动态分布参数的Bayes可靠性综合试验与评估方法研究
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摘要
可靠性试验鉴定与评估是武器装备研制、定型、采办和使用过程中的重要环节。随着现代武器装备的技术含量和复杂程度不断提高,复杂装备的造价和试验费用昂贵,具有现场试验次数少、各阶段试验具有继承性但试验条件不尽相同等特点。因此,对于“小子样、多阶段、异总体”装备可靠性试验与评估问题,采用传统的统计分析方法难以给出科学合理的结论。
论文针对装备研制阶段可靠性试验与评估的工程需求,将变动统计理论和Bayes方法引入到装备研制阶段可靠性试验与分析中,从新的视角对动态分布参数的Bayes多源信息融合、可靠性增长规划、可靠性鉴定方案的优化选择和可靠性试验评估等一系列难题进行了系统深入的研究,建立了一套适用于动态分布参数的Bayes可靠性试验鉴定与评估的理论和方法,为现代武器装备研制阶段可靠性保障提供技术支撑。论文主要研究工作及结论包括:
1.作为论文工作的基础,首先,从装备研制需求和可靠性规范(国军标)的角度分析武器装备研制试验的特点及其在工程应用中存在的问题,分析表明:验前分布的表示、可靠性建模和信息融合是动态分布参数Bayes分析的核心问题。其次,以动态分布参数的Bayes可靠性分析为主线,构建了动态分布参数的Bayes可靠性综合试验与评估流程及选取原则。再次,研究了基于优化模型的D-S证据推理的信息融合方法,增长因子的多阶段可靠性信息融合方法,以及基于序化模型的Bayes可靠性增长信息融合方法。最后,研究了以随机过程和序化模型为主的动态分布参数的Bayes可靠性建模方法。
2.针对装备研制分批次分阶段试验的特点,结合组件级和系统级产品的试验修正策略,分别构建了组件级多阶段Bayes可靠性增长规划模型和基于非齐次Poisson过程的系统级多阶段Bayes可靠性增长规划模型。获得了装备研制不同阶段不同增长效率情况下可靠性增长试验的准确信息,实现了装备研制阶段可靠性增长的动态规划和管理。
3.为了合理利用专家经验、多阶段和异总体的多源试验信息评估产品的可靠性,针对新的Dirichlet先验分布,采用最优化理论解决了其分布参数因物理意义不明确而难以确定的问题,提出了动态分布参数的成败型Bayes可靠性评估与预测模型;分别构建了指数型Bayes可靠性增长评估模型和基于加延时间试验的威布尔寿命型Bayes可靠性增长评估模型,拓宽了模型的应用范围。采用MCMC算法解决了复杂后验量高维积分的计算问题。研究表明,该模型能够实现小子样多阶段复杂系统可靠性的评估与预测,克服了一般Bayes模型只能进行可靠性评估的缺陷。
4.针对不同阶段不同层次试验信息的小样本可靠性鉴定问题,采用环境因子折合法,提出了在样本量一定的条件下Bayes可靠性鉴定试验方案,并从生产和使用双方风险的角度与经典方案进行了对比分析,进一步明确了Bayes方法的适用范围。通过引入继承因子,分别提出并推导了基于混合Beta分布和混合Gamma分布的Bayes可靠性鉴定试验方案,为小子样装备的可靠性鉴定方案制定提供了更为客观的依据。研究表明,采用混合先验分布能充分利用异总体的先验信息,有效降低可靠性鉴定的试验量。
5.以××装备为对象,进行了本文所研究动态分布参数的Bayes可靠性试验与评估方法的设计与应用,验证了本文研究成果的有效性和可行性,为“小子样、多阶段、异总体”装备研制阶段可靠性试验与评估提供了完整的工程应用案例。
总之,本文在国家高技术研究发展计划(863)和部委“十一五”预先研究项目的资助下,从理论、方法和应用的不同层面对动态分布参数的Bayes可靠性试验与评估问题开展了系统深入的研究。本文的研究结果,以及在此基础上的进一步研究将为小子样装备研制可靠性试验与评估提供一套理论完善、工程适用的理论与方法,对开展“小子样、多阶段、异总体”装备可靠性保障技术研究具有重要的理论和工程价值。
The assessment and qualification of reliability is an important work in the development, design, finalization, purchase, application of weapon system. With the constant improvement of technique level and complexity equipment, the cost and test charge of complex equipment is expensive, the test of which is successive but the test number is not enough and the test conditions are different. Therefore, for the reliability test and assessment of equipment which has the characteristics of small sample, multi-stage, and dynamic population, it’s difficult to give scientific and rational result in traditional statistical analysis method.
Aimed at the engineering application requirement and theory requirement for reliability test and analysis in the equipment development, this dissertation carries on a systemic research on a series of difficult problem from a completely new perspective, including Bayes fuse information, reliability-growth plan, optimal selection of test scheme for a Bayes plan and reliability assessment of dynamic distribution parameter. A set of theories and methods of Bayes analysis of dynamic distribution parameter is established by incorporating change statistics theory and Bayes method, which provides technical support for equipment development. The main contributions of this dissertation are summarized as follows:
1. Firstly, considering the demand of equipment development and reliability specification, this paper analyze the characteristics of development test and its problems in engineering, the above research shows that the representation of prior distribution, modeling of reliability and fuse information are the key problems in Bayes analysis of dynamic distribution parameter. Secondly, based on the Bayes analysis of dynamic distribution parameter,the reliability test and assessment flow chart and its selection principle is constructed. Thirdly, expert's information fusion model based on the D-S principium and optimization model is put forward and the Bayes reliability information fusion approach such as order relation model and conversion of reliability test information are presented. Finally, in this paper the dynamic distribution parameter modeling methods such as order relation model and stochastic process are studied.
2. Aimed at the characteristics of equipment development by stages and by batch,Bayes reliability-growth programming model which combines test revised strategy is proposed based on exponential distribution and the non-homogeneous Poisson process. In the case of different stages and different reliability-growth levels, the exact testing information is obtained, and dynamic reliability-growth program and reliability-growth management is implemented.
3. A Bayesian reliability growth models of diverse populations based on the new Dirichlet prior distribution is studied. Aiming at some history and expert information during the equipment development, Bayes reliability growth model is presented based on the new Dirichlet distribution, the model can be used to predict the product reliability, which extends application range of the model. The method for determining prior distribution parameters is given by optimization method, it solves the problem of how to verify the hyper-parameters of the new Dirichlet prior distribution as these parameters have no specific physical meaning. Furthermore, it also establishes Bayesian model that can be applied to the reliability growth of Exponential distribution and Weibull distribution products. Then, Bayes point assessment and confidence lower limit on product reliability at current stage are imputed by comprehensively making use of the MCMC algorithm. The results show that the model can not only estimate the reliability growth, but also predict the reliability of posterior development period, which overcomes the defect that the general Bayes model can only estimate reliability growth.
4. A bayes plan of reliability qualification test is discussed in combination with the results obtained from the actual tests. On the premise that the quantity of the test is ensured, a scheme of reliability acceptance test in binomial case is formulated by using prior information efficiently. In view of the meaning of sampling risk actually concerned by users and producers in acceptance test, the difference and relationship between the Bayes reliability qualification and assessment are analyzed.The outcome of the project instance is compared with the traditional method according to the plan of reliability acceptance test, the range of Bayes method is clear and definite. Considering not only the testing information from related products but also differences between such products, a new mixed prior distribution is used by introducing the inheritance factor, moreover the inheritance factor is thought as a random variable, then the Bayes decision model of the qualification plan was established. Based on the method above, the Bayes plans of qualification test are researched for binomial case and exponential case. The design experiments show such algorithm is very effective and efficient, and it has active guidance meaning and applied value to the engineering practice.
5. The methods of reliability synthetical test design for Bayes analysis of dynamic distribution parameter are applied to reliability test and analysis of the weapon system, which has the characteristics of small sample, multi-stage, dynamic population.
In summary, with the aid of related key projects of armament, this dissertation investigates the problem of Bayes reliability analysis of dynamic distribution parameter systematically and deeply in theory, method and application. The studying results and improvements based on this paper will provide a set of perfect-theory and applicable-engineering theory and methods for Bayes reliability analysis of dynamic distribution parameter. And the results are of important theory value and engineering instruction significance for researches on the equipment reliability support technique, which has the characteristics of small sample, multi-stage, and dynamic population.
论文针对装备研制阶段可靠性试验与评估的工程需求,将变动统计理论和Bayes方法引入到装备研制阶段可靠性试验与分析中,从新的视角对动态分布参数的Bayes多源信息融合、可靠性增长规划、可靠性鉴定方案的优化选择和可靠性试验评估等一系列难题进行了系统深入的研究,建立了一套适用于动态分布参数的Bayes可靠性试验鉴定与评估的理论和方法,为现代武器装备研制阶段可靠性保障提供技术支撑。论文主要研究工作及结论包括:
1.作为论文工作的基础,首先,从装备研制需求和可靠性规范(国军标)的角度分析武器装备研制试验的特点及其在工程应用中存在的问题,分析表明:验前分布的表示、可靠性建模和信息融合是动态分布参数Bayes分析的核心问题。其次,以动态分布参数的Bayes可靠性分析为主线,构建了动态分布参数的Bayes可靠性综合试验与评估流程及选取原则。再次,研究了基于优化模型的D-S证据推理的信息融合方法,增长因子的多阶段可靠性信息融合方法,以及基于序化模型的Bayes可靠性增长信息融合方法。最后,研究了以随机过程和序化模型为主的动态分布参数的Bayes可靠性建模方法。
2.针对装备研制分批次分阶段试验的特点,结合组件级和系统级产品的试验修正策略,分别构建了组件级多阶段Bayes可靠性增长规划模型和基于非齐次Poisson过程的系统级多阶段Bayes可靠性增长规划模型。获得了装备研制不同阶段不同增长效率情况下可靠性增长试验的准确信息,实现了装备研制阶段可靠性增长的动态规划和管理。
3.为了合理利用专家经验、多阶段和异总体的多源试验信息评估产品的可靠性,针对新的Dirichlet先验分布,采用最优化理论解决了其分布参数因物理意义不明确而难以确定的问题,提出了动态分布参数的成败型Bayes可靠性评估与预测模型;分别构建了指数型Bayes可靠性增长评估模型和基于加延时间试验的威布尔寿命型Bayes可靠性增长评估模型,拓宽了模型的应用范围。采用MCMC算法解决了复杂后验量高维积分的计算问题。研究表明,该模型能够实现小子样多阶段复杂系统可靠性的评估与预测,克服了一般Bayes模型只能进行可靠性评估的缺陷。
4.针对不同阶段不同层次试验信息的小样本可靠性鉴定问题,采用环境因子折合法,提出了在样本量一定的条件下Bayes可靠性鉴定试验方案,并从生产和使用双方风险的角度与经典方案进行了对比分析,进一步明确了Bayes方法的适用范围。通过引入继承因子,分别提出并推导了基于混合Beta分布和混合Gamma分布的Bayes可靠性鉴定试验方案,为小子样装备的可靠性鉴定方案制定提供了更为客观的依据。研究表明,采用混合先验分布能充分利用异总体的先验信息,有效降低可靠性鉴定的试验量。
5.以××装备为对象,进行了本文所研究动态分布参数的Bayes可靠性试验与评估方法的设计与应用,验证了本文研究成果的有效性和可行性,为“小子样、多阶段、异总体”装备研制阶段可靠性试验与评估提供了完整的工程应用案例。
总之,本文在国家高技术研究发展计划(863)和部委“十一五”预先研究项目的资助下,从理论、方法和应用的不同层面对动态分布参数的Bayes可靠性试验与评估问题开展了系统深入的研究。本文的研究结果,以及在此基础上的进一步研究将为小子样装备研制可靠性试验与评估提供一套理论完善、工程适用的理论与方法,对开展“小子样、多阶段、异总体”装备可靠性保障技术研究具有重要的理论和工程价值。
The assessment and qualification of reliability is an important work in the development, design, finalization, purchase, application of weapon system. With the constant improvement of technique level and complexity equipment, the cost and test charge of complex equipment is expensive, the test of which is successive but the test number is not enough and the test conditions are different. Therefore, for the reliability test and assessment of equipment which has the characteristics of small sample, multi-stage, and dynamic population, it’s difficult to give scientific and rational result in traditional statistical analysis method.
Aimed at the engineering application requirement and theory requirement for reliability test and analysis in the equipment development, this dissertation carries on a systemic research on a series of difficult problem from a completely new perspective, including Bayes fuse information, reliability-growth plan, optimal selection of test scheme for a Bayes plan and reliability assessment of dynamic distribution parameter. A set of theories and methods of Bayes analysis of dynamic distribution parameter is established by incorporating change statistics theory and Bayes method, which provides technical support for equipment development. The main contributions of this dissertation are summarized as follows:
1. Firstly, considering the demand of equipment development and reliability specification, this paper analyze the characteristics of development test and its problems in engineering, the above research shows that the representation of prior distribution, modeling of reliability and fuse information are the key problems in Bayes analysis of dynamic distribution parameter. Secondly, based on the Bayes analysis of dynamic distribution parameter,the reliability test and assessment flow chart and its selection principle is constructed. Thirdly, expert's information fusion model based on the D-S principium and optimization model is put forward and the Bayes reliability information fusion approach such as order relation model and conversion of reliability test information are presented. Finally, in this paper the dynamic distribution parameter modeling methods such as order relation model and stochastic process are studied.
2. Aimed at the characteristics of equipment development by stages and by batch,Bayes reliability-growth programming model which combines test revised strategy is proposed based on exponential distribution and the non-homogeneous Poisson process. In the case of different stages and different reliability-growth levels, the exact testing information is obtained, and dynamic reliability-growth program and reliability-growth management is implemented.
3. A Bayesian reliability growth models of diverse populations based on the new Dirichlet prior distribution is studied. Aiming at some history and expert information during the equipment development, Bayes reliability growth model is presented based on the new Dirichlet distribution, the model can be used to predict the product reliability, which extends application range of the model. The method for determining prior distribution parameters is given by optimization method, it solves the problem of how to verify the hyper-parameters of the new Dirichlet prior distribution as these parameters have no specific physical meaning. Furthermore, it also establishes Bayesian model that can be applied to the reliability growth of Exponential distribution and Weibull distribution products. Then, Bayes point assessment and confidence lower limit on product reliability at current stage are imputed by comprehensively making use of the MCMC algorithm. The results show that the model can not only estimate the reliability growth, but also predict the reliability of posterior development period, which overcomes the defect that the general Bayes model can only estimate reliability growth.
4. A bayes plan of reliability qualification test is discussed in combination with the results obtained from the actual tests. On the premise that the quantity of the test is ensured, a scheme of reliability acceptance test in binomial case is formulated by using prior information efficiently. In view of the meaning of sampling risk actually concerned by users and producers in acceptance test, the difference and relationship between the Bayes reliability qualification and assessment are analyzed.The outcome of the project instance is compared with the traditional method according to the plan of reliability acceptance test, the range of Bayes method is clear and definite. Considering not only the testing information from related products but also differences between such products, a new mixed prior distribution is used by introducing the inheritance factor, moreover the inheritance factor is thought as a random variable, then the Bayes decision model of the qualification plan was established. Based on the method above, the Bayes plans of qualification test are researched for binomial case and exponential case. The design experiments show such algorithm is very effective and efficient, and it has active guidance meaning and applied value to the engineering practice.
5. The methods of reliability synthetical test design for Bayes analysis of dynamic distribution parameter are applied to reliability test and analysis of the weapon system, which has the characteristics of small sample, multi-stage, dynamic population.
In summary, with the aid of related key projects of armament, this dissertation investigates the problem of Bayes reliability analysis of dynamic distribution parameter systematically and deeply in theory, method and application. The studying results and improvements based on this paper will provide a set of perfect-theory and applicable-engineering theory and methods for Bayes reliability analysis of dynamic distribution parameter. And the results are of important theory value and engineering instruction significance for researches on the equipment reliability support technique, which has the characteristics of small sample, multi-stage, and dynamic population.
引文
[1]张金槐,刘琦,冯静. Bayes试验分析方法[M].长沙:国防科技大学出版社,2007.
[2] Jeffreys H. Theory of Probability[M]. Oxford University Press,1961.
[3] Robbins H. An Empirical Bayes Approach to Statistics[J]. Proc. Third Berkeley Symposium on Math Statist and Prob, 1955,1:157-164.
[4] Raiffa H., Schlaifer R. Applied Statistical Decision Theory[M]. Boston: Harvard University Press, 1961.
[5] Box G.E. P., Tiao G. C. Bayesian Inference in Statistical Analysis[M].London: Addison-Wesley, 1973.
[6] DeFinetti B. Theory of Probability[M]. New York: Wiley,1975.
[7] Martz H F, Waller R A.Bayesian reliability analysis[M].John Wiley & Sons,New York:1982.
[8] Berger J. Statistical decision theory and bayesian analysis[M].2nd edition.New York:Springer-Verlag,1985.
[9]张金槐,唐雪梅. Bayes方法[M].长沙:国防科大出版社,1992.
[10] Pate-Cornell M E.Uncertainties in risk analysis:six level oftreatment[J]. Reliability Engineering & System Safety,1996,54:2-3.
[11] Coolen F P A. On Bayesian reliability analysis with informative priors and censoring[J]. Reliability Engineering and System Safety,1996,53:91-98.
[12] Singpurwalla N D, Song M S. Reliability analysis usingWeibull lifetime data and expert opinion[J].IEEE Trans onReliability,1988,37(3):340-347.
[13] Coolen F P A, Newby M J. Bayesian reliability analysis with imprecise prior probabilities[J]. Reliability Engineering and System Safety,1994,43:75-85.
[14] Lichtenstein S, Newman J R. Empirical scaling of commonverbal phrases associated with numerical probabilities[J].Psychonometric Science,1967,9(10): 563-564.
[15] Ayyub B M.A. practical guide on conducting expert-opinionelicitation of probabilities and consequences for corps facilities[R]. IWR Report 01-R-01, 2001,1.
[16]刘琦,武小悦,张金槐.小子样武器装备可靠性评定过程中专家信息的规范化描述[J].航空动力学报, 2007,21(1):37-40.
[17]马溧梅,武小悦,刘琦.小子样装备可靠性试验中专家信息描述性综述[J].电子产品可靠性与环境试验,2007,25(2):62-66.
[18]王华伟.液体火箭发动机可靠性增长管理研究[D].国防科技大学博士学位论文, 2004.
[19]蔡洪,张士峰. Bayes试验分析与评估[M].长沙:国防科技大学出版社, 2004.
[20]张金槐.多维分布参数可变时的Bayes估计[J].飞行器测控学报,2002, 25(1):62-66.
[21]张金槐.多维动态参数的多层Bayes融合估计[J].飞行器测控学报, 2003,25(1):62-66.
[22] Joseph G. Ibrahim, Chen Minghui. Bayesian survival analysis[M]. New York: Springer, 2001.
[23] Bilal M. Ayyub, Richard. H. McCuen. Probability, Statistics, & Reliability for Engineers[M].Boca Raton: CRC Press, 1997, New York.
[24]张孝令,刘福生.贝叶斯动态模型及其预测[M].济南:山东科学技术出版社,1992.
[25]傅惠民.二项分布整体推断方法[J].航空学报, 2000.2:155-158.
[26]傅惠民.整体推断的极大似然方法[J].机械强度, 2002.1:1-5.
[27]张士峰.Bayes小子样理论及其在武器装备系统评估中的运用研究[D].国防科技大学博士学位论文, 2000.
[28] Martz H.F, Waller R. A. The basics of Bayes reliability estimation from attribute test data[M]. Los Alamos Scientific Laboratory: Report UC-79p, February, 1976.
[29] Tran T.H.,Murdock W.,Paul J.,Pohl E.A. Bayesian analysis for system reliability inferences[C]. Proceedings of the Annual Reliability and Maintainability Symposium 1999. IEEE, Piscataway, NJ, USA,99CB36283: 151-153.
[30] Martz H.F. etc. Empirical Bayes estimation of the reliability of Nuclear- Power-Plant emergency diesel generators[J].Technometrics, 1996,Vol.38, No.1:11-24.
[31] Robbins H. The empirical Bayes approach to statistical decision problems[J]. Annual Mathematics Statistics,1964,35:1-20.
[32] Kurt Pom.The two-stage Bayesian method used for the T-Book application. Reliability engineering and system safety,1996,169-179.
[33] Barlow R. E., Scheuer E. M. Reliability Growth During a Development Testing Program[J]. Technometrics, 1966, 8(1): 53-60.
[34]何国伟.评估电子产品平均寿命的一种变动统计方法[J].电子学报, 1981,5: 70-74.
[35]周源泉,郭建英.故障分类时顺序约束指数可靠性增长的Bayes精确限[J].仪器仪表学报, 1999,12, 20(6): 626-629.
[36]张士峰,杨万君.异总体统计问题的Bayes分析[J].战术导弹技术, 2003,3: 33-37.
[37]周源泉.估计逐次提高试制产品精度的Bayes方法[J].电子学报, 1984,7, 12(4):51-56.
[38] MAZZUCHI T.A, SOYER R.A. Bayes attribute reliability growth model[C].//Proceedings Annual Reliability and Maintainability Symposium, 1991.322-325.
[39] VanDorp, J. R., Mazzuchi, T. A., Soyer, R. Sequential Inference and Decision Making for Single Mission Systems Development [J], Journal of Statistical Planning and Inference, August 15 1997, 62(2): 207-218.
[40] Erkanli A, Mazzuchi T A, Soyer R. Bayesian computations for a class of reliability growth models[J]. American Statistical Association and the American Society for Quality Technometrics, 1998, 40(1):14-23.
[41] MIL-HDBK-189(1981), Reliability Growth Management.
[42] MIL-HDBK-338(1984), Reliability Design Handbook for Electronic Equipment.
[43] Sanford A.D., Martin G.M. Simulation-based Bayesian estimation of an affine term structure model[J]. Computational Statistics & Data Analysis, 2005, Vol.49(2): 527-554.
[44] Kennedy M.C., Hagan A.O. Bayesian calibration of computer models[J]. Journal of the Royal Statistical Society.ser.B. Statistical menthodological. 2001, Vol.63(3): 425-464.
[45] Paulino C.D., Silva G., Achcar J.A. Bayesian analysis of correlated misclassified binary data[J]. Computational Statistics & Data Analysis, 2005, Vol.49(4): 1120-113.
[46] Vidal P.I., Branco M.D., Arellano-Valle R.B. Bayesian sensitivity analysis and model comparison for skew elliptical models[J]. Journal of Statistical Planning and Inference, 2006, Vol.136(10): 3435-3457.
[47] Strickland C.M., Catherine S.F., Martin G.M. Bayesian analysis of the stochastic conditional duration model[J]. Computational Statistics & Data Analysis, 2006, Vol.50(9): 2247-2267.
[48] Wendai W., Loman J. A new approach for evaluating the reliability of highly reliable systems[C]. Reliability and Maintainability Symposium, 2003. Annual.
[49] Chaudhuri G., Hu K., Afshar N. A new approach to system reliability[J].IEEE Transactions on Reliability. 2001, Vol.50(1): 75-84.
[50] Mahadevan S., Rebba R. Validation of reliability computational models using Bayes networks[J]. Reliability Engineering & System Safety, 2005,Vol.87 (2):223-232.
[51] Guida M., Pulcini G. Bayesian analysis of repairable systems showing a bounded failure intensity[J].Reliability Engineering & System Safety, 2006,Vol.91 (7): 828-838.
[52] Philippe W., Lionel J. Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)[J],Reliability Engineering & System Safety, 2006, Vol. 91(2): 149-162.
[53] Sarhan M. The Bayes procedure in exponential reliability family models using conjugate convex tent prior family. Reliability engineering & system safety[J]. European Safety and Reliability Association. 2001, Vol.71(1): 97-102.
[54] Rutten A.L.B., Stolper C.F., Lugten R.F.G. A Bayesian perspective on the reliability of homeopathic repertories[J]. 2006, Vol. 95(2): 88-93.
[55]李欣欣.基于Bayes变动统计的精度鉴定与可靠性增长评估研究[D].国防科技大学博士学位论文,2008.
[56]唐雪梅,张金槐.武器装备小子样试验分析与评估[M].北京:国防工业出版社,2000.
[57]王玲玲.指数分布下可靠性Bayes验证[J].华东师范大学学报(自然科学版), 1993, (3):1-10.
[58]张志华,姜礼平.指数型产品失效率鉴定试验的Bayes方案[J].应用概率统计, 2000, 16(1): 66-70.
[59]许志强,王艳.指数分布下失效率的Bayes验证试验抽样方案[J].长春工业大学学报, 2004, 25(1): 76-78.
[60]陈宜辉,姜礼平,吴树和.指数分布下Bayes鉴定试验方案[J]..运筹与管理, 2002, 11(2): 56-59.
[61]李秀良等,靶场雷达可靠性、可维性验证方法研究[J],舰船电子工程,1998,(3):43-46.
[62]张士峰. Weibull型产品的可靠性验证[J].国防科技大学学报, 2001, 23(4): 16-19.
[63]陈文华.威布尔分布下失效率的Bayes验证试验方法[J].机械工程学报, 2005. 41(12): 118-121.
[64]姜礼平,张志华.成败型产品的成功率鉴定试验的一种Bayes方法[J].工程数学学报, 2000, 17(4): 25-29.
[65]王国玉.电子系统小子样试验理论方法[M].国防工业出版社,2003.
[66] ZhangKun.The Bayesian Sequential Testing Analysis[J].Chinese Journal ofApplied Probability and Statistics, 1999, 15(3):287-293.
[67]周桃庚,沙定国.贝叶斯可靠性序贯验证试验方法[J].仪器仪表学报,2001,22(4):373-374.
[68]唐义良,王景芹.基于Bayes的量度继电器成功率可靠性验证试验[J].系统工程与电子技术,1999,021(008):60-63.
[69]申绪涧,戚宗锋,汪连栋等.正态分布未知参数的联合序贯验后加权检验方法[J].系统工程与电子技术,2004,26(6):744-746.
[70]邢云燕.小子样条件下武器装备可靠性与维修性指标验证方法[D].国防科技大学硕士学位论文,2005.
[71]金振中.战术导弹综合试验与Bayes评估方法研究[D].国防科技大学博士学位论文, 2007.
[72]王永杰,金振中,孙晓峰.小子样综合试验方法在舰空导弹武器系统设计定型中的应用[C].总装备部武器装备小子样试验方法研讨会论文集,2003.
[73]唐雪梅.武器装备小子样试验分析与评估[D].国防科技大学博士学位论文, 2008.
[74] Woodroofe M. W., Willard D., Singpurwalla N. D. PershingⅡFollow-on Test: Size Reduced by Sequential Analysis[R]. AD-A192136.
[75] Alok P., Ashok S., William J. Z. Bayes Estimation of the Linear Hazard-rate Model[J].IEEE Trans. on Reliability, 1993, 42(4):636-640.
[76] Sarhan A.. Bayes Estimation of the General Hazard Rate Model[J]. Reliability Engineering of System Safety, 1999, 66: 85-91.
[77]王之任.近代大型液体火箭发动机的特点[J].推进技术, 1991(4):23-25.
[78]庞敏.当前可靠性增长试验存在问题及建议[J].质量与可靠性, 2004(1).
[79]梅文华.可靠性增长试验[M].国防工业出版社,2003.
[80] ZHOU Yuan-quan, WENG Zhao-xi. AMSAA-Bise model[C] In:3rd Japan-China Symposium on Statistics. Tokyo, Japan: Soka University, 1989:179-182.
[81]周源泉,翁朝曦.可靠性增长[M].北京:科学出版社, 1992.
[82]周源泉.质量可靠性增长与评定方法[M].北京:北京航空航天大学出版社, 1997.
[83]田国梁. Gompertz模型可靠性增长的分析方法[J].强度与环境, 1989, 5: 22-29.
[84]田国梁.负二项分布可靠性增长的经典分析方法[J].强度与环境, 1990, 1: 28-33.
[85]谢红卫,宫二玲,贺勇军.时变结构多阶段任务系统的可靠度研究[J].国防科技大学学报, 1999, 05:44-48.
[86]赵宇,黄敏.变母体变环境数据的可靠性综合评估模型[J].北京航空航天大学学报, 2002, 28(5): 597-600.
[87]杜振华,赵宇,陈金盾.研制阶段产品可靠性综合评估技术研究[J].可靠性工程,2002, 4: 180-183.
[88]刘琦.液体火箭发动机可靠性增长试验评定方法研究[D].国防科技大学研究生院, 2003.
[89]胡明祥.鱼雷可靠性增长试验和分析方法研究[D].西北工业大学研究生院, 2006.
[90] Barlow R.E., Scheuer E.M. Reliability growth during a development testing program[J]. Technometrics, 1966, 8(1): 53-60.
[91] Weinrich M.C., Gross A.J. The Barlow-Scheuer reliability growth model from a Bayesian viewpoint[J]. Technometrics, 1978, 20(3): 249-254.
[92] Smith A.F.M. A Bayesian note on reliability growth during a development testing program[J]. IEEE Trans. Reliability, 1977, 26: 346-347.
[93] Fard N.S., Dietrich D.L. A Bayes reliability growth model for a development testing program[J]. IEEE Trans. Reliability, 1987, 36: 568-572.
[94] Fard N.S., Dietrich D.L. Comparison of attribute reliability growth models[C]. Proc. Ann. Reliability & Maintainability Symp, 1983: 24-29.
[95] Mazzuchi T.A., Soyer R. Reliability assessing and prediction during product development[C]. Proc. Ann. Reliability & Maintainability Symp, 1992: 468-474.
[96] Mazzuchi T.A., Soyer R. A Bayes method for assessing product-reliability during development testing [J]. IEEE Trans. Reliability, 1993, 42(3): 503-510.
[97] Somerrville I F, Dietrich D L, Mazzuchi T A. Bayes reliability analysis using the dirichlet prior distribution with emphasis on accelerated life testing run in random order[J]. Nonlinear Analysis, Theory Method&Application, 1997, 30(7): 4415-4423.
[98]周源泉,翁朝曦.可靠性评定[M].北京:科学出版社, 1990.
[99] Duane J.T. Learning curve approach to reliability monitoring[J]. IEEE Trans.Aerospace, 1964, 2: 563-566.
[100] Woods W.M., Drake J.E., Chandler J.D. Analysis and evaluation of discrete reliability growth models with and without failure discounting[R]. NPS55-88-013, US Naval Postgraduate School, 1988.
[101] Woods W.M. The effect of discounting failures and weighting data on the accuracy of some reliability growth models[C]. Proc. Ann. Reliability &Maintainability Symp, 1990: 200-204.
[102] Gross A.J., Kamins M. Reliability assessment in the presence of reliability growth [C]. Proc. Ann. Reliability & Maintainability Symp, 1968: 187-198.
[103] Virene E.P. Reliability growth and its upper limit [C]. Proc. Ann. Reliability & Maintainability Symp, 1968: 265-270.
[104] Kececioglu D, Jiang S, Vassiliou P. The modified Gompertz reliability-growth model [C]. Proc. Ann. Reliability & Maintainability Symp, 1994: 160-165.
[105] Duane J. T. Learning Curves Approach to Reliability Monitoring[J]. IEEE Transaction on Aerospace,1964: 563-566.
[106] Crow L H. Reliability Analysis for Complex Repairable System[R]. ADA0296, 1975.
[107] Guida M, Calabria R, Pulcini G. Bayes Inference for a Non-homogeneous Passion Process with Power Intensity Law[J]. IEEE Transaction on Reliability,1989, 38(4):603-609.
[108]周源泉,郭建英.可靠性增长模型的Bayes推断及其在发动机上的应用[J].推进技术, 2000, 21(1): 49-53.
[109]周源泉.阿里安运载火箭的可靠性增长分析[J].导弹与航天运载技术, 2000,247(5):25-29.
[110]周源泉,张立堂,刘振德.涡轮发动机的可靠性增长分析[J].推进技术, 1997,18(4): 496-499.
[111]周源泉,周百里,王家明.某型涡轮发动机的可靠性增长分析[J].推进技术, 1997,18(5): 17-21.
[112]周源泉,杨双进,顿赛英.中国长征系列运载火箭的可靠性分析[J].导弹与航天运载火箭技术,2002, 2:37-40.
[113]田国梁.二项分布的可靠性增长预测模型[J].强度与环境, 1991, 4: 17-25.
[114]田国梁.二项分布的可靠性增长模型[J].宇航学报, 1992, 1:55-61.
[115]宫二玲,谢红卫,李鹏波,蒋英杰.指数寿命可靠性增长评估中增长因子的确定方法[J].国防科技大学学报, 2008,06: 57-60.
[116]吴祺,闫志强,谢红卫.复杂系统可靠性增长的动态建模方法[J].计算机仿真, 2007,11:321-324.
[117] Yan Zhiqiang, Li Xinxin,Xie Hongwei,Jiang Yingjie.Bayesian synthetic evaluation of multistage reliability growth with instant and delayed fix modes[J]Journal of Systems Engineering and Electronics, 2008,06:229-236.
[118]党晓玲.柔性制造系统可靠性增长管理与分析技术研究[D].国防科技大学研究生院, 1999.
[119]张湘平.小子样统计推断与融合理论在武器系统评估中的应用研究[D].国防科技大学研究生院, 2003.
[120]冯静.小子样复杂系统可靠性信息融合方法与应用研究[D].国防科技大学研究生院, 2004.
[121]刘飞.固体火箭发动机可靠性增长试验理论及应用研究[D].国防科技大学研究生院, 2006.
[122]叶尔骅.具有可靠性增长的三项分布模型随机参量的贝叶斯估计[J].宇航学报,1985, 4: 31-35.
[123]陈世基.具有可靠性增长的二、三项分布模型参数Bayes估计的注记[J].福建师范大学学报(自然科学版),1983(1): 11-16.
[124]韩明.基于无失效数据的可靠性参数估计[M].北京:中国统计出版社, 2005.
[125]林达明.一个贝叶斯离散可靠性增长模型—多元Beta模型[J].汕头大学学报(自然科学版), 1996, 11(2): 34-43.
[126]张士锋,李荣.基于Dirichlet验前的Bayes可靠性分析[J].电子产品可靠性与环境试验,1999,6:12-15.
[127]喻天翔,宋笔锋,冯蕴文.基于Dirichlet先验分布的Bayes二项可靠性增长方法[J].系统工程理论与实践.2006.1:131-135.
[128]刘飞,窦毅芳,张为华.基于狄氏先验分布的固体火箭发动机可靠性增长Bayes分析[J].固体火箭技术.Vo.l 29,No.4,2006(11):239-242.
[129]刘飞,王中伟,张为华.指数寿命产品可靠性增长的Bayes分析[J].国防科技大学学报.Vol 28 No.4 2006:1-14:128-132.
[130] Guo-Ying Li, Qi-Guang Wu, Yong-Hui Zhao. ON BAYESIAN ANALYSIS OF BINOMIAL RELIABILITY GROWTH[J]. J.Japan Statist.Soc.Vol.32 No.1 2002:1-14.
[131]吴启光,李国英,赵勇辉.基于指数可靠性增长模型的一类新的先验分布[J].数学物理学报.Vol 23A, 2003:474-484.
[132] Edwards W., Lingdman H., Savage L. J. Bayesian Statistical Inference for Psychological Research[M]. Psychol. Rev. 70, 1963:193-242.
[133] Lindley D. V. Approximate Bayesian Methods Bayesian Statistics II[M].Valencia Press,1980: 223-245.
[134] Tierney L., Kadane J. B. Accurate Approximations for Posterior Moments andMarginals[R]. Technical Report #326, Department of Statistics, Carnegie Mellon University, Pittsburgh, 1984.
[135] Naylor J. C., Smith A. M. Applications of Method for the Efficient Computationof Posterior Distributions[J]. Appl. Statist.31, 1982: 214-225.
[136] Ibrahim J G, Chen Minghui, Sinha D. Bayesian Survival Analysis [M]. NewYork Berlin Heidelberg, 2001. 13-17.
[137] Scollnik P M. Actuarial modeling with MCMC and BUGS[J]. North American Actuarial Journal, 2001, 5(2): 96-125.
[138] David S, Andrew T, Nicky B. BUGS 0.5: Bayesian inference Using Gibbs Sampling Manual[M].version ii.Cambridge, U. K.: MRC Biostatistics Unit,1996.
[139]茆诗松,王静龙,濮晓龙.高等数理统计[M].高等教育出版社和施普林格出版社,1998.
[140]曹晋华,程侃.可靠性数学引论[M].高等教育出版社,2006.
[141] Mihir Arjunwadkar, Marc Fasnacht. A Bayesian analysis of Monte Carlo correlation times for the two-dimensional Ising model[J].Physica A: Statistical Mechanics and its Applications.2003, Vol.323:487-503.
[142] Awad E.G. Bayes estimation of parameters in a three non-independent component series system with time dependent failure rate[J].Applied Mathematics and Computation, 2004, Vol.158(1):121-132.
[143]杨明,毕涌,雷英杰.混合Weibull分布参数估计的EM算法[J].华北工学院学报,2003, 24(4): 353-356.
[144] Carlin B.P.,Louis T.A. Bayes and Empirical Bayes Methods for Data Analysis [M]. New York: Chapman and Hall, 2000.
[145] Gilks W.R, Richardson S. Markov Chain Monte Carlo in Practice[M]. New York:Chapman and Hall,1996.
[146]唐年胜,韦博成.非线性再生散度模型[M].北京:科学出版社,2007.
[147] Tillman F. A., Kuo W., Hwang C. L. Bayesian Reliability & Avilability Review [J].IEEE Trans.on Reliability, 1982, 31(4): 362-372.
[148]林静,韩玉启,朱慧明.基于MCMC稳态模拟的Weibull共享异质性模型及其可靠性应用[J].中国工程科学. 2006,8(2):55-60.
[149]龚庆祥,赵宇,顾长鸿.型号可靠性工程手册[M].北京:国防工业出版社, 2007.
[150]朱曦全.航天产品的可靠性增长试验方法[J].导弹与航天运载技术,2006.1:58-61.
[151]朱曦全.可靠性增长试验载人航天运载火箭电气产品研制过程中的应用[J].导弹与航天运载技术,2004.1:61-66.
[152]安伟光,胡经畬.可靠性增长试验方法的研究[M].哈尔滨:哈尔滨工业大学出版社,1996.
[153] Perter Congdon.Applied Bayesian Modeling[M]. New York: John Wiley&Sons,Itd. 2003.
[154] Morris C. N. Natural exponential families with quadratic variance function[M]. Statistica ltheory. Ann.Statist., 1983,11:515-529.
[155] Walter G.G, Hamedani G.G. Bayes empirical estimation for discrete exponential families[J]. Ann.Inst.Statist.Math.,1989,41:101-119.
[156]严惠云.Bayes理论在二项分布可靠性分析中的应用[D].西北工业大学研究生院, 2007.
[157]王玮,周海云,尹国举.使用混合Beta分布的Bayes方法[J].系统工程理论与实践. 2005,9: 142-144.
[158] Nader Ebrahimi.Testing Exponentiality of the Residual Life Based On Dynamic Kullback-Leibler Information[J].IEEE TRANSACTIONS ON RELIABILITY, VOL. 47, NO. 2, 1998 JUNE:197-201.
[159]任开军,吴孟达.先验分布融合的KULLBACK信息方法[J].装备指挥技术学院学报, 2002, 13(4): 90-92.
[160] Pullkkinen U. Methods for combination of expert judgements[J]. Reliability Engineering and System Safety,1993:40:111-118.
[161] Robinson D.G, Dietrich D. A New Nonparametric Growth Model[J]. IEEE Trans. onReliability, 1987, 36(10): 411-418.
[162] Robinson D.G, Dietrich D. A Nonparametric-Bayes Reliability-growth Model[J]. IEEE Trans. on Reliability, 1989, 38(11):591-59.
[163] Athanasios Kottas.Nonparametric Bayesian survival analysis using mixtures ofWeibull distributions[J].Journal of Statistical Planning and Inference. 136 (2006) 578-596.
[164] Luz Callea M, Guadalupe Gomez. Nonparametric Bayesian estimation from interval-censored data using Monte Carlo methods[J].Journal of Statistical Planning and Inference .98 (2001) 73-87.
[165] Guida M, Pulcini G. Bayesian reliability assessment of repairable systems during multi-stage development programs[J]. IIE Transactions. 2005, 37(11), 1071-1081.
[166] Guida M, Pulcini G. Bayesian reliability-growth modeling of repairable mechanical systems[C].// Proceedings of the 3rd International Conference on Mathematical Methods in Reliability-MMR2002, Trondheim(Norway), 2002, 263-266.
[167] Benton A.W, Crow L.H. Integrated reliability growth testing[C],1989 proe. Annual Reliability&Maintainability Symp,1989,160-166.
[168] IEC 1164, Reliability growth-statistical test and estimation method[S],1995.
[169] Kenneth J.F, Ali Mosleh. An approach to quantifyingReliability-GrowthEffectiveness[J].1995 Proceedings Annual Reliability and Maintainability Symposium,166-173.
[170] http://www.mrc-bsu.com.ac.uk/bugs/the bugs project.
[171] Nelson W B.Accelerated testing statiscal model test plan data analysis[M].New York:John Wiley & Sons,Inc.1990.
[172] Meeker,M.Q.,ESCOBAR, L.A. Statiscal Methods for Reliability Data[M].New York:John Wiley & Sons,Inc.1998.
[173]何国伟.可靠性试验技术[M].北京:国防工业出版社,1995.
[174] Kleyner A,et al. Bayesian Techniques to Reducee the Sample Size in Automotive Electronics Attribute Testing[J].Microelectronis Reliability,1997, 37(6): 879-883.
[175]张士峰.成败型产品可靠性的Bayes评估[J].兵工学报, 2001, 22(2): 238-240.
[176]苏德清.可靠性标准及其应用[M].北京:国防工业出版社,1988.
[2] Jeffreys H. Theory of Probability[M]. Oxford University Press,1961.
[3] Robbins H. An Empirical Bayes Approach to Statistics[J]. Proc. Third Berkeley Symposium on Math Statist and Prob, 1955,1:157-164.
[4] Raiffa H., Schlaifer R. Applied Statistical Decision Theory[M]. Boston: Harvard University Press, 1961.
[5] Box G.E. P., Tiao G. C. Bayesian Inference in Statistical Analysis[M].London: Addison-Wesley, 1973.
[6] DeFinetti B. Theory of Probability[M]. New York: Wiley,1975.
[7] Martz H F, Waller R A.Bayesian reliability analysis[M].John Wiley & Sons,New York:1982.
[8] Berger J. Statistical decision theory and bayesian analysis[M].2nd edition.New York:Springer-Verlag,1985.
[9]张金槐,唐雪梅. Bayes方法[M].长沙:国防科大出版社,1992.
[10] Pate-Cornell M E.Uncertainties in risk analysis:six level oftreatment[J]. Reliability Engineering & System Safety,1996,54:2-3.
[11] Coolen F P A. On Bayesian reliability analysis with informative priors and censoring[J]. Reliability Engineering and System Safety,1996,53:91-98.
[12] Singpurwalla N D, Song M S. Reliability analysis usingWeibull lifetime data and expert opinion[J].IEEE Trans onReliability,1988,37(3):340-347.
[13] Coolen F P A, Newby M J. Bayesian reliability analysis with imprecise prior probabilities[J]. Reliability Engineering and System Safety,1994,43:75-85.
[14] Lichtenstein S, Newman J R. Empirical scaling of commonverbal phrases associated with numerical probabilities[J].Psychonometric Science,1967,9(10): 563-564.
[15] Ayyub B M.A. practical guide on conducting expert-opinionelicitation of probabilities and consequences for corps facilities[R]. IWR Report 01-R-01, 2001,1.
[16]刘琦,武小悦,张金槐.小子样武器装备可靠性评定过程中专家信息的规范化描述[J].航空动力学报, 2007,21(1):37-40.
[17]马溧梅,武小悦,刘琦.小子样装备可靠性试验中专家信息描述性综述[J].电子产品可靠性与环境试验,2007,25(2):62-66.
[18]王华伟.液体火箭发动机可靠性增长管理研究[D].国防科技大学博士学位论文, 2004.
[19]蔡洪,张士峰. Bayes试验分析与评估[M].长沙:国防科技大学出版社, 2004.
[20]张金槐.多维分布参数可变时的Bayes估计[J].飞行器测控学报,2002, 25(1):62-66.
[21]张金槐.多维动态参数的多层Bayes融合估计[J].飞行器测控学报, 2003,25(1):62-66.
[22] Joseph G. Ibrahim, Chen Minghui. Bayesian survival analysis[M]. New York: Springer, 2001.
[23] Bilal M. Ayyub, Richard. H. McCuen. Probability, Statistics, & Reliability for Engineers[M].Boca Raton: CRC Press, 1997, New York.
[24]张孝令,刘福生.贝叶斯动态模型及其预测[M].济南:山东科学技术出版社,1992.
[25]傅惠民.二项分布整体推断方法[J].航空学报, 2000.2:155-158.
[26]傅惠民.整体推断的极大似然方法[J].机械强度, 2002.1:1-5.
[27]张士峰.Bayes小子样理论及其在武器装备系统评估中的运用研究[D].国防科技大学博士学位论文, 2000.
[28] Martz H.F, Waller R. A. The basics of Bayes reliability estimation from attribute test data[M]. Los Alamos Scientific Laboratory: Report UC-79p, February, 1976.
[29] Tran T.H.,Murdock W.,Paul J.,Pohl E.A. Bayesian analysis for system reliability inferences[C]. Proceedings of the Annual Reliability and Maintainability Symposium 1999. IEEE, Piscataway, NJ, USA,99CB36283: 151-153.
[30] Martz H.F. etc. Empirical Bayes estimation of the reliability of Nuclear- Power-Plant emergency diesel generators[J].Technometrics, 1996,Vol.38, No.1:11-24.
[31] Robbins H. The empirical Bayes approach to statistical decision problems[J]. Annual Mathematics Statistics,1964,35:1-20.
[32] Kurt Pom.The two-stage Bayesian method used for the T-Book application. Reliability engineering and system safety,1996,169-179.
[33] Barlow R. E., Scheuer E. M. Reliability Growth During a Development Testing Program[J]. Technometrics, 1966, 8(1): 53-60.
[34]何国伟.评估电子产品平均寿命的一种变动统计方法[J].电子学报, 1981,5: 70-74.
[35]周源泉,郭建英.故障分类时顺序约束指数可靠性增长的Bayes精确限[J].仪器仪表学报, 1999,12, 20(6): 626-629.
[36]张士峰,杨万君.异总体统计问题的Bayes分析[J].战术导弹技术, 2003,3: 33-37.
[37]周源泉.估计逐次提高试制产品精度的Bayes方法[J].电子学报, 1984,7, 12(4):51-56.
[38] MAZZUCHI T.A, SOYER R.A. Bayes attribute reliability growth model[C].//Proceedings Annual Reliability and Maintainability Symposium, 1991.322-325.
[39] VanDorp, J. R., Mazzuchi, T. A., Soyer, R. Sequential Inference and Decision Making for Single Mission Systems Development [J], Journal of Statistical Planning and Inference, August 15 1997, 62(2): 207-218.
[40] Erkanli A, Mazzuchi T A, Soyer R. Bayesian computations for a class of reliability growth models[J]. American Statistical Association and the American Society for Quality Technometrics, 1998, 40(1):14-23.
[41] MIL-HDBK-189(1981), Reliability Growth Management.
[42] MIL-HDBK-338(1984), Reliability Design Handbook for Electronic Equipment.
[43] Sanford A.D., Martin G.M. Simulation-based Bayesian estimation of an affine term structure model[J]. Computational Statistics & Data Analysis, 2005, Vol.49(2): 527-554.
[44] Kennedy M.C., Hagan A.O. Bayesian calibration of computer models[J]. Journal of the Royal Statistical Society.ser.B. Statistical menthodological. 2001, Vol.63(3): 425-464.
[45] Paulino C.D., Silva G., Achcar J.A. Bayesian analysis of correlated misclassified binary data[J]. Computational Statistics & Data Analysis, 2005, Vol.49(4): 1120-113.
[46] Vidal P.I., Branco M.D., Arellano-Valle R.B. Bayesian sensitivity analysis and model comparison for skew elliptical models[J]. Journal of Statistical Planning and Inference, 2006, Vol.136(10): 3435-3457.
[47] Strickland C.M., Catherine S.F., Martin G.M. Bayesian analysis of the stochastic conditional duration model[J]. Computational Statistics & Data Analysis, 2006, Vol.50(9): 2247-2267.
[48] Wendai W., Loman J. A new approach for evaluating the reliability of highly reliable systems[C]. Reliability and Maintainability Symposium, 2003. Annual.
[49] Chaudhuri G., Hu K., Afshar N. A new approach to system reliability[J].IEEE Transactions on Reliability. 2001, Vol.50(1): 75-84.
[50] Mahadevan S., Rebba R. Validation of reliability computational models using Bayes networks[J]. Reliability Engineering & System Safety, 2005,Vol.87 (2):223-232.
[51] Guida M., Pulcini G. Bayesian analysis of repairable systems showing a bounded failure intensity[J].Reliability Engineering & System Safety, 2006,Vol.91 (7): 828-838.
[52] Philippe W., Lionel J. Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)[J],Reliability Engineering & System Safety, 2006, Vol. 91(2): 149-162.
[53] Sarhan M. The Bayes procedure in exponential reliability family models using conjugate convex tent prior family. Reliability engineering & system safety[J]. European Safety and Reliability Association. 2001, Vol.71(1): 97-102.
[54] Rutten A.L.B., Stolper C.F., Lugten R.F.G. A Bayesian perspective on the reliability of homeopathic repertories[J]. 2006, Vol. 95(2): 88-93.
[55]李欣欣.基于Bayes变动统计的精度鉴定与可靠性增长评估研究[D].国防科技大学博士学位论文,2008.
[56]唐雪梅,张金槐.武器装备小子样试验分析与评估[M].北京:国防工业出版社,2000.
[57]王玲玲.指数分布下可靠性Bayes验证[J].华东师范大学学报(自然科学版), 1993, (3):1-10.
[58]张志华,姜礼平.指数型产品失效率鉴定试验的Bayes方案[J].应用概率统计, 2000, 16(1): 66-70.
[59]许志强,王艳.指数分布下失效率的Bayes验证试验抽样方案[J].长春工业大学学报, 2004, 25(1): 76-78.
[60]陈宜辉,姜礼平,吴树和.指数分布下Bayes鉴定试验方案[J]..运筹与管理, 2002, 11(2): 56-59.
[61]李秀良等,靶场雷达可靠性、可维性验证方法研究[J],舰船电子工程,1998,(3):43-46.
[62]张士峰. Weibull型产品的可靠性验证[J].国防科技大学学报, 2001, 23(4): 16-19.
[63]陈文华.威布尔分布下失效率的Bayes验证试验方法[J].机械工程学报, 2005. 41(12): 118-121.
[64]姜礼平,张志华.成败型产品的成功率鉴定试验的一种Bayes方法[J].工程数学学报, 2000, 17(4): 25-29.
[65]王国玉.电子系统小子样试验理论方法[M].国防工业出版社,2003.
[66] ZhangKun.The Bayesian Sequential Testing Analysis[J].Chinese Journal ofApplied Probability and Statistics, 1999, 15(3):287-293.
[67]周桃庚,沙定国.贝叶斯可靠性序贯验证试验方法[J].仪器仪表学报,2001,22(4):373-374.
[68]唐义良,王景芹.基于Bayes的量度继电器成功率可靠性验证试验[J].系统工程与电子技术,1999,021(008):60-63.
[69]申绪涧,戚宗锋,汪连栋等.正态分布未知参数的联合序贯验后加权检验方法[J].系统工程与电子技术,2004,26(6):744-746.
[70]邢云燕.小子样条件下武器装备可靠性与维修性指标验证方法[D].国防科技大学硕士学位论文,2005.
[71]金振中.战术导弹综合试验与Bayes评估方法研究[D].国防科技大学博士学位论文, 2007.
[72]王永杰,金振中,孙晓峰.小子样综合试验方法在舰空导弹武器系统设计定型中的应用[C].总装备部武器装备小子样试验方法研讨会论文集,2003.
[73]唐雪梅.武器装备小子样试验分析与评估[D].国防科技大学博士学位论文, 2008.
[74] Woodroofe M. W., Willard D., Singpurwalla N. D. PershingⅡFollow-on Test: Size Reduced by Sequential Analysis[R]. AD-A192136.
[75] Alok P., Ashok S., William J. Z. Bayes Estimation of the Linear Hazard-rate Model[J].IEEE Trans. on Reliability, 1993, 42(4):636-640.
[76] Sarhan A.. Bayes Estimation of the General Hazard Rate Model[J]. Reliability Engineering of System Safety, 1999, 66: 85-91.
[77]王之任.近代大型液体火箭发动机的特点[J].推进技术, 1991(4):23-25.
[78]庞敏.当前可靠性增长试验存在问题及建议[J].质量与可靠性, 2004(1).
[79]梅文华.可靠性增长试验[M].国防工业出版社,2003.
[80] ZHOU Yuan-quan, WENG Zhao-xi. AMSAA-Bise model[C] In:3rd Japan-China Symposium on Statistics. Tokyo, Japan: Soka University, 1989:179-182.
[81]周源泉,翁朝曦.可靠性增长[M].北京:科学出版社, 1992.
[82]周源泉.质量可靠性增长与评定方法[M].北京:北京航空航天大学出版社, 1997.
[83]田国梁. Gompertz模型可靠性增长的分析方法[J].强度与环境, 1989, 5: 22-29.
[84]田国梁.负二项分布可靠性增长的经典分析方法[J].强度与环境, 1990, 1: 28-33.
[85]谢红卫,宫二玲,贺勇军.时变结构多阶段任务系统的可靠度研究[J].国防科技大学学报, 1999, 05:44-48.
[86]赵宇,黄敏.变母体变环境数据的可靠性综合评估模型[J].北京航空航天大学学报, 2002, 28(5): 597-600.
[87]杜振华,赵宇,陈金盾.研制阶段产品可靠性综合评估技术研究[J].可靠性工程,2002, 4: 180-183.
[88]刘琦.液体火箭发动机可靠性增长试验评定方法研究[D].国防科技大学研究生院, 2003.
[89]胡明祥.鱼雷可靠性增长试验和分析方法研究[D].西北工业大学研究生院, 2006.
[90] Barlow R.E., Scheuer E.M. Reliability growth during a development testing program[J]. Technometrics, 1966, 8(1): 53-60.
[91] Weinrich M.C., Gross A.J. The Barlow-Scheuer reliability growth model from a Bayesian viewpoint[J]. Technometrics, 1978, 20(3): 249-254.
[92] Smith A.F.M. A Bayesian note on reliability growth during a development testing program[J]. IEEE Trans. Reliability, 1977, 26: 346-347.
[93] Fard N.S., Dietrich D.L. A Bayes reliability growth model for a development testing program[J]. IEEE Trans. Reliability, 1987, 36: 568-572.
[94] Fard N.S., Dietrich D.L. Comparison of attribute reliability growth models[C]. Proc. Ann. Reliability & Maintainability Symp, 1983: 24-29.
[95] Mazzuchi T.A., Soyer R. Reliability assessing and prediction during product development[C]. Proc. Ann. Reliability & Maintainability Symp, 1992: 468-474.
[96] Mazzuchi T.A., Soyer R. A Bayes method for assessing product-reliability during development testing [J]. IEEE Trans. Reliability, 1993, 42(3): 503-510.
[97] Somerrville I F, Dietrich D L, Mazzuchi T A. Bayes reliability analysis using the dirichlet prior distribution with emphasis on accelerated life testing run in random order[J]. Nonlinear Analysis, Theory Method&Application, 1997, 30(7): 4415-4423.
[98]周源泉,翁朝曦.可靠性评定[M].北京:科学出版社, 1990.
[99] Duane J.T. Learning curve approach to reliability monitoring[J]. IEEE Trans.Aerospace, 1964, 2: 563-566.
[100] Woods W.M., Drake J.E., Chandler J.D. Analysis and evaluation of discrete reliability growth models with and without failure discounting[R]. NPS55-88-013, US Naval Postgraduate School, 1988.
[101] Woods W.M. The effect of discounting failures and weighting data on the accuracy of some reliability growth models[C]. Proc. Ann. Reliability &Maintainability Symp, 1990: 200-204.
[102] Gross A.J., Kamins M. Reliability assessment in the presence of reliability growth [C]. Proc. Ann. Reliability & Maintainability Symp, 1968: 187-198.
[103] Virene E.P. Reliability growth and its upper limit [C]. Proc. Ann. Reliability & Maintainability Symp, 1968: 265-270.
[104] Kececioglu D, Jiang S, Vassiliou P. The modified Gompertz reliability-growth model [C]. Proc. Ann. Reliability & Maintainability Symp, 1994: 160-165.
[105] Duane J. T. Learning Curves Approach to Reliability Monitoring[J]. IEEE Transaction on Aerospace,1964: 563-566.
[106] Crow L H. Reliability Analysis for Complex Repairable System[R]. ADA0296, 1975.
[107] Guida M, Calabria R, Pulcini G. Bayes Inference for a Non-homogeneous Passion Process with Power Intensity Law[J]. IEEE Transaction on Reliability,1989, 38(4):603-609.
[108]周源泉,郭建英.可靠性增长模型的Bayes推断及其在发动机上的应用[J].推进技术, 2000, 21(1): 49-53.
[109]周源泉.阿里安运载火箭的可靠性增长分析[J].导弹与航天运载技术, 2000,247(5):25-29.
[110]周源泉,张立堂,刘振德.涡轮发动机的可靠性增长分析[J].推进技术, 1997,18(4): 496-499.
[111]周源泉,周百里,王家明.某型涡轮发动机的可靠性增长分析[J].推进技术, 1997,18(5): 17-21.
[112]周源泉,杨双进,顿赛英.中国长征系列运载火箭的可靠性分析[J].导弹与航天运载火箭技术,2002, 2:37-40.
[113]田国梁.二项分布的可靠性增长预测模型[J].强度与环境, 1991, 4: 17-25.
[114]田国梁.二项分布的可靠性增长模型[J].宇航学报, 1992, 1:55-61.
[115]宫二玲,谢红卫,李鹏波,蒋英杰.指数寿命可靠性增长评估中增长因子的确定方法[J].国防科技大学学报, 2008,06: 57-60.
[116]吴祺,闫志强,谢红卫.复杂系统可靠性增长的动态建模方法[J].计算机仿真, 2007,11:321-324.
[117] Yan Zhiqiang, Li Xinxin,Xie Hongwei,Jiang Yingjie.Bayesian synthetic evaluation of multistage reliability growth with instant and delayed fix modes[J]Journal of Systems Engineering and Electronics, 2008,06:229-236.
[118]党晓玲.柔性制造系统可靠性增长管理与分析技术研究[D].国防科技大学研究生院, 1999.
[119]张湘平.小子样统计推断与融合理论在武器系统评估中的应用研究[D].国防科技大学研究生院, 2003.
[120]冯静.小子样复杂系统可靠性信息融合方法与应用研究[D].国防科技大学研究生院, 2004.
[121]刘飞.固体火箭发动机可靠性增长试验理论及应用研究[D].国防科技大学研究生院, 2006.
[122]叶尔骅.具有可靠性增长的三项分布模型随机参量的贝叶斯估计[J].宇航学报,1985, 4: 31-35.
[123]陈世基.具有可靠性增长的二、三项分布模型参数Bayes估计的注记[J].福建师范大学学报(自然科学版),1983(1): 11-16.
[124]韩明.基于无失效数据的可靠性参数估计[M].北京:中国统计出版社, 2005.
[125]林达明.一个贝叶斯离散可靠性增长模型—多元Beta模型[J].汕头大学学报(自然科学版), 1996, 11(2): 34-43.
[126]张士锋,李荣.基于Dirichlet验前的Bayes可靠性分析[J].电子产品可靠性与环境试验,1999,6:12-15.
[127]喻天翔,宋笔锋,冯蕴文.基于Dirichlet先验分布的Bayes二项可靠性增长方法[J].系统工程理论与实践.2006.1:131-135.
[128]刘飞,窦毅芳,张为华.基于狄氏先验分布的固体火箭发动机可靠性增长Bayes分析[J].固体火箭技术.Vo.l 29,No.4,2006(11):239-242.
[129]刘飞,王中伟,张为华.指数寿命产品可靠性增长的Bayes分析[J].国防科技大学学报.Vol 28 No.4 2006:1-14:128-132.
[130] Guo-Ying Li, Qi-Guang Wu, Yong-Hui Zhao. ON BAYESIAN ANALYSIS OF BINOMIAL RELIABILITY GROWTH[J]. J.Japan Statist.Soc.Vol.32 No.1 2002:1-14.
[131]吴启光,李国英,赵勇辉.基于指数可靠性增长模型的一类新的先验分布[J].数学物理学报.Vol 23A, 2003:474-484.
[132] Edwards W., Lingdman H., Savage L. J. Bayesian Statistical Inference for Psychological Research[M]. Psychol. Rev. 70, 1963:193-242.
[133] Lindley D. V. Approximate Bayesian Methods Bayesian Statistics II[M].Valencia Press,1980: 223-245.
[134] Tierney L., Kadane J. B. Accurate Approximations for Posterior Moments andMarginals[R]. Technical Report #326, Department of Statistics, Carnegie Mellon University, Pittsburgh, 1984.
[135] Naylor J. C., Smith A. M. Applications of Method for the Efficient Computationof Posterior Distributions[J]. Appl. Statist.31, 1982: 214-225.
[136] Ibrahim J G, Chen Minghui, Sinha D. Bayesian Survival Analysis [M]. NewYork Berlin Heidelberg, 2001. 13-17.
[137] Scollnik P M. Actuarial modeling with MCMC and BUGS[J]. North American Actuarial Journal, 2001, 5(2): 96-125.
[138] David S, Andrew T, Nicky B. BUGS 0.5: Bayesian inference Using Gibbs Sampling Manual[M].version ii.Cambridge, U. K.: MRC Biostatistics Unit,1996.
[139]茆诗松,王静龙,濮晓龙.高等数理统计[M].高等教育出版社和施普林格出版社,1998.
[140]曹晋华,程侃.可靠性数学引论[M].高等教育出版社,2006.
[141] Mihir Arjunwadkar, Marc Fasnacht. A Bayesian analysis of Monte Carlo correlation times for the two-dimensional Ising model[J].Physica A: Statistical Mechanics and its Applications.2003, Vol.323:487-503.
[142] Awad E.G. Bayes estimation of parameters in a three non-independent component series system with time dependent failure rate[J].Applied Mathematics and Computation, 2004, Vol.158(1):121-132.
[143]杨明,毕涌,雷英杰.混合Weibull分布参数估计的EM算法[J].华北工学院学报,2003, 24(4): 353-356.
[144] Carlin B.P.,Louis T.A. Bayes and Empirical Bayes Methods for Data Analysis [M]. New York: Chapman and Hall, 2000.
[145] Gilks W.R, Richardson S. Markov Chain Monte Carlo in Practice[M]. New York:Chapman and Hall,1996.
[146]唐年胜,韦博成.非线性再生散度模型[M].北京:科学出版社,2007.
[147] Tillman F. A., Kuo W., Hwang C. L. Bayesian Reliability & Avilability Review [J].IEEE Trans.on Reliability, 1982, 31(4): 362-372.
[148]林静,韩玉启,朱慧明.基于MCMC稳态模拟的Weibull共享异质性模型及其可靠性应用[J].中国工程科学. 2006,8(2):55-60.
[149]龚庆祥,赵宇,顾长鸿.型号可靠性工程手册[M].北京:国防工业出版社, 2007.
[150]朱曦全.航天产品的可靠性增长试验方法[J].导弹与航天运载技术,2006.1:58-61.
[151]朱曦全.可靠性增长试验载人航天运载火箭电气产品研制过程中的应用[J].导弹与航天运载技术,2004.1:61-66.
[152]安伟光,胡经畬.可靠性增长试验方法的研究[M].哈尔滨:哈尔滨工业大学出版社,1996.
[153] Perter Congdon.Applied Bayesian Modeling[M]. New York: John Wiley&Sons,Itd. 2003.
[154] Morris C. N. Natural exponential families with quadratic variance function[M]. Statistica ltheory. Ann.Statist., 1983,11:515-529.
[155] Walter G.G, Hamedani G.G. Bayes empirical estimation for discrete exponential families[J]. Ann.Inst.Statist.Math.,1989,41:101-119.
[156]严惠云.Bayes理论在二项分布可靠性分析中的应用[D].西北工业大学研究生院, 2007.
[157]王玮,周海云,尹国举.使用混合Beta分布的Bayes方法[J].系统工程理论与实践. 2005,9: 142-144.
[158] Nader Ebrahimi.Testing Exponentiality of the Residual Life Based On Dynamic Kullback-Leibler Information[J].IEEE TRANSACTIONS ON RELIABILITY, VOL. 47, NO. 2, 1998 JUNE:197-201.
[159]任开军,吴孟达.先验分布融合的KULLBACK信息方法[J].装备指挥技术学院学报, 2002, 13(4): 90-92.
[160] Pullkkinen U. Methods for combination of expert judgements[J]. Reliability Engineering and System Safety,1993:40:111-118.
[161] Robinson D.G, Dietrich D. A New Nonparametric Growth Model[J]. IEEE Trans. onReliability, 1987, 36(10): 411-418.
[162] Robinson D.G, Dietrich D. A Nonparametric-Bayes Reliability-growth Model[J]. IEEE Trans. on Reliability, 1989, 38(11):591-59.
[163] Athanasios Kottas.Nonparametric Bayesian survival analysis using mixtures ofWeibull distributions[J].Journal of Statistical Planning and Inference. 136 (2006) 578-596.
[164] Luz Callea M, Guadalupe Gomez. Nonparametric Bayesian estimation from interval-censored data using Monte Carlo methods[J].Journal of Statistical Planning and Inference .98 (2001) 73-87.
[165] Guida M, Pulcini G. Bayesian reliability assessment of repairable systems during multi-stage development programs[J]. IIE Transactions. 2005, 37(11), 1071-1081.
[166] Guida M, Pulcini G. Bayesian reliability-growth modeling of repairable mechanical systems[C].// Proceedings of the 3rd International Conference on Mathematical Methods in Reliability-MMR2002, Trondheim(Norway), 2002, 263-266.
[167] Benton A.W, Crow L.H. Integrated reliability growth testing[C],1989 proe. Annual Reliability&Maintainability Symp,1989,160-166.
[168] IEC 1164, Reliability growth-statistical test and estimation method[S],1995.
[169] Kenneth J.F, Ali Mosleh. An approach to quantifyingReliability-GrowthEffectiveness[J].1995 Proceedings Annual Reliability and Maintainability Symposium,166-173.
[170] http://www.mrc-bsu.com.ac.uk/bugs/the bugs project.
[171] Nelson W B.Accelerated testing statiscal model test plan data analysis[M].New York:John Wiley & Sons,Inc.1990.
[172] Meeker,M.Q.,ESCOBAR, L.A. Statiscal Methods for Reliability Data[M].New York:John Wiley & Sons,Inc.1998.
[173]何国伟.可靠性试验技术[M].北京:国防工业出版社,1995.
[174] Kleyner A,et al. Bayesian Techniques to Reducee the Sample Size in Automotive Electronics Attribute Testing[J].Microelectronis Reliability,1997, 37(6): 879-883.
[175]张士峰.成败型产品可靠性的Bayes评估[J].兵工学报, 2001, 22(2): 238-240.
[176]苏德清.可靠性标准及其应用[M].北京:国防工业出版社,1988.