi-Station Machining Processes. Proceedings of International Mechanical Engineering Congress & Exposition. 2000.1-8.
[131] G. Baffi, J. Morris, E. Martin. Non-Linear Model Based Predictive Control Through Dynamic Non-Linear Partial Lease Squares. Transaction Institution of Chemical Engineers, 2002, 80: 75-86.
[132] Xun Wang, Uwe Kruger, Barry Lennox. Recursive partial least squares algorithms for monitoring complex industrial processes. Control Engineering Practice, 2003, 11: 613-632.
[133] Di Xu. Mulitivariate Statistical Modeling and Robust Optimization in Quality Engineering. New Jersey: The State University of New Jersey, 2001.
[134] Daming Lin, Ming J. Zuo, Richard C.M. Yam. Sequential Imperfect Preventive Maintenance Models with Two Categories of Failure Modes. Naval Research Logistics, 2001, 48: 172-183.
[135] D. R. Cox. Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 1972, 34 (2): 187-220.
[136] P. J. Vlok, J. L. Coetzee, D. Banjevic, A. K. S. Jardine. Optimal Component Replacement Decisions Using Vibration Monitoring and the Proportional-Hazards Model. The Journal of the Operational Research Society, 2002, 53 (2): 193-202.
[137] A. Ghasemi, S. Yacout, M. S. Ouali. Optimal condition based maintenance with imperfect information and the proportional hazards model. International Journal of Production Research, 2007, 45 (4): 989-1012.
[138] Viliam Makis, Andrew K. S. Jardine. Optimal Replacement In The Proportional Hazards Model. Information Systems and Operational Research, 1992, 30 (1): 172-183.
[139] Wayne Nelson. Applied Life Data Analysis. New York: John Wiley & Sons, 1982.
[140] Wenzhen Yan, Jianxiong Chen, Ali T. Herfat. Designing a Reliability Test Plan Using Customer Usage and Bench Life Test Data. Annual Symposium Reliability and Maintainability. 2005.
[141] M. P. Kaminskiy, V. V. Krivtsov. A simple procedure for Bayesian estimation of the Weibull distribution. IEEE Transactions on Reliability, 2005, 54 (4): 612-616.
[142] Bryan Dodson. Weibull Analysis Handbook (Second Edition). Milwaukee: Quality Press, 2006.
[143] Waloddi Weibull. A Statistical distribution function of wide Applicability. Journal of Applied Mechanics, 1951, 18: 293-297.
[144] E.L. Welker, M. Lipow. Estimating the Exponential Failure Rate from Data with No Failure Events. Proceedings of the Annual Reliability and Maintainability Symposium, 1974: 420-427.
[145]王淑娟,刘丽平,翟国富,李远光.航天继电器“小子样、零失效"情况下的可靠性评估方法.机电元件, 2007, 27 (2): 3-6.
[146] Robert T. Bailey. Estimation from Zero-Failure Data. Risk Analysis, 1997, 17 (3): 375-380.
[147] Wendai Wang, David R. Langanke. Comparing Two Designs When the New Design Has Few or No Failures- Is the New Design Better Than Previous One? Proceedings of the Annual Reliability and Maintainability Symposium. 2001.322-325.
[148] K.W. Miller, L.J. Morell, R.E. Noonan, S.K. Park. Estimating the Probability of Failure When Testing Reveals No Failures. IEEE Transactions on Software Engineering, 1992, 18 (1): 33-43.
[149] L.M. Kaufman, B.W. Johnson, J.B. Dugan. Coverage Estimation Using Statistics of the Extremes for When Testing Reveals No Failures. IEEE Transactions on Computers, 2002, 51 (1): 3-12.
[150]韩明.无失效数据的可靠性分析.北京:中国统计出版社, 1999.
[151]冯静,刘琦,周经伦,沙基昌. Weibul1分布产品零失效下的可靠性增长研究.系统工程理论方法应用, 2004, 13 (1): 85-88.
[152]王玲玲,王炳兴.无失效数据的统计分析—修正似然函数方法.数理统计与应用概率, 1996, 11 (1): 64-70.
[153] Ayman Baklizi, S.E. Ahmed. On the estimation of reliability function in a Weibull lifetime distribution. Statistics, 2008, 42 (4): 351-362.
[154]王毓芳,肖诗唐.统计过程控制的策划与实施.北京:中国经济出版社, 2005.
[155] Housila Prasad Singh, Shrard Saxena, Harshada Joshi. A family of shrinkage estimators for Weibull shape parameter in censored sampling. Statistical Papers, 2008, 49: 513-529.
[156]周广文,贾亚洲.专用计算机数控机床故障率实验研究.组合机床与自动化加工技术, 2005, (1): 45-52.
[157]张黎,张波.电气设备故障率参数的一种最优估计算法.继电器, 2005, 33 (17): 31-34.
[158]李瑞莹,康锐.基于神经网络的故障率预测方法.航空学报, 2008, 29 (2): 357-363.
[159]杨婷,杨根科,潘常春.基于BP神经网络的汽车故障率预测.计算机仿真, 2009, 26 (1): 267-275.
[160]任震,张静伟,张晋昕.基于偏最小二乘法的设备故障率计算.电网技术, 2005, 29 (5): 12-15.
[161]周源泉,刘振德,陈宝延,马同玲.指数分布阶段可靠性增长模型的Bayes融合.质量与可靠性, 2006, 11 (2): 14-18.
[162] MIL-STD-471A. MAINTAINABILITY_VERIFICATION/DEMONSTRATION/ EVALUATION. 1973.
[163] GJB 2072-94.维修性试验与评定. 1994.
[164] E. L. Lehmann. Testing Statistical Hypotheses. New York: John Wiley, 1959.
[165] A. Wald. Sequential Analysis. New York: John Wiley, 1947.
[166] J. O. Berger. Statistical Decision Theory and Bayesian Analysis. New York: Springer-Verlag World Publishing Corporation, 1980.
[167] J. O. Berger, L. D. Brown, R. L. Wolpert. A unified conditional frequentist and Bayesian test for fixed and sequential simple hypothesis testing. Annual Statistics, 1994, 22: 1787-1807.
[168] H. F. Martz, R. A. Waller. Bayesian Reliability Analysis. New York: John Wiley & Sons, 1982.
[169] J. Haddock, T. R. Willemain. Sequential Bayesian Analysis of Simulation Experiments. Winter Simulation Conference. 1990.276-280.
[170] M. Karny, J. Kracik, I. Nagy, P. Nedoma. When has estimation reached a steady state? The Bayesian sequential test. International Journal of Adaptive Control and Signal Processing, 2004, 18: 1-18.
[171] T. Leonard, J. S. J. Hsu. Bayesian Methods. Cambridge University Press, 1999.
[172] M. A. Selby, M. M. Shoukri. Bayes comparison of 2 lognormal reliability functions. IEEE Transactions on Reliability, 1990, 39 (3): 336-341.
[173] O. Akman, L. Huwang. Bayes computation for reliability estimation. IEEE Transactions on Reliability, 1997, 46 (1): 52-55.
[174]张金槐,唐雪梅.贝叶斯方法.长沙:国防科技大学出版社, 1993.
[175]蔡洪,张士峰,张金槐. Bayes试验分析与评估.长沙:国防科技大学出版社, 2004.
[176]申绪涧,戚宗锋,汪连栋.正态分布未知参数的联合序贯验后加权检验方法.系统工程与电子技术, 2004, 26 (6): 744-746.
[177]王国玉,申绪涧,汪连栋.电子系统小子样试验理论方法.北京:国防工业出版社, 2003.
[178] N. I. Achieser. Theory of Approximation. New York: Dover publication, 1992.
[179] J. L. Devore. Probability and Statistics for Engineering and the Sciences. Thomson Learning, 2004.
[2] M. A. Karim, A. J. R. Smith, S. Halgamuge. Empirical relationships between some manufacturing practices and performance. International Journal of Production Research, 2008, 46 (13): 3583–3613.
[3] Josim U. Ahmed. Modern approaches to product reliability improvement. International Journal of Quality & Reliability Management, 1996, 13 (3): 27-41.
[4] Kanchan Kumar Das. Modeling Reliability Consideration in the Design and Analysis of Cellular Manufacturing Systems. University of Windsor, 2006.
[5] Milena Krasich, John Quigley, Lesley Walls. Modeling Reliability Growth in the Product Design Process. Annual Reliability and Maintainability Symposium(RAMS'04). 2004.424-430.
[6]朱文冰.航天产品电子元器件的可靠性控制.电子工程师, 2006, 32 (3): 11-13.
[7]蔡光起,原所先,高航.机械制造技术基础.沈阳:东北大学出版社, 2002.
[8] Yuan Lu, H.T.Loh, Y. Ibrahim, P. C. Sander. Reliability in a Time-Driven Product Development Process. Quality and Reliability Engineering International, 1999, 15: 427-430.
[9] Joseph Talavage, Roger G. Hannam. Flexible Manufacturing Systems in Practice. New York: Marcel Dekker, 1988.
[10]高利辉,王润孝,薛俊峰.先进制造系统的发展与故障诊断系统设计.制造技术与机床, 2003, 1: 30-33.
[11]可靠性维修性术语. 1990.
[12]刘衍平,李林.机械制造工艺过程的可靠性研究.机械设计, 2000, 8: 186-188.
[13]蒋平.柔性制造系统的工艺可靠性分析.长沙:国防科学技术大学, 2002.
[14]林国湘,彭如恕,刘迪荣.工艺方案的可靠性优化设计.组合机床与自动化加工技术, 2001, 3: 31-33.
[15]普罗尼科夫,机器可靠性.(译著);四川人民出版社:成都, 1983; 'Vol.' p.
[16] Serope Kalpakjian, Steven R. Schmid. Manufacturing Engineering and Technology (Fifth Edition). Pearson Education South Asia Pte Ltd, 2006.
[17]贾新章.工艺可靠性及其关键技术.半导体技术, 2000, 3 (25): 13-17.
[18]质量管理和质量保证标准. 1994.
[19] Marlin U. Thomas. Reliability And Warranties: Methods for Product Development and Quality Improvement. New York: Taylor & Francis, 2006.
[20] L.W. Condra. Reliability Improvement With Design of Experiments. NewYork: Marcel Dekker, 1993.
[21]Bruno Bosacchi. Fuzzy Logic Application to Quality & Reliability in Microelectronics. International Society for Optical Engineering, 1996.
[22] William Q. Meeker, Luis A. Escobar. Reliability: The Other Dimension of Quality. Quality Technnology & Quantitative Management, 2004, 1 (1): 1-25.
[23] Finn Jensen, Yield, quality and reliability—A natural correlation? Elsevier Applied Science: London, 1991: 739–750.
[24]赵天绪,段旭朝,郝跃.基于制造成品率模型的集成电路早期可靠性估计.电子学报, 2005, 33 (11): 1965-1968.
[25] Michael Pecht, Abhijit Dasgupta. Physics-of-failure: an approach to reliable product development. Journal of the Institute of Environmental Sciences, 1995, 38 (5): 30-34.
[26] Taeho Kim, Way Kuo. Modeling Manufacturing Yield and Reliability. IEEE Transactions on Semiconductor Manufacturing, 1999, 12 (4): 485-492.
[27] Way Kuo, Taeho Kim. An Overview of Manufacturing Yield and Reliability Modeling for Semiconductor Products. Proceedings of the IEEE. 1999.1329-1344.
[28] Manus Rungtusanatham. Beyond improved quality: the motivational effects of statistical process control. Journal of Operations Management, 2001, 19: 653–673.
[29]田学民,曹玉苹.统计过程控制的研究现状及展望.中国石油大学学报(自然科学版), 2008, 32 (4): 175-180.
[30] Connie M. Borror. The Certified Quality Engineer Handbook (Third Edition). Milwaukee: 2008.
[31]胡兴才,叶文华.小批量生产条件下的统计过程控制研究.机械研究与应用, 2006, 19 (1): 36-38.
[32]余忠华,吴昭同.面向多品种、小批量加工过程的统计质量控制应用策略与方法的探讨.系统工程理论与实践, 1999, 7: 128-131.
[33]杨倬.一元累积和控制图与多指标统计过程控制图研究.北京:北京工业大学, 2007.
[34]王海宇.过程质量控制的性能评价与改进方法研究.西安:西北工业大学, 2006.
[35]杨世元,吴德会,苏海涛.基于小批量制造过程的动态质量控制限及其简便计算方法.中国机械工程, 2006, 17 (14): 1476-1479.
[36]余忠华,吴昭同.面向小批量制造过程的质量控制方法研究.机械工程学报, 2001, 37 (8): 60-64.
[37]苗瑞,孙小明,李树刚,杨东.基于小批量生产的统计过程质量控制研究.计算机集成制造系统, 2005, 11 (11): 1633-1635.
[38] J.F. MacGregor, T. Kourtl. Statistical Process Control of Multivariate Processes. Control Engineering Practice, 1995, 3 (3): 403-414.
[39] Ian. T. Jolliffe. Principal Component Analysis. New York: Springer-Verlag, 2002.
[40]王惠文,吴载斌,孟洁.偏最小二乘回归的线性与非线性方法.北京:国防工业出版社, 2006.
[41] Leo H. Chiang, Lloyd F. Colegrove. Industrial implementation of on-line multivariate quality control. Chemometrics and Intelligent Laboratory Systems, 2007, 88: 143–153.
[42] Chien-Wei Wu, W.L. Pearn, Samuel Kotz. An overview of theory and practice on process capability indices for quality assurance. International Journal of Production Economics, 2009, 117: 338–359.
[43] Bharatwaj Ramakrishnan, Peter Sandborn, Michael Pecht. Process capability indices and product reliability. Microelectronics Reliability, 2001, 41 (1): 2067-2070.
[44] Y.C. Chang. Interval estimation of capability index Cpmk for manufacturing processes with asymmetric tolerances. Computers & Industrial Engineering, 2009, 56: 312–322.
[45] A. Parchami, M. Mashinchi. Fuzzy estimation for process capability indices. Information Sciences, 2007, 177: 1452–1462.
[46] K. S. Chen, M. L. Huang, R. K. Li. Process capability analysis for an entire product. International Journal of Production Research, 2001, 39 (17): 4077-4087.
[47] W.L. Pearn, Ming-Hung Shu. Manufacturing capability control for multiple power-distribution switch processes based on modified Cpk MPPAC. MicroelectronicsReliability, 2003, 43: 963-975.
[48] F. K. Wang, Norma F. Hubele, Frederick P. Lawrence, John D. Miskulin. Comparison of Three Multivariate Process Capability Indices. Journal of Quality Technology, 2000, 32 (3): 263-275.
[49] Qiang Huang, Shiyu Zhou, Jianjun Shi. Diagnosis of multi-operational machining processes through variation propagation analysis. Robotics and Computer Integrated Manufacturing, 2002, 18: 233–239.
[50] Shiyu Zhou, Qiang Huang, Jianjun Shi. State Space Modeling of Dimensional Variation Propagation in Multistage Machining Process Using Differential Motion Vectors. IEEE Transactions on Robotics and Automation, 2003, 19 (2): 296-309.
[51] Jianjun Shi, Shiyu Zhou. Quality control and improvement for multistage systems: A survey. IIE Transactions, 2009, 41: 744-753.
[52] S. Charles Liu, S. Jack Hu. Variation Simulation for Deformable Sheet Metal Assemblies Using Finite Element Methods. Journal of Manufacturing Science and Engineering, 1997, 119: 368-374.
[53] Jaime Camelio, S. Jack Hu, Dariusz Ceglarek. Modeling Variation Propagation of Multi-Station Assembly Systems With Compliant Parts. Journal of Mechanical Design, 2003, 125: 673-681.
[54] Yong Chen, Jionghua Jin. Quality-Reliability Chain Modeling for System-Reliability Analysis of Complex Manufacturing Processes. IEEE Transactions on Reliability, 2005, 54 (3): 475-488.
[55] J. Sun, L. Xi, E. Pan, S. Du. Integration of product quality and tool degradation for reliability modeling and analysis of multi-station manufacturing systems. International Journal of Computer Integrated Manufacturing, 2009, 22 (3): 267–279.
[56]孙继文,奚立峰,潘尔顺,杜世昌.面向产品尺寸质量的制造系统可靠性建模与分析.上海交通大学学报, 2008, 42 (7): 1100-1104.
[57] Angus Jeang, Chien-Ping Chung. Process mean, process tolerance, and use time determination for product life application under deteriorating process. International Journal of Advanced Manufacturing Technology, 2008, 36: 97-113.
[58] Biren Prasad. A model for optimizing performance based on reliability, life-cycle costs and other measurements. Production Planning and Control, 1999, 10 (3): 286– 300.
[59] Jingshan Li, Semyon M. Meerkov. Production variability in manufacturing systems: Bernoulli reliability case. Annals of Operations Research, 2000, 93: 299-324.
[60] Seiichi Nakajima. Introduction to Total Productive Maintenance(TPM). Cambridge: Productivity Press, 1988.
[61] Kathleen E. Mckone, Roger G. Schroeder, Kristy O. Cua. Total Productive Maintenance: a contextual view. Journal of Operations Management, 1999, 17: 123-144.
[62] Bo Tang Han, Cai Bo Zhang, Chang Sen Sun, Chun Jie Xu. Reliability Analysis of Flexible Manufacturing Cells Based on Triangular Fuzzy Number. Communications in Statistics—Theory and Methods, 2006, 35: 1897–1907.
[63] B. M. Hsu, M. H. shu. Fuzzy Testing and Selecting the Better Manufacturing Process Using Loss-based Capability Index. Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06). 2006.
[64] J. M. Bernardo, A. F. M. Smith. Bayesian Theory. Chichester: Wiley, 1994.
[65] Om Prakash Yadav, Nanua Singh, Parveen S. Goel, Rachel Itabashi-Campbell. A Framework for Reliability Prediction During Product Development ProcessIncorporating Engineering Judgments. Quality Engineering, 2003, 15 (4): 649-662.
[66] Lesley Walls, John Quigley, Jane Marshall. Modeling to Support Reliability Enhancement During Product Development With Applications in the U.K. Aerospace Industry. IEEE Transactions on Engineering Management, 2006, 53 (2): 263-274.
[67] Tetsurou Seki, Shin-ichiro Yokoyama. Robust Parameter-Estimation Using the Bootstrap Method for the 2-Parameter Weibull Distribution. IEEE Transactions on Reliability, 1996, 45 (1): 34-41.
[68] Linda Lee Ho, Aldy Fernandes Da Selva. Bias Correction for Mean Time to Failure and p-Quantiles in a Weibull Distribution by Bootstrap Procedure. Communications in Statistics—Simulation and Computation, 2005, 34: 617–629.
[69] X. Zwingmann, D. Ait-Kadi, A. Coulibaly, B. Mutel. Product reliability assessment using virtual samples. 2002 IEEE International Conference on Systems, Man and Cybernetics. 2002:624-629.
[70] D. Venkatesan, K. Kannan, R. Saravanan. A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Computing & Applications, 2009, 19: 135–140.
[71] M.I. Mazhar, S. Kara, H. Kaebernick. Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks. Journal of Operations Management, 2007, 25: 1184–1193.
[72]李勇,段正澄.基于支持向量机的机械加工误差预测与补偿模型的研究.机床与液压, 2007, 35 (1): 173-176.
[73] P. A. Tobias, D. C. Trindade. Applied Reliability. New York: Van Nostrand Reinhold, 1986.
[74] Shankara Prasad. Improving Manufacturing Reliability in IC Package Assembly Using the FMEA Technique. IEEE Transactions on Component, Hybrids, and Manufacturing Technology, 1991, 14 (3): 452-456.
[75] Randolph G. Phillips, Sameer Vittal. Product Reliability Validation and Continuous Improvement Program at GE Energy. Annual Reliability and Maintainability Symposium(RAMS'06). 2006:13-20.
[76] Carlos Adrino Rigo Teixeira, Katia Lucchesi Cavalca. Reliability as an Added-value Factor in an Automotive Clutch System. Quality and Reliability Engineering International, 2008, 24: 229-248.
[77]郭波,武小悦.系统可靠性分析.长沙:国防科技大学出版社, 2002.
[78] Patrick D.T. O'Connor, Practical Reliability Engineering (Fourth Edition). Publishing House of Electronics Industry: Beijing, 2005.
[79]孙进康,郦正能.可修复系统故障率分析.北京航空航天大学学报, 2001, 27 (5): 578-581.
[80]郑锐,张英芝,申桂香,何宇.一种新的可修系统故障率模型.吉林大学学报(工学版), 2009, 39 (2): 324-327.
[81]陆廷孝,郑鹏洲.可靠性设计与分析.北京:国防工业出版社, 1995.
[82]徐安,乔向明.基于更新理论的复杂设备故障率表达.吉林大学学报(工学版), 2006, 36 (3): 359-362.
[83]李云刚.基于移动平均法的改进.统计与决策, 2009, 19: 158-159.
[84]武小悦,刘琦.应用统计学.长沙:国防科技大学出版社, 2009.
[85] Wolfgang Hardle, Leopold Simar. Applied Multivariate Statistical Analysis (Second Edition). Berlin: Springer, 2007.
[86] F.T.S. Chan, H.C.W. Lau, R.W.L. Ip, H.K. Chan. Implementation of total productivemaintenance: A case study. International Journal of Production Economics, 2005, 95: 71-94.
[87] William Denson. The History of Reliability Prediction. IEEE Transactions on Reliability, 1998, 47 (3): 321-328.
[88] D.J. White. Decision Methodology. New York: John Wiley & Sons, 1975.
[89] Huaiqing Wang. Knowledge Level Analysis of Group Decision Support Systems. Expert Systems with Applications, 1997, 13 (2): 155-162.
[90] Chin-Chun Lo, Ping Wang, Kuo-Ming Chao. A fuzzy group-preferences analysis method for new-product development. Expert Systems with Applications, 2006, 31: 826-834.
[91] Xianfeng Fan, M.J Zuo. Fault diagnosis of machines based on D-S evidence theory, Part 1. D-S evidence theory and its improvement. Pattern Recognition Letters, 2006, 27: 366-376.
[92] Xianfeng Fan, M.J Zuo. Fault diagnosis of machines based on D-S evidence theory, Part 2. D-S evidence theory and its improvement. Pattern Recognition Letters, 2006, 27: 377-385.
[93]朱美娴,张志鸿,贺泽膏.《工程中的可靠性》系列标准译文集.北京:全国军事技术装备质量管理标准化委员会, 1999.
[94] Patricia L. Cooper, Gordon J. Savage. Manufacturing Systems Estimation Using Neural Network Models. World Scientific, 2001.
[95] M Perzyk, R Biernacki, J Kozlowski. Data mining in manufacturing: significance analysis of process parameters. Journal of Engineering Manufacturing, 2008, 222: 1503-1515.
[96] Dov Dvir, Arie Ben-David, Arik Sadeh, Aaron J. Shenhars. Critical managerial factors affecting defense projects success: A comparison between neural network and regression analysis. Engineering Applications of Artificial Intelligence, 2006, 19: 535-543.
[97] Genichi Taguchi, Subir Chowdhury, Shin Taguchi. Robust Engineering. NewYork: McGraw-Hill, 2000.
[98] R. Jeyapaul, P. Shahabudeen, K. Krishnaiah. Quality management research by considering multi-response problems in the Taguchi method-a review. International Journal of Advanced Manufacturing Technology, 2005, 26: 1331-1337.
[99] T. Gordon, H. Haywood. Initial experiments with the cross impact matrix method of forecasting. Futures, 1968, 1 (2): 100-116.
[100] G.H. Jeong, S.H. Kim. A qualitative cross-impact approach to find the key technology. Technological. Forecasting & Social Change, 1997, 55 (3): 203-214.
[101] Umut Asan, Cafer Erhan Bozdag, Seckin Polat. A fuzzy approach to qualitative cross impact analysis. Omega, 2004, 32: 443-458.
[102] Changwoo Choi, Seungkyum Kim, Yongtae Park. A patent-based cross impact analysis for quantitative estimation of technological impact: The case of information and communication technology. Technological Forecasting & Social Change, 2007, 74: 1296-1314.
[103] Tillmann Sachs, Robert L. K. Tiong. Quantifying Qualitative Information on Risks: Development of the QQIR Method. Journal of Construction Engineering and Management, 2009, (1): 56-71.
[104] T. Sachs, R. Tiong, D. Wagner. The Quantification and Financial Impact of Political Risk Perceptions on Infrastructure Projects in Asia. Journal of Structured Finance, 2008: 80-104.
[105] Laura A. Nabors, Matthew W. Reynolds, Mark D. Weist. Qualitative Evaluation of a High School Mental Health Program. Journal of Youth and Adolescence, 2000, 29 (1): 1-13.
[106] Kimlin T. Ashing-Giwa, Marjorie Kagawa-Singer, Geraline V. Padilia, Judith S. Tejero. The Impact of cervical Cancer and Dysplasia: A Qualitative, Multiethnic Study. Psychooncology, 2004, 13 (10): 709-728.
[107] H.A. Reijers, S. Liman Mansar. Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega, 2005, 33: 283-306.
[108] Sabine Garbarino, Jeremy Holland. Quantitative and Qualitative Methods in Impact Evaluation and Measuring Results, 2009.
[109] Y. Chen, R. Y. K. Fung, J. Tang. Fuzzy expected value modelling approach for determining target values of engineering characteristics in QFD. International Journal of Production Research, 2005, 43 (17): 3583-3604.
[110] Bi-Min Hsu, Ming-Hung Shu. Fuzzy inference to assess manufacturing process capability with imprecise data. European Journal of Operational Research, 2008, 186: 652-670.
[111] George J. Klir. Uncertainty and Information: Foundations of Generalized Information Theory. New York: Wiley, Hoboken, 2006.
[112] S. M. Chen, M. S. Yeh, P. Y. Hsiao. A comparison of similarity measures of fuzzy values. Fuzzy Sets and Systems, 1995, 72: 79-89.
[113] Kanliang Wang, Hui Min Liu. A fuzzy aggregation approach to group decision-making based on centroid measurement. Expert Systems, 2006, 23 (5): 313-322.
[114] C. H. Cheng. A new approach for ranking fuzzy numbers by distance method. Fuzzy Sets and Systems, 1998, 95: 307-317.
[115]张发平,孙厚芳,袁光明,陈光明.工-夹系统刚度计算及变形加工误差模型.北京理工大学学报, 2004, 24 (12): 1041-1044.
[116]吴彤峰,向宇.磨削加工的动力学模型与误差修复.金刚石与磨料磨具工程, 1999, 112 (4): 23-26.
[117]周艳红,周济,周云飞,詹泳.五坐标数控加工的理论误差分析与控制.机械工程学报, 1999, 35 (10): 54-57.
[118]刘启东,徐春广.基于多体系统理论的车铣中心空间误差模型分析.组合机床与自动化加工技术, 2005, 5: 55-58.
[119] Yan-Kwang Chen, Kun-Lin Hsieh. Hotelling's T2 charts with variable sample size and control limit. European Journal of Operational Research, 2007, 182: 1251-1262.
[120] S.F. Yang, M.A. Rahim. Economic statistical process control for multivariate quality characteristics under Weibull shock model. International Journal of Production Economics, 2005, 98: 215-226.
[121]郑联语,唐晓青,汪叔淳.基于QFD的数控加工工艺质量优化规划方法.机械工程学报, 2001, 37 (8): 38-42.
[122] Baibing Li, Julian Morris, Elaine B. Martin. Model selection for partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 2002, (64): 79-89.
[123]任若恩,王惠文.多元统计数据分析——理论、方法、实例.北京:国防工业出版社, 1997.
[124]张润楚.多元统计分析.北京:科学出版社, 2006.
[125] Jian Liu, Jianjun Shi, S.Jack Hu. Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes. IIE Transactions, 2009, 41: 323-334.
[126] R.W. Bagshaw, S.T. Newman. Manufacturing data analysis of machine tool errors within a contemporary small manufacturing enterprise. International Journal of Machine Tools & Manufacture, 2002, 42: 1065-1080.
[127] Jianming Li, Theodor Freiheit, S. Jack Hu, Yoram Koren. A Quality Prediction Framework for Multistage Machining Processes Driven by an Engineering Model and Variation Propagation Model. Transactions of the ASME, Journal of Manufacturing Science and Engineering, 2007, 129: 1088-1100.
[128] H. Hotelling. Multivariate Quality Control—Techniques of Statistical Analysis. New York: MacGraw-Hill, 1947.
[129] S.H. Chen, C.C. Yang, W.T. Lin, T.M. Yeh. Performance evaluation for introducing statistical process control to the liquid crystal display industry. International Journal of Production Economics, 2008, 111: 80-92.
[130] Qiang Huang, Nairong Zhou, Jianjun Shi. Stream of Variation modeling and Diagnosis of Multi-Station Machining Processes. Proceedings of International Mechanical Engineering Congress & Exposition. 2000.1-8.
[131] G. Baffi, J. Morris, E. Martin. Non-Linear Model Based Predictive Control Through Dynamic Non-Linear Partial Lease Squares. Transaction Institution of Chemical Engineers, 2002, 80: 75-86.
[132] Xun Wang, Uwe Kruger, Barry Lennox. Recursive partial least squares algorithms for monitoring complex industrial processes. Control Engineering Practice, 2003, 11: 613-632.
[133] Di Xu. Mulitivariate Statistical Modeling and Robust Optimization in Quality Engineering. New Jersey: The State University of New Jersey, 2001.
[134] Daming Lin, Ming J. Zuo, Richard C.M. Yam. Sequential Imperfect Preventive Maintenance Models with Two Categories of Failure Modes. Naval Research Logistics, 2001, 48: 172-183.
[135] D. R. Cox. Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 1972, 34 (2): 187-220.
[136] P. J. Vlok, J. L. Coetzee, D. Banjevic, A. K. S. Jardine. Optimal Component Replacement Decisions Using Vibration Monitoring and the Proportional-Hazards Model. The Journal of the Operational Research Society, 2002, 53 (2): 193-202.
[137] A. Ghasemi, S. Yacout, M. S. Ouali. Optimal condition based maintenance with imperfect information and the proportional hazards model. International Journal of Production Research, 2007, 45 (4): 989-1012.
[138] Viliam Makis, Andrew K. S. Jardine. Optimal Replacement In The Proportional Hazards Model. Information Systems and Operational Research, 1992, 30 (1): 172-183.
[139] Wayne Nelson. Applied Life Data Analysis. New York: John Wiley & Sons, 1982.
[140] Wenzhen Yan, Jianxiong Chen, Ali T. Herfat. Designing a Reliability Test Plan Using Customer Usage and Bench Life Test Data. Annual Symposium Reliability and Maintainability. 2005.
[141] M. P. Kaminskiy, V. V. Krivtsov. A simple procedure for Bayesian estimation of the Weibull distribution. IEEE Transactions on Reliability, 2005, 54 (4): 612-616.
[142] Bryan Dodson. Weibull Analysis Handbook (Second Edition). Milwaukee: Quality Press, 2006.
[143] Waloddi Weibull. A Statistical distribution function of wide Applicability. Journal of Applied Mechanics, 1951, 18: 293-297.
[144] E.L. Welker, M. Lipow. Estimating the Exponential Failure Rate from Data with No Failure Events. Proceedings of the Annual Reliability and Maintainability Symposium, 1974: 420-427.
[145]王淑娟,刘丽平,翟国富,李远光.航天继电器“小子样、零失效"情况下的可靠性评估方法.机电元件, 2007, 27 (2): 3-6.
[146] Robert T. Bailey. Estimation from Zero-Failure Data. Risk Analysis, 1997, 17 (3): 375-380.
[147] Wendai Wang, David R. Langanke. Comparing Two Designs When the New Design Has Few or No Failures- Is the New Design Better Than Previous One? Proceedings of the Annual Reliability and Maintainability Symposium. 2001.322-325.
[148] K.W. Miller, L.J. Morell, R.E. Noonan, S.K. Park. Estimating the Probability of Failure When Testing Reveals No Failures. IEEE Transactions on Software Engineering, 1992, 18 (1): 33-43.
[149] L.M. Kaufman, B.W. Johnson, J.B. Dugan. Coverage Estimation Using Statistics of the Extremes for When Testing Reveals No Failures. IEEE Transactions on Computers, 2002, 51 (1): 3-12.
[150]韩明.无失效数据的可靠性分析.北京:中国统计出版社, 1999.
[151]冯静,刘琦,周经伦,沙基昌. Weibul1分布产品零失效下的可靠性增长研究.系统工程理论方法应用, 2004, 13 (1): 85-88.
[152]王玲玲,王炳兴.无失效数据的统计分析—修正似然函数方法.数理统计与应用概率, 1996, 11 (1): 64-70.
[153] Ayman Baklizi, S.E. Ahmed. On the estimation of reliability function in a Weibull lifetime distribution. Statistics, 2008, 42 (4): 351-362.
[154]王毓芳,肖诗唐.统计过程控制的策划与实施.北京:中国经济出版社, 2005.
[155] Housila Prasad Singh, Shrard Saxena, Harshada Joshi. A family of shrinkage estimators for Weibull shape parameter in censored sampling. Statistical Papers, 2008, 49: 513-529.
[156]周广文,贾亚洲.专用计算机数控机床故障率实验研究.组合机床与自动化加工技术, 2005, (1): 45-52.
[157]张黎,张波.电气设备故障率参数的一种最优估计算法.继电器, 2005, 33 (17): 31-34.
[158]李瑞莹,康锐.基于神经网络的故障率预测方法.航空学报, 2008, 29 (2): 357-363.
[159]杨婷,杨根科,潘常春.基于BP神经网络的汽车故障率预测.计算机仿真, 2009, 26 (1): 267-275.
[160]任震,张静伟,张晋昕.基于偏最小二乘法的设备故障率计算.电网技术, 2005, 29 (5): 12-15.
[161]周源泉,刘振德,陈宝延,马同玲.指数分布阶段可靠性增长模型的Bayes融合.质量与可靠性, 2006, 11 (2): 14-18.
[162] MIL-STD-471A. MAINTAINABILITY_VERIFICATION/DEMONSTRATION/ EVALUATION. 1973.
[163] GJB 2072-94.维修性试验与评定. 1994.
[164] E. L. Lehmann. Testing Statistical Hypotheses. New York: John Wiley, 1959.
[165] A. Wald. Sequential Analysis. New York: John Wiley, 1947.
[166] J. O. Berger. Statistical Decision Theory and Bayesian Analysis. New York: Springer-Verlag World Publishing Corporation, 1980.
[167] J. O. Berger, L. D. Brown, R. L. Wolpert. A unified conditional frequentist and Bayesian test for fixed and sequential simple hypothesis testing. Annual Statistics, 1994, 22: 1787-1807.
[168] H. F. Martz, R. A. Waller. Bayesian Reliability Analysis. New York: John Wiley & Sons, 1982.
[169] J. Haddock, T. R. Willemain. Sequential Bayesian Analysis of Simulation Experiments. Winter Simulation Conference. 1990.276-280.
[170] M. Karny, J. Kracik, I. Nagy, P. Nedoma. When has estimation reached a steady state? The Bayesian sequential test. International Journal of Adaptive Control and Signal Processing, 2004, 18: 1-18.
[171] T. Leonard, J. S. J. Hsu. Bayesian Methods. Cambridge University Press, 1999.
[172] M. A. Selby, M. M. Shoukri. Bayes comparison of 2 lognormal reliability functions. IEEE Transactions on Reliability, 1990, 39 (3): 336-341.
[173] O. Akman, L. Huwang. Bayes computation for reliability estimation. IEEE Transactions on Reliability, 1997, 46 (1): 52-55.
[174]张金槐,唐雪梅.贝叶斯方法.长沙:国防科技大学出版社, 1993.
[175]蔡洪,张士峰,张金槐. Bayes试验分析与评估.长沙:国防科技大学出版社, 2004.
[176]申绪涧,戚宗锋,汪连栋.正态分布未知参数的联合序贯验后加权检验方法.系统工程与电子技术, 2004, 26 (6): 744-746.
[177]王国玉,申绪涧,汪连栋.电子系统小子样试验理论方法.北京:国防工业出版社, 2003.
[178] N. I. Achieser. Theory of Approximation. New York: Dover publication, 1992.
[179] J. L. Devore. Probability and Statistics for Engineering and the Sciences. Thomson Learning, 2004.