民机产品可靠性评估技术研究
|
摘要
航空工业是我国的战略性高科技产业,我国自主研制的新支线飞机ARJ21完成总装并已开始试飞,C919大飞机项目也已在紧锣密鼓的进行当中,所有的这些对于提升我国航空工业在国民经济中的地位及国际影响力有着举足轻重的作用。民机产品可靠性评估在整个飞机的方案论证、工程研制、投入生产及交付使用、维护等各个阶段都非常重要,可以衡量产品的可靠性是否达到了预期的设计目标,验证产品可靠性设计的合理性,指出产品的薄弱环节,为改进产品的设计、制造、工艺及维修优化指明方向。因此,民机产品可靠性评估技术的研究对于我国民机事业的发展具有非常重要的意义。本文以此为背景,针对民机研制和使用过程中所采集的可靠性信息特点,深入研究了相关的可靠性评估理论和技术,同时开发了实用化的民机信息数据库和可靠性评估系统。主要研究内容和结论包括:
(1)研究了基于小样本寿命信息的民机产品可靠性分析评估方法。根据民机研制过程中的小样本可靠性寿命信息,对其进行分布的假设检验,并充分利用专家的经验信息,用基于模糊贝叶斯的方法进行融合,判断产品的寿命分布,并计算其可靠性参数;若在评估过程中没有专家经验信息或其他信息可以利用时,提出将机器智能研究领域的统计学习理论运用到小样本寿命信息的可靠性分析中去,在样本数目有限的情况下,可以获得很高的评估精度,充分显示了其在小样本可靠性分析方面具有的独特优势。
(2)传统的可靠性信息仅仅记录产品的失效数量和时间,而高可靠性长寿命民机产品的试验和使用包含着准确、丰富且与寿命紧密相关的性能退化信息,故本文从产品性能参数的变化入手,对产品的性能数据进行连续测量,获得退化数据,利用支持向量机算法在函数逼近中的优秀性能对产品的退化轨迹进行建模,获得产品在失效阈值下的寿命数据,分析了性能退化轨迹与寿命分布之间的关系,进而对产品的可靠性进行评估,由此可以在较少的退化样本下对高可靠长寿命产品的可靠性进行分析,具有广泛的适应性。
(3)在民机复杂系统可靠性评估方面,为了充分挖掘产品多种来源的可靠性信息,提出了基于环境因子和信息熵综合法的可靠性评估技术,将不同时间环境下的试验信息融合为工作环境下的等效试验信息,将零部件和子系统的工作环境试验信息融合为系统的等效试验信息,最后利用系统的综合试验信息进行可靠性评估。用此方法对ARJ21襟翼收放系统的可靠性进行了评估,经工程实践验证,效果很好。
(4)民机发动机的寿命与可靠性管理是一项非常重要的工作。本文针对发动机的多个状态参数,提出了采用以单一寿命预测模型为基础,由能够综合利用不同模型带来的有效信息的免疫粒子群组合预计方法(IA-PSO方法)对发动机的在翼寿命进行预测,在此基础上对在翼寿命的可靠性分布进行了拟合优度检验,得到了寿命的分布及相应的分布参数。并提出了民航发动机使用时的在翼寿命控制方法。
(5)最后,从民机产品可靠性评估的实际问题出发,根据论文依托项目的需求,结合先进的数据库及应用程序设计技术,开发了一套民机产品可靠性评估系统RASAP。文中的理论和方法及开发的系统在西安飞机设计研究院取得了良好的应用效果。这一工作为我国在民机研制及运行使用方面的可靠性评估分析及维修优化提供了方便、实用的方法和工具。
Aviation industry is one of the most important hi-technology strategy industries in our county. The new ARJ 21(Advanced Regional Jet 21st) has already finished its assembly and flight test successfully; the C919 commercial aircraft project is also in progress. All these have been done is of great importance in improving the position in national economy and international power of our country’s aviation industry. The reliability assessments for civil aircraft products are significant in the process of total aircraft’s project demonstration, engineering research & development, production, consignation into use and maintenance. It can evaluate whether the productions’reliability reaching the original object, validate the rationality of reliability design, point out the productions’weakness and demonstrate direction in elevating the civil aircraft’s design, manufacturing, technics and maintenance optimization. Therefore, Researches on technique of reliability assessment for civil aircraft productions are of great significance to develop our county’s civil aircraft projects. With the background of the situation, according to the reliability information’s characteristics gathering in the aircraft design and operation’s process, this paper puts emphasis on the researches about the theory and technique of reliability assessments, at the same time we have developed practicable aircraft information database and reliability assessments system. The main research contents and conclusions include:
(1) The reliability assessment method for civil aircraft productions based on small sample life information is proposed. According to the small sample reliability life information gathered during the aircraft’s design and operation process, the paper has tested the hypothesis of distribution, proposed a fuzzy-bayes method fusing the experts experience information, evaluated the productions’life distribution and finally computed the reliability parameters; When there are no experts experience information or other information available, research on the reliability analysis with small sample life information is carried out by using Statistical Learning Theory in machine intelligence research fields. Under the circumstances of limited sample, the method can achieve high precision in the assessment, fully indicating the particular advantages in the reliability analysis of small sample.
(2) Traditional reliability information is merely recorded the failure number and time. However the tests and operation of high-reliability and long-life civil aircraft products include accurate, ample performance degradation information related to the life closely. So, in order to make full use of the performance degradation data, a Support Vector Machine (SVM) based reliability method is established by modeling the degradation track, acquiring the degradation data, analyzing the relationship between the track and the life distribution, evaluating the products’reliability. This method can be used to analyze the high-reliability and long-life civil aircraft products on the condition of less degradation sample and has comprehensive applicability.
(3) On the aspect of assessing complex systems of aircraft products, in order to fully mine the reliability information from all sources of databases, the reliability assessment technique based on the environment factor and integrated information entropy is proposed. The method fuse the test information gathered under different circumstances into equivalent test information of working circumstances and evaluate the reliability of aircraft products based on the systems integrated test information eventually. In this paper, the method is used in assessing the reliability of ARJ 21’s flap operation system and has good effects through engineering practice.
(4) The life and reliability management of civil aeroengine is an important work. In this paper, according to several aeroengine parameters, a combined prediction method named IA-PSO is brought forward to predicting the life on wing. Multiple single models was based in this method, Particle Swarm Optimization algorithm (PSO) improved by Immunity Algorithm (IA) was used to set weights for every single model which attended combing, and different kinds of information form different single models could be integrated by it. Based on the prediction, the distribution of the life on wing and its parameters can be achieved. Finally, a method which is used to control the life on wing is established.
(5) According to the requirements of the projects this paper relied on, a set of reliability assessment system of aircraft products (RASAP) is developed by using the technology of advanced database and application in order to solve the problems of reliability assessments. The theory and methods including the developed system mentioned in this paper have received high estimation. The work provides convenient and practical methods and tools for the reliability assessments and maintenance optimization in the fields of our county’s civil aircraft design and operation.
(1)研究了基于小样本寿命信息的民机产品可靠性分析评估方法。根据民机研制过程中的小样本可靠性寿命信息,对其进行分布的假设检验,并充分利用专家的经验信息,用基于模糊贝叶斯的方法进行融合,判断产品的寿命分布,并计算其可靠性参数;若在评估过程中没有专家经验信息或其他信息可以利用时,提出将机器智能研究领域的统计学习理论运用到小样本寿命信息的可靠性分析中去,在样本数目有限的情况下,可以获得很高的评估精度,充分显示了其在小样本可靠性分析方面具有的独特优势。
(2)传统的可靠性信息仅仅记录产品的失效数量和时间,而高可靠性长寿命民机产品的试验和使用包含着准确、丰富且与寿命紧密相关的性能退化信息,故本文从产品性能参数的变化入手,对产品的性能数据进行连续测量,获得退化数据,利用支持向量机算法在函数逼近中的优秀性能对产品的退化轨迹进行建模,获得产品在失效阈值下的寿命数据,分析了性能退化轨迹与寿命分布之间的关系,进而对产品的可靠性进行评估,由此可以在较少的退化样本下对高可靠长寿命产品的可靠性进行分析,具有广泛的适应性。
(3)在民机复杂系统可靠性评估方面,为了充分挖掘产品多种来源的可靠性信息,提出了基于环境因子和信息熵综合法的可靠性评估技术,将不同时间环境下的试验信息融合为工作环境下的等效试验信息,将零部件和子系统的工作环境试验信息融合为系统的等效试验信息,最后利用系统的综合试验信息进行可靠性评估。用此方法对ARJ21襟翼收放系统的可靠性进行了评估,经工程实践验证,效果很好。
(4)民机发动机的寿命与可靠性管理是一项非常重要的工作。本文针对发动机的多个状态参数,提出了采用以单一寿命预测模型为基础,由能够综合利用不同模型带来的有效信息的免疫粒子群组合预计方法(IA-PSO方法)对发动机的在翼寿命进行预测,在此基础上对在翼寿命的可靠性分布进行了拟合优度检验,得到了寿命的分布及相应的分布参数。并提出了民航发动机使用时的在翼寿命控制方法。
(5)最后,从民机产品可靠性评估的实际问题出发,根据论文依托项目的需求,结合先进的数据库及应用程序设计技术,开发了一套民机产品可靠性评估系统RASAP。文中的理论和方法及开发的系统在西安飞机设计研究院取得了良好的应用效果。这一工作为我国在民机研制及运行使用方面的可靠性评估分析及维修优化提供了方便、实用的方法和工具。
Aviation industry is one of the most important hi-technology strategy industries in our county. The new ARJ 21(Advanced Regional Jet 21st) has already finished its assembly and flight test successfully; the C919 commercial aircraft project is also in progress. All these have been done is of great importance in improving the position in national economy and international power of our country’s aviation industry. The reliability assessments for civil aircraft products are significant in the process of total aircraft’s project demonstration, engineering research & development, production, consignation into use and maintenance. It can evaluate whether the productions’reliability reaching the original object, validate the rationality of reliability design, point out the productions’weakness and demonstrate direction in elevating the civil aircraft’s design, manufacturing, technics and maintenance optimization. Therefore, Researches on technique of reliability assessment for civil aircraft productions are of great significance to develop our county’s civil aircraft projects. With the background of the situation, according to the reliability information’s characteristics gathering in the aircraft design and operation’s process, this paper puts emphasis on the researches about the theory and technique of reliability assessments, at the same time we have developed practicable aircraft information database and reliability assessments system. The main research contents and conclusions include:
(1) The reliability assessment method for civil aircraft productions based on small sample life information is proposed. According to the small sample reliability life information gathered during the aircraft’s design and operation process, the paper has tested the hypothesis of distribution, proposed a fuzzy-bayes method fusing the experts experience information, evaluated the productions’life distribution and finally computed the reliability parameters; When there are no experts experience information or other information available, research on the reliability analysis with small sample life information is carried out by using Statistical Learning Theory in machine intelligence research fields. Under the circumstances of limited sample, the method can achieve high precision in the assessment, fully indicating the particular advantages in the reliability analysis of small sample.
(2) Traditional reliability information is merely recorded the failure number and time. However the tests and operation of high-reliability and long-life civil aircraft products include accurate, ample performance degradation information related to the life closely. So, in order to make full use of the performance degradation data, a Support Vector Machine (SVM) based reliability method is established by modeling the degradation track, acquiring the degradation data, analyzing the relationship between the track and the life distribution, evaluating the products’reliability. This method can be used to analyze the high-reliability and long-life civil aircraft products on the condition of less degradation sample and has comprehensive applicability.
(3) On the aspect of assessing complex systems of aircraft products, in order to fully mine the reliability information from all sources of databases, the reliability assessment technique based on the environment factor and integrated information entropy is proposed. The method fuse the test information gathered under different circumstances into equivalent test information of working circumstances and evaluate the reliability of aircraft products based on the systems integrated test information eventually. In this paper, the method is used in assessing the reliability of ARJ 21’s flap operation system and has good effects through engineering practice.
(4) The life and reliability management of civil aeroengine is an important work. In this paper, according to several aeroengine parameters, a combined prediction method named IA-PSO is brought forward to predicting the life on wing. Multiple single models was based in this method, Particle Swarm Optimization algorithm (PSO) improved by Immunity Algorithm (IA) was used to set weights for every single model which attended combing, and different kinds of information form different single models could be integrated by it. Based on the prediction, the distribution of the life on wing and its parameters can be achieved. Finally, a method which is used to control the life on wing is established.
(5) According to the requirements of the projects this paper relied on, a set of reliability assessment system of aircraft products (RASAP) is developed by using the technology of advanced database and application in order to solve the problems of reliability assessments. The theory and methods including the developed system mentioned in this paper have received high estimation. The work provides convenient and practical methods and tools for the reliability assessments and maintenance optimization in the fields of our county’s civil aircraft design and operation.
引文
[1]郭永基.可靠性工程原理[M].北京:清华大学出版社, 2002.
[2]吴静敏.民用飞机全寿命维修成本控制与分析关键问题研究[D]. [博士学位论文],南京,南京航空航天大学, 2006.
[3]毛欣,基于维修的民用航空器部件可靠性管理研究[D]. [硕士学位论文],南京,南京航空航天大学, 2007.
[4] Jianyin Zhao, Fang Liu. Reliability assessment of the metallized film capacitors from degradation data [J]. Microelectronics Reliability, 2007, 47(2):434-436.
[5] Zhiwei Guo, Guangchen Bai. Application of Least Squares Support Vector Machine for Regression to Reliability Analysis [J]. Chinese Journal of Aeronautics, 2009, 22(2):160-166.
[6] David Mercier, Benjamin Quost, Thierry Den?ux. Refined modeling of sensor reliability in the belief function framework using contextual discounting [J]. Information Fusion, 2008, 9(2):246-258.
[7]陈云翔.可靠性与维修性工程[M].北京:国防工业出版社, 2007.
[8] Torre M. New ASME Turbine Conference Focuses on Aero Engine Life Management. [EB/OL].http://www.asme.org/NewsPublicPolicy/PressReleases/New_Turbine_Conference.cfm. 2008-5-8.
[9] Zhao J, Tang J. A generalized random variable approach for strain-based fatigue reliability analysis [J]. Journal of Pressure Vessel Technology, 2000, 122(2):156-161.
[10] Huang W, Rronaland G A. A generalized SSI reliability model considering stochastic loading and strength aging degradation [J]. IEEE Trans. On Reliability, 2004, 53(1):77-82.
[11]孙权,赵建印,周经伦.复合应力作用下强度退化的应力-强度干涉模型可靠性统计分析[J].计算力学学报, 2007, 24(3):358-361.
[12]谢里阳,李翠玲.应力-强度干涉模型在系统失效概率分析中的应用及相关问题[J].机械强度, 2005, 27(4):492-497.
[13] Fatemi A, Yang L. Cumulative fatigue damage and life prediction theoris: a survey of the state of the art for homogeneous materials [J]. International Journal of Fatigue, 1998, 20(1):9-34.
[14] Rao J S, Pathak A and Chawla A. Blade life: a comparison by cumulative damage theories [J]. Journal of Engineering for Gas Turbines and Power, 2001, 123(4):886-892.
[15] Khen R, Altus E. Micro-macro relations for fatigue crack growth[J]. Mechanics of Materials, 1995, 19(2-3):89-101.
[16]董聪.现代结构系统可靠性理论及其应用[M].北京:科学出版社,2001.
[17]安伟光,赵维涛,严心池.不完整结构系统同时考虑强度和刚度的可靠性分析[J].工程力学, 2005, 22(4):58-61.
[18] Lee E T, Wang J W. Statistical Methods for Survival Data Analysis [J]. Technometrics, 2003, 45(4):372-373(2).
[19] Marcorin A J, Abackerli A J. Field Failure Data: an Alternative Proposal for Reliability Estimation [J]. Quality and Reliability Engineering International, 2006, 22(7):851-862.
[20] Ghosh J K, Mohan Delampady, Tapas Samanta. An Introduction to Bayesian Analysis: Theory and Methods [M]. Springer, 2006.
[21] Phillips M J. Bayesian estimation from censored data with incomplete information [J]. Quality and Reliability Engineering International, 2004, 20:237-245.
[22] John Quigley, Lesley Walls. Confidence intervals for reliability-growth models with small sample-sizes [J]. IEEE Transactions on Reliability, 2003, 52(2):257-262.
[23]冯静,周经纶.基于Bayes-模糊逻辑算子的小子样可靠性信息融合方法[J].航空动力学报, 2008, 23(9):106-109.
[24]茆诗松,王玲玲,蹼晓龙.威布尔分布场合无失效数据的可靠性分析[J].应用概率统计, 1996, 12(1):95-107.
[25] Software R. Reliability: A Practitioner’s Guide [M].陈晓彤等译.北京:北京航空航天大学出版社, 2005.
[26] Dey, D K, Lee, T M. Bayes computation for life testing and reliability estimation [J]. IEEE Transactions on Reliability, 1992, 41(4):621-626.
[27] Mroczkowski, R S, Maynard, J M. Estimating the reliability of electrical connectors [J]. IEEE Transactions on Reliability, 1991, 40(5):507-512.
[28] Thiagarajah, K R. Homogeneity tests for scale parameters of 2-parameter exponential populations under time censoring [J]. IEEE Transactions on Reliability, 1995, 44(2):297-301.
[29] Chun Kin, C Seraidaridia, C I. et al. Sequential sampling plans for early-life reliability assessment [A]. Proceedings Annual Reliability and Maintainability Symposium [C]. 1997:131-135.
[30] Gibson, D. Statistical reliability prediction [M]. International Integrated Reliability Workshop. 1995:161.
[31]葛广平.我国加速寿命试验研究的现状与展望[J].数理统计与管理, 2000, 19(1):25-29.
[32] Nelson, W B. Accelerated Life Testing-Step-Stress Models and Data Analysis [J]. IEEE Transactions on Reliability, 1980, 29(2):103-108.
[33]宋保维.系统可靠性设计与分析[M].西安:西北工业大学出版社, 2008.
[34]卢明银,徐人平.系统可靠性[M].北京:机械工业出版社, 2008.
[35]胡昌寿.可靠性工程——设计、试验、分析、管理.北京:宇航出版社, 1989.
[36] Box G E P, Tiao G C. Bayesian Inference in Statistical Analysis [M]. Reading, Addison-Wesley, 1973.
[37] Lindley D V, Smith A F M. Bayes estimates for the linear model. J.Roy. Statist. Soe. (Ser.B), 1972, 34: l-41.
[38] DeFinettiB. Theory of Probility [M]. New York: Wiley, 1975.
[39] Berger J O. Statistical decision theory and Bayesian analysis [M]. NewYork: Springer-Verlag, 1985.
[40]金星,洪延姬,沈怀荣等.可靠性数据计算与应用[M].北京:国防工业出版社, 2003: 58-69.
[41] Martz H F, Waller R A. Bayesian Reliability Analysis [M], John Wiley & Sons (New York), 1982.
[42]周源泉,翁朝曦著.可靠性评定[M].北京:科学出版社, 1990
[43]张士峰,樊树江,张金槐.成败型产品可靠性的Bayes评估[J].兵工学报, 2001, 22(5):238-240.
[44]韩明,定时截尾指数分布先验数的适合域[J],数理统计与应用概率, 1997, 12(l).
[45] Cuirong Ren, Dongchu Sun, Dipak K. Dey. Bayesian and frequentist estimation and prediction for exponential distributions [J]. Journal of Statistical Planning and Inference, 2006, 136:2873-2897.
[46]周源泉,刘文生.双参数指数分布的可靠性评估(Ⅰ) [J].质量与可靠性, 2004(1):5-10.
[47]周源泉,刘文生.双参数指数分布的可靠性评估(Ⅱ) [J].质量与可靠性, 2004(2): 19-24.
[48] Ahmed A Soliman, A H Abd Ellah, K S Sultan. Comparison of estimates using record statistics from weibull model: Bayesian and non-Bayesian approaches [J]. Computational Statistics & Data Analysis, 2005, 12.
[49]张金槐,唐雪梅. Bayes方法(第二版) [M].长沙:国防科技大学出版社, 1992.
[50] Pate-Cornell M E, Uncertainties in rich analysis: six level of treatment[J]. Relibaility Engineering and system Safety, 1996, 54:2-3.
[51] Coolen F P A. On Bayesian reliability analysis with informative priors and cersoring [J]. Reliability Engineering and System Safety, 1996, 53:91-98.
[52] Singpuwralla N D, Song M S. Reliability analysis using Weibull life time data and expert opinion [J]. IEEE Transaction on Reliability, 1988, 37(3).
[53] Coolen F P A, Newby M J. Bayesian reliability analysis with imprecise prior probabilities[J]. Reliability Engineering and Safety, 1994, 43:75-85.
[54] Lichtenstein S, Newman J R. Empirical scaling of common verbal phrases associated with numerical probabilities [J]. Psychonometric Science, 9(10):563-564.
[55] Bilal M A. A Practical guide on conducting expert-opinion elicitation of probabilities and consequences for corps facilities [R]. IWR Report 01-R-01, 2001.
[56]路晓辉.指数模型Bayes评定中的验前分布[J].北京理工大学学报, 1990, 10(1).
[57] Erto p. New practical Bayes estimates for the 2-parameter Weibull distribution [J]. Microelectronis reliability, 1994, 34(5):789-802.
[58] Calabrla R, Pulcini G. An engineering approach to Bayes estimation for the Weibull distribution [J]. Microelectron Reliability, 1994, 24(5).
[59] Singh S K. Bayes interval for the Weibull parameters utilizing guessed estimates [J]. Microelectronics Reliability, 1993, 33(6).
[60] Elperin T, Gertsbakh I. Bayes credibility estimation of an Exponential parameter for random censoring&incomplete information [J]. IEEE Transaction on Reliability, 1990, 39(2).
[61] Ram C T, Yang Y Z, Jyoti N Z. Bayes estimation for the pareto failure-model using gibbs samling [J]. IEEE Transaction on Reliability, 1996, 43(3).
[62] Arturo J F. Bayesian inference from type II doubly cersored Raleigh data [J]. Statistics&Probability Letters, 2000, 48:393-399.
[63] Robbins H. The empirical Bayes approach to statistical decision Problems [J]. Annual Mathematics Statistics, 1964, 35:1-20.
[64] Hong-bo Zhao. Slope reliability analysis using a support vector machine [J]. Computers and Geotechnics, 2008, 35(3).
[65] Ping-Feng Pai. System reliability forecasting by support vector machines with genetic algorithms [J]. Mathematical and Computer Modelling, 2006, 43(4)
[66] Martz H F, Waller R A. A Bayesian Zero-Failure Reliability Demonstration Testing Procedure [J]. Jounral of Quality Technology 1979, 11(3):128-137
[67]赵宇.可靠性数据分析教程[M].北京:北京航空航天大学出版社, 2009.
[68]韩明.无失效数据的可靠性分析[M].北京:中国统计出版社, 1999.
[69] Gerstsbackh, I B, Kordonskiy, et al. Models of Failure [M]. New York: Wiley. 1969.
[70] Nair V N. Discussion of Estimation of Relibaility in Field Perofrmance Sutdies by J.D. Kalbfleisch and J.F. Lwaless, Technometrics, 1988, 30:379-383
[71] Feinberg, A A, Widom A. Connecting Parametric Aging to Catastrophic Failure Through Thermodynamics [J]. IEEE Transaction on Reliability, 1996, 45(1):28-33.
[72] L Gallais, Natoli, J Y, et al. Statistical study of single and multiple pulse laser-induceddamage in glasses [J]. Optics Express, 2002, 10(25): 2-6.
[73] Viviane Ribeiro Branco de Oliveiral, E A O C. Comparison of Methods to Estimate the Time-to-failure Distribution in Degradation Test [J]. Quality and Reliability Engineering Internation. 2004, (20):363-373.
[74] Meeker W Q, Hamada M. Statistical Tools of the Rapid Development&Evaluation of High-relibaility Products [J]. IEEE Transaction On Reliability, 1995, 44(2):187-198.
[75] Lu C J, Meeker W Q, Escobar L A. A Comparison of Degradation and Failure-time analysis Methods for Estimating a Time-failure Distribution [J]. Statistical Sinica, 1996, 6: 531-546.
[76]姚增起.系统退化和系统可靠性研究[D]. [博士学位论文],合肥:中国科学院自动化研究所, 1988.
[77]庄东辰.退化失效模型及其统计分析[D]. [博士学位论文],上海:华东师范大学研究生院, 1994.
[78] Chao M T. Degradation Analysis and Related Topics: Some Thoughts and a Review, Proc. Natl. Sci. Counc. ROC(A), 1999, 23(5):555-566.
[79] Lu J C, Park J, Yang Q. Statistical Ineference of A Time-to-Failure Distribution Derived From Linear Degradation Data [J]. Technometrics, 1997, 39:391-400.
[80] Huang W. Relibaility Analysis Considering Product Perofmrance Degradation [D]. [P.h.D Thesis], The Universiyt of Arizona, 2002.
[81] Su C, Lu J C, Chen D, et. al. A random Coefficient Degradation Model With Random Sample Size [J]. Lieftime Data Analysis, 1999, 5:173-183.
[82] Robinson M, Crowder M. Bayesian Methods for a Growth-curve Degradation Model with Repeated Measures [J]. Lieftime Data Analysis, 2000, 6:357-374.
[83] Wu S Y, Tsai T R. Estimation of Time-to-failure Distribution Derived From a Degradation Model Using Fuzzy Clustering [J]. Quality and Reliability Engineering Intenrational, 2000, 16: 261-267.
[84] Gopikrishnan A. Reliability Inference Based on Degradation and Time to FailureData: Some Models, Mehtods and Efficiency Comparisions [D]. [P.h.D Thesis], The University of Michigan, 2004.
[85] Meeker W Q, Escobar L A. Statistical Methods for Reliability Data [M]. John Wiley&Sons, 1998.
[86] Lu C J, Meeker W Q. Using Degradation Measures to Estimate a Time-to-failure Distribution [J]. Technometrics, 1993, 35(2):161-174.
[87] Wilson S P, Taylor D. Reliability Assessment From Fatigue Micro-crack Data [J], IEEE Transactions On Reliability. 1997, 46(2): 165-171.
[88] Place C S, Sturtt J E, Allsopp K, et al. Reliability prediction of Helicopter Transmission System Using Stress-strength Interference with Underlying Damage Accumulation [J]. Quality and Reliability Engineering International, 1999, 15:69-78.
[89] Takeda E, Suzuki N. An Emprical Model for Device Degradation due to Hot-Carrier Injection [J]. IEEE Trans. On Elect Device Lett EDL, 1983, 4:111.
[90] Chan C K, Boulanger M, Tortorella M. Analysis of Parameter-degradation Data Using Life-data Analysis Programs [A]. Proceedings Annual Reliability and Maintainability Symposium [C], 1994, 288-291.
[91] Carey M B Koenig, R H. Reliability Assessment Based on Accelerated Degradation: A Case Sutdy [J]. IEEE Transactions On Reliability, 1991, 40(5):499-506.
[92] Meeker W Q, LuValle M J. An Accelerated Life Test Model Based on Relibaility Kinetics [J]. Technometrics, 1995, 37:133-146.
[93] Ramirez J G, Gore W L, Johnston G. New Methods for Modeling Relibaility Using Degradation Data [J]. Statistics Data Analysis and Data Mining, 2001, 263-226.
[94] Nelson, W B. Anaiysis of Performance-degradation data from accelerated tests [J]. IEEE Tansactions on Reliablity, 1981, 30(2):149-154.
[95] E Ioannides, E Beghini, G Bergling, et al. Cleanliness and its importance for bearing performance [J]. Ball Bearing Journal, 1993:242-248.
[96]张永强,刘琦,周经伦..小子样条件下基于指数退化轨道的性能可靠性评定[J].装备指挥技术学院学报, 2007, (1).
[97]邓爱民,陈循,张春华等.基于性能退化数据的可靠性评估[J].宇航学报, 2006, (3).
[98]庄钊文,郁文贤,王浩等.信息融合技术在可靠性评估中的应用[J].系统工程与电子技术, 2000, 22(3):75-80.
[99] Buehler R J. Confidence limits for the product of two binomial parameters [J]. Journal of American Statistical Association. 1957, 52: 482-493.
[100] Crowder M J, Kimber A C, Smith R L, Sweeting T J. Statistical Analysis of Reliability Data [M]. Norwell: Chapman&Hall, 1991.
[101] Easterling R G. Approximate confidence intervals for system reliability [J]. Journal of American Statistical Association. 1972, 67:220-222.
[102] Winterbottom A. Asymptotic expansions to improve large sample confidence intervals for system reliability [J]. Biometrica, 1980, 67:351-357.
[103] Winterbottom A. The interval estimation of system reliability from component test data [J]. Operations Research. 1984.
[104] Mann N L , Grubbs F E. Approximate optimum confidence bounds for system reliabilitybased on component test data [J]. Technometrics, 1974, 16:335-347.
[105] Madansky A. Approximate confidence limits for the reliability of series and parallel systems [J]. Technometrics, 1965, 7: 495-503.
[106] Martz H F, Duran B S. A comparison of three methods for calculating lower confidence limits on system reliability using binomial component data [J]. IEEE Transactions on Reliability, 1985, R-34:143-154.
[107] Willits C J, Dietz D C , Moore A H. Series-system reliability estimation using very small binomial samples [J]. IEEE Transactions on Reliability, 1997, 46(2):296-302.
[108]刘军,王士俊.串联系统的可靠性下限评估方法研究[J].系统工程与电子技术, 1998, 4:68-74.
[109] Levy L J, Moore A H. A Monte Carlo technique for obtaining system reliability confidence limits from component test data. IEEE Transactions on Reliability, 1967, R34:69-72.
[110] Rice R E, Moore A H. A Monte Carlo technique for Confidence limits of system reliability using pass-fail data [J]. IEEE Transactions on Reliability, 1987, R36: 109-112.
[111] Chao A , Huwang L-C. A modified Monte Carlo technique for confidence limits of system reliability using pass-fail data [J]. IEEE Transactions on Reliability, 1987, R36:109-112.
[112] Martz H F, Waller R. Bayesian Reliability Analysis [M]. John Wiley&Sons Press, 1982.
[113] Abdelwahid A R, Winterbottom A. The approximation of system reliability posterior distributions [J]. Journal of statistical Planning and Inference, 1987, 16:267-275.
[114] Springer M D, Thompson W E. Bayesian confidence limits for the product of n binomial parameters [J]. Bimetrika, 1966, 53(3/4): 611-613.
[115] Tann J, Gupta A K. On the distribution of the product of independent beta random variables [J]. Statistics&Probability Letters, 1984, 2:165-16.
[116] Wofe J E. Bayesian reliability assessment from test data [A]. Proc. Ann. Reliability &Maintainability Symp[C], 1976:411-419.
[117] Springer M D. The Algebra of Random Variables [M]. John Wiley&Sons, New York: 1979.
[118] Thompson W E, Haynes R D. On the reliability, availability and Bayes confidence intervals for multi-comonent systems [J]. Naval Reseach Logistics Quarterly, 1980, 27:345-358.
[119] Tang J, Tang K, Moskowitz H. Bayes credibility intervals for reliability of series systems with very reliable components [J]. IEEE Transactions on Reliability, 1994, 43(1): 132-137.
[120] Martz H F, Waller R A, Fickas E T. Bayesian reliability analysis of series systems of binomial subsystems and components [J]. Technometrics, 1988, 30:143-154.
[121]赵勇辉,程侃,于丹.利用先验信息修正经典限的可靠性评估方法[J].系统工程理论与实践,2002,(5).
[122]张金槐.复杂系统可靠性的Bayes-Easterling综合方法[J].模糊系统与数学, 1997, (3).
[123]冯蕴雯,冯元生.极小子样高可靠性成败型产品试验的贝叶斯评估方法研究[J].机械科学与技术.1999, 18(2): 198-200.
[124]张士峰,樊树江,王慧频.复杂系统的Bayes可靠性评估[J].航天控制, 2000,(2).
[125] Speis A , Healey G. Analytical and experimental study of the performance of Markov random field applied to textured images using small samples [J]. IEEE Trans Image Processing, 1996, 53:447-458.
[126] Fulton J W, Abernethy R B. Likelihood adjustment: a simple method for berrer forecasting from small samples [A]. Proceedings of the Annual Reliability and Maintainability Symposium [C], 2000:150-1549.
[127] Davison C R, Birk A M. Development of Fault Diagnosis and Failure Prediction Techniques for Small Gas Turbine Engines [A]. ASME [C], 2001-GT-548.
[128] Zaita. Performance Deterioration modeling in aircraft gas turbine engines [J]. Journal of Engineering for Gas Turbine Engine&Power , 1998, 120(2):344-349.
[129] Roemer M J. Development of diagnostic and prognostic technologies for aerospace health management applications [A]. 2001 IEEE Aerospace Conference [C], Vol.6, Big Sky, Montanna, USA, 2001: 3139-3147.
[130] Vittal S, Hajela P, Joshi A. Review of Approaches to gas turbine life management [A]. Proceeding of 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference [C]. New York. 2004:1-11.
[131] Torre M. New ASME Turbine Conference Focuses on Aero Engine Life Management. http://www.asme.org/NewsPublicPolicy/PressReleases/New_Turbine_Conference.cfm. 2004-1-5/2008-5-8.
[132] Enright M P, Huyse L, Mcclung R C, et al. Probabilistic Methodology for Life Prediction of Aircraft Turbine Rotors [A]. Proceeding of Engineering, Construction, and Operations in Challenging Environments: Earth & Space 2004 [C]. Houston, TX. 2004:453-460.
[133]吴学亮,叶新农.军用航空发动机寿命控制方法[J].航空科学技术, 1996(5):19-22.
[134]赵福星.以耐久性与损伤容限为中心的现役发动机寿命控制方法[J].航空动力学报, 2001, 16(4):305-308.
[135]李建国,叶新农,徐可君.涡扇发动机寿命管理研究[J].海军航空工程学院学报, 2005, 20(4):468-472.
[136] Jardine a K S, Anderson P M, Mann D S. Applications of the Weibull proportional hazards Model to Aircraft and Marine Engine Failure Data [J]. Quality & Reliability Engineering International, 1987, 3:77-82.
[137] Suarez E L, Duffy M I, Gamache R N, et al. Jet engine life prediction systems integrated with prognostics health management [A]. Proceeding of IEEE Aerospace Conference [C]. 2004:3596- 3602.
[138]王通北,陈美英.军用航空发动机的可靠性和寿命[J].航空发动机, 1994(1):36-47.
[139]张连祥.提高航空发动机可靠性的途径[J].航空发动机, 2002(2):17-21.
[140]张宝诚,刘孝安.航空发动机可靠性和经济性[M].北京:国防工业出版社, 1998.
[141]张逸民.航空涡轮发动机翻修寿命与可靠性分析[J].航空学报, 1981, 2(4):44-51.
[142]郝英.基于智能技术的民航发动机故障诊断和寿命预测研究[D]. [博士学位论文],南京:南京航空航天大学, 2006.
[143]徐可君,江龙平.军用航空发动机可靠性和寿命管理[J].中国工程科学, 2003, 5(1):82-88.
[144]戎翔.民航发动机健康管理中的寿命预测与维修决策方法研究[D]. [博士学位论文],南京:南京航空航天大学, 2008.
[145] Pate-Cornell M e. Uncertainties in risk analysis: six level of treatment [J]. Reliability Engineering and System Safety, 1996,54 :2-3.
[146] Coolen F P A, Newby M J. Bayesian reliability analysis with imprecise prior probabilities [J]. Reliability Engineering and System Safety, 1994, 43: 75-85.
[147] Coolen F P A. On Bayesian reliability analysis with informative priors and censoring [J]. Reliability Engineering and System Safety, 1996, 53: 91-98.
[148] Lichtenstein S, Newman J R, Empirical scaling of common verbal phrases associated with numerical probabilities [J]. Psychonometric Science, 9(10):563-564.
[149] Erto P. New practical Bayes estimates for the 2-parameter Weibull distribution [J]. Microelectronics reliability, 1994, 34(5):789-802.
[150] Selby M A, Shoukri M M. Bayes comparison of 2 lognormal reliability functions [J]. IEEE Tranactions on Reliability, 1990, 39(3).
[151] Elperin T, Gertsbakh I. Bayes credibility estimation of an Exponential parameter for random censoring&incomplete information [J]. IEEE Transactions on Reliability, 1990,39(2).
[152] Arturo J F. Bayesian inference from type II doubly censored Raleigh data [J]. Statistics&probability letters, 2000, 48:393-399.
[153]周桃庚.贝叶斯方法在光电系统可靠性中的应用研究[D]. [博士学位论文],北京:北京理工大学, 2002.
[154]宋保维,毛昭勇,王雯琴等.基于故障树分析的鱼雷可靠性评定方法[J].系统仿真学报, 2007, (10).
[155] Savchuk V P, Martz H F. Bayes Reliability Estimation Using Multiple Sources of PriorInformation: Binomial Sampling [J]. IEEE Transactions on Reliability, 1994, 43(1): 138-144.
[156] D Dubois , H Prade. Possibility theory and data fusion in poorly informed environments [J]. Control Eng. Practice, 1994, 811-823.
[157] Didier Dubios, Henri Prade. Possibility theory in information fusion. NMR2002:103-116.
[158] S A Sandri, D Dubois, H W Kalfsbeek. Elicitation, assessment and pooling of expert judgements using possibility theory [J]. IEEE Transactions on Fussy Systems, 1995, 3(3):313-315.
[159] Philippe Smets. The transferable belief model for expert judgments and reliability problems [J]. Reliability Engineering and System System Safety, 1992, 38: 59-66.
[160] Barlow R E, Scheuer E M. Reliability growth during a development testing program [J]. Technometrics, 1966, 8:53-60.
[161] Thomas A, Mazzuchi, Refik Soyer. A Bayesian method for assessing product reliability during development testing [J]. IEEE Transactions on Reliability, 1993, 42(2):503-509.
[162]张洪才.小子样复杂机械系统可靠性分析与预测理论与应用的研究[D]. [博士学位论文],济南:山东大学, 2003.
[163]王浩,庄钊文.基于模糊测度准则的可靠性评估方法[J].系统工程与电子技术, 2000, 22(12):93-96.
[164]张士峰. Bayes小子样理论及其在武器系统评估中的运用研究[D]. [博士学位论文],国防科学技术大学, 2000.
[165]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]. [博士学位论文],国防科学技术大学, 2003.
[166]刘琦.液体火普发动机可靠性增长试验评定方法研究[D]. [博士学位论文],国防科学技术大学, 2003.
[167]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, (1).
[168]冯静,周经伦.基于Bayes-模糊逻辑算子的小子样可靠性信息融合方法[J].航空动力学报, 2008, (9).
[169]冯静,周经伦,孙权. Bayes分析中多源验前信息融合的ML-II方法[J].数学的实践与认识, 2006, (6).
[170]王华伟.液体火箭发动机可靠性增长管理研究[D]. [博士学位论文],国防科学技术大学, 2003.
[171]张金槐.多层验前信息场合下多维动态参数Bayes估计的稳健性分析[J].质量与可靠性, 2006, (1).
[172]孙有朝.指数型单元混联系统可靠性综合的信息论方法[J].航空动力学报, 2005, (5).
[173] Vapnik V. The nature of statistical learning theory. Springer, Berlin Heidelberg New York, 1995.
[174] Sugumaran V, Sabareesh G R, Ramachandran K I. Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine [J]. Expert Systems with Applications, 2008, 34(4):3090-3098.
[175] Hong Jin-Hyuk, Min Jun-Ki, Cho Ung-Keun,et al. Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers [J]. Pattern Recognition, 2008, 41(2):662-671.
[176] Lu Wei, Chung Fu-Lai, Lu Hongtao, Choi Kup-Sze. Detecting fake images using watermarks and support vector machines [J]. Computer Standards&Interfaces, 2008, 30(3):132-136.
[177] Lau K W, Wu Q H. Local prediction of non-linear time series using support vector regression [J]. Pattern Recognition, 2008, 41(5):1539-1547.
[178] Rocco C M, Moreno J A. Fast Monte Carlo reliability evaluation using support vector machine [J]. Reliability Engineering and system Safety, 2002, 76:237-243.
[179] Chakguy P, Theodore B Trafalis, S Raman. Support Vector Regression for Determination of Minimum Zone [A]. Transactions of the ASME [C], 2003, 125:736-739.
[180] Hurtado J E, Alvarez D A. Classification approach for reliability analysis with stochastic finite-element modeling [J]. Journal of structural engineering, 2003, 8:1141-1149.
[181] Hong Wei-Chiang, Pai Ping-Feng. Predicting engine reliability by support vector machines [J]. Int J Adv Manuf Technol, 2005, 17 (4):2340-2348.
[182] Pai Ping-Feng. System reliability forecasting by support vector machines with genetic algorithms [J]. Mathematical and Computer Modelling, 2006, 43:262-274.
[183]李洪双,吕震宙.支持向量回归机在结构可靠性分析中的应用[J].航空学报, 2007, 28 (l):94-99.
[184]马超,吕震宙.结构可靠性分析的支持向量机分类迭代算法[J].中国机械工程, 2007, 18(7):816-819.
[185]余国林,吴海桥,丁运亮.威布尔分布在飞机系统使用可靠性评估中的应用[J].航空维修与工程,2006, (2).
[186]麻晓敏,张士杰,胡丽琴等.可靠性数据威布尔分析中秩评定算法改进研究[J].核科学与工程, 2007, (2).
[187] Lin Kuanming, Lin Chihjen. A Study on Reduced Support Vector Machines [J]. IEEE Transactions on Neural Networks, 2003, 14(6):1449-1459.
[188] Vapnik V N. Statistical Learning Theory [M]. New York: John Wiley & Sons, 1998.
[189] Kruif B J, Vries T J. On Using a Support Vector Machine in Learning Feed-Forward Control [C]. Proceeding of Advanced Intelligent.Como, Italy, 2005.
[190] Chen Chiachuang, Su Shunfeng. Robust Support Vector Regression Networks for Function Approximation with Outliers [J]. IEEE Transaction on Neural Networks, 2002, 13(6):1322-1330.
[191] Mackay D J C. Practical Bayesian Framework for Backprop Networks [J]. Neural Computation, 1992, 4(3): 448-472.
[192]孙有朝,左洪福,施军.发动机可靠性多级综合的信息熵法第一近似限[J].航空动力学报, 2002, (4).
[193]王润生.信息融合[M].北京:科学出版社, 2007.
[194] MTU. Engine maintenance cost [A]. Engine Year book [M], 2008.
[195]袁志发,宋世德.多元统计分析[M].北京:科学出版社, 2009.
[196]张恒喜,朱家元,郭基联.军用飞机型号发展工程导论[M].北京:国防工业出版社, 2004.
[197]刘思峰,谢乃明.灰色系统理论及其应用[M].北京:科学出版社, 2008.
[198]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社, 2005
[199] Bates J M. Granger C W J. Combination forecasts [J]. Operations Research Quarterly, 1969, 20(4):451~468.
[200] Kennedy J, Eberhart R C. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. Perth, Australia, 1995:1942~1948.
[201] Trelea I. C. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 2003, 85(6): 317~325
[202]潘峰,陈杰,辛斌等.粒子群优化方法若干特性分析[J].自动化学报, 2009, (7).
[203] Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation 1999. Washington DC, IEEE Press, 1999.
[204]常士基.现代民用航空维修工程管理[M].太原:山西科学技术出版社, 2002
[2]吴静敏.民用飞机全寿命维修成本控制与分析关键问题研究[D]. [博士学位论文],南京,南京航空航天大学, 2006.
[3]毛欣,基于维修的民用航空器部件可靠性管理研究[D]. [硕士学位论文],南京,南京航空航天大学, 2007.
[4] Jianyin Zhao, Fang Liu. Reliability assessment of the metallized film capacitors from degradation data [J]. Microelectronics Reliability, 2007, 47(2):434-436.
[5] Zhiwei Guo, Guangchen Bai. Application of Least Squares Support Vector Machine for Regression to Reliability Analysis [J]. Chinese Journal of Aeronautics, 2009, 22(2):160-166.
[6] David Mercier, Benjamin Quost, Thierry Den?ux. Refined modeling of sensor reliability in the belief function framework using contextual discounting [J]. Information Fusion, 2008, 9(2):246-258.
[7]陈云翔.可靠性与维修性工程[M].北京:国防工业出版社, 2007.
[8] Torre M. New ASME Turbine Conference Focuses on Aero Engine Life Management. [EB/OL].http://www.asme.org/NewsPublicPolicy/PressReleases/New_Turbine_Conference.cfm. 2008-5-8.
[9] Zhao J, Tang J. A generalized random variable approach for strain-based fatigue reliability analysis [J]. Journal of Pressure Vessel Technology, 2000, 122(2):156-161.
[10] Huang W, Rronaland G A. A generalized SSI reliability model considering stochastic loading and strength aging degradation [J]. IEEE Trans. On Reliability, 2004, 53(1):77-82.
[11]孙权,赵建印,周经伦.复合应力作用下强度退化的应力-强度干涉模型可靠性统计分析[J].计算力学学报, 2007, 24(3):358-361.
[12]谢里阳,李翠玲.应力-强度干涉模型在系统失效概率分析中的应用及相关问题[J].机械强度, 2005, 27(4):492-497.
[13] Fatemi A, Yang L. Cumulative fatigue damage and life prediction theoris: a survey of the state of the art for homogeneous materials [J]. International Journal of Fatigue, 1998, 20(1):9-34.
[14] Rao J S, Pathak A and Chawla A. Blade life: a comparison by cumulative damage theories [J]. Journal of Engineering for Gas Turbines and Power, 2001, 123(4):886-892.
[15] Khen R, Altus E. Micro-macro relations for fatigue crack growth[J]. Mechanics of Materials, 1995, 19(2-3):89-101.
[16]董聪.现代结构系统可靠性理论及其应用[M].北京:科学出版社,2001.
[17]安伟光,赵维涛,严心池.不完整结构系统同时考虑强度和刚度的可靠性分析[J].工程力学, 2005, 22(4):58-61.
[18] Lee E T, Wang J W. Statistical Methods for Survival Data Analysis [J]. Technometrics, 2003, 45(4):372-373(2).
[19] Marcorin A J, Abackerli A J. Field Failure Data: an Alternative Proposal for Reliability Estimation [J]. Quality and Reliability Engineering International, 2006, 22(7):851-862.
[20] Ghosh J K, Mohan Delampady, Tapas Samanta. An Introduction to Bayesian Analysis: Theory and Methods [M]. Springer, 2006.
[21] Phillips M J. Bayesian estimation from censored data with incomplete information [J]. Quality and Reliability Engineering International, 2004, 20:237-245.
[22] John Quigley, Lesley Walls. Confidence intervals for reliability-growth models with small sample-sizes [J]. IEEE Transactions on Reliability, 2003, 52(2):257-262.
[23]冯静,周经纶.基于Bayes-模糊逻辑算子的小子样可靠性信息融合方法[J].航空动力学报, 2008, 23(9):106-109.
[24]茆诗松,王玲玲,蹼晓龙.威布尔分布场合无失效数据的可靠性分析[J].应用概率统计, 1996, 12(1):95-107.
[25] Software R. Reliability: A Practitioner’s Guide [M].陈晓彤等译.北京:北京航空航天大学出版社, 2005.
[26] Dey, D K, Lee, T M. Bayes computation for life testing and reliability estimation [J]. IEEE Transactions on Reliability, 1992, 41(4):621-626.
[27] Mroczkowski, R S, Maynard, J M. Estimating the reliability of electrical connectors [J]. IEEE Transactions on Reliability, 1991, 40(5):507-512.
[28] Thiagarajah, K R. Homogeneity tests for scale parameters of 2-parameter exponential populations under time censoring [J]. IEEE Transactions on Reliability, 1995, 44(2):297-301.
[29] Chun Kin, C Seraidaridia, C I. et al. Sequential sampling plans for early-life reliability assessment [A]. Proceedings Annual Reliability and Maintainability Symposium [C]. 1997:131-135.
[30] Gibson, D. Statistical reliability prediction [M]. International Integrated Reliability Workshop. 1995:161.
[31]葛广平.我国加速寿命试验研究的现状与展望[J].数理统计与管理, 2000, 19(1):25-29.
[32] Nelson, W B. Accelerated Life Testing-Step-Stress Models and Data Analysis [J]. IEEE Transactions on Reliability, 1980, 29(2):103-108.
[33]宋保维.系统可靠性设计与分析[M].西安:西北工业大学出版社, 2008.
[34]卢明银,徐人平.系统可靠性[M].北京:机械工业出版社, 2008.
[35]胡昌寿.可靠性工程——设计、试验、分析、管理.北京:宇航出版社, 1989.
[36] Box G E P, Tiao G C. Bayesian Inference in Statistical Analysis [M]. Reading, Addison-Wesley, 1973.
[37] Lindley D V, Smith A F M. Bayes estimates for the linear model. J.Roy. Statist. Soe. (Ser.B), 1972, 34: l-41.
[38] DeFinettiB. Theory of Probility [M]. New York: Wiley, 1975.
[39] Berger J O. Statistical decision theory and Bayesian analysis [M]. NewYork: Springer-Verlag, 1985.
[40]金星,洪延姬,沈怀荣等.可靠性数据计算与应用[M].北京:国防工业出版社, 2003: 58-69.
[41] Martz H F, Waller R A. Bayesian Reliability Analysis [M], John Wiley & Sons (New York), 1982.
[42]周源泉,翁朝曦著.可靠性评定[M].北京:科学出版社, 1990
[43]张士峰,樊树江,张金槐.成败型产品可靠性的Bayes评估[J].兵工学报, 2001, 22(5):238-240.
[44]韩明,定时截尾指数分布先验数的适合域[J],数理统计与应用概率, 1997, 12(l).
[45] Cuirong Ren, Dongchu Sun, Dipak K. Dey. Bayesian and frequentist estimation and prediction for exponential distributions [J]. Journal of Statistical Planning and Inference, 2006, 136:2873-2897.
[46]周源泉,刘文生.双参数指数分布的可靠性评估(Ⅰ) [J].质量与可靠性, 2004(1):5-10.
[47]周源泉,刘文生.双参数指数分布的可靠性评估(Ⅱ) [J].质量与可靠性, 2004(2): 19-24.
[48] Ahmed A Soliman, A H Abd Ellah, K S Sultan. Comparison of estimates using record statistics from weibull model: Bayesian and non-Bayesian approaches [J]. Computational Statistics & Data Analysis, 2005, 12.
[49]张金槐,唐雪梅. Bayes方法(第二版) [M].长沙:国防科技大学出版社, 1992.
[50] Pate-Cornell M E, Uncertainties in rich analysis: six level of treatment[J]. Relibaility Engineering and system Safety, 1996, 54:2-3.
[51] Coolen F P A. On Bayesian reliability analysis with informative priors and cersoring [J]. Reliability Engineering and System Safety, 1996, 53:91-98.
[52] Singpuwralla N D, Song M S. Reliability analysis using Weibull life time data and expert opinion [J]. IEEE Transaction on Reliability, 1988, 37(3).
[53] Coolen F P A, Newby M J. Bayesian reliability analysis with imprecise prior probabilities[J]. Reliability Engineering and Safety, 1994, 43:75-85.
[54] Lichtenstein S, Newman J R. Empirical scaling of common verbal phrases associated with numerical probabilities [J]. Psychonometric Science, 9(10):563-564.
[55] Bilal M A. A Practical guide on conducting expert-opinion elicitation of probabilities and consequences for corps facilities [R]. IWR Report 01-R-01, 2001.
[56]路晓辉.指数模型Bayes评定中的验前分布[J].北京理工大学学报, 1990, 10(1).
[57] Erto p. New practical Bayes estimates for the 2-parameter Weibull distribution [J]. Microelectronis reliability, 1994, 34(5):789-802.
[58] Calabrla R, Pulcini G. An engineering approach to Bayes estimation for the Weibull distribution [J]. Microelectron Reliability, 1994, 24(5).
[59] Singh S K. Bayes interval for the Weibull parameters utilizing guessed estimates [J]. Microelectronics Reliability, 1993, 33(6).
[60] Elperin T, Gertsbakh I. Bayes credibility estimation of an Exponential parameter for random censoring&incomplete information [J]. IEEE Transaction on Reliability, 1990, 39(2).
[61] Ram C T, Yang Y Z, Jyoti N Z. Bayes estimation for the pareto failure-model using gibbs samling [J]. IEEE Transaction on Reliability, 1996, 43(3).
[62] Arturo J F. Bayesian inference from type II doubly cersored Raleigh data [J]. Statistics&Probability Letters, 2000, 48:393-399.
[63] Robbins H. The empirical Bayes approach to statistical decision Problems [J]. Annual Mathematics Statistics, 1964, 35:1-20.
[64] Hong-bo Zhao. Slope reliability analysis using a support vector machine [J]. Computers and Geotechnics, 2008, 35(3).
[65] Ping-Feng Pai. System reliability forecasting by support vector machines with genetic algorithms [J]. Mathematical and Computer Modelling, 2006, 43(4)
[66] Martz H F, Waller R A. A Bayesian Zero-Failure Reliability Demonstration Testing Procedure [J]. Jounral of Quality Technology 1979, 11(3):128-137
[67]赵宇.可靠性数据分析教程[M].北京:北京航空航天大学出版社, 2009.
[68]韩明.无失效数据的可靠性分析[M].北京:中国统计出版社, 1999.
[69] Gerstsbackh, I B, Kordonskiy, et al. Models of Failure [M]. New York: Wiley. 1969.
[70] Nair V N. Discussion of Estimation of Relibaility in Field Perofrmance Sutdies by J.D. Kalbfleisch and J.F. Lwaless, Technometrics, 1988, 30:379-383
[71] Feinberg, A A, Widom A. Connecting Parametric Aging to Catastrophic Failure Through Thermodynamics [J]. IEEE Transaction on Reliability, 1996, 45(1):28-33.
[72] L Gallais, Natoli, J Y, et al. Statistical study of single and multiple pulse laser-induceddamage in glasses [J]. Optics Express, 2002, 10(25): 2-6.
[73] Viviane Ribeiro Branco de Oliveiral, E A O C. Comparison of Methods to Estimate the Time-to-failure Distribution in Degradation Test [J]. Quality and Reliability Engineering Internation. 2004, (20):363-373.
[74] Meeker W Q, Hamada M. Statistical Tools of the Rapid Development&Evaluation of High-relibaility Products [J]. IEEE Transaction On Reliability, 1995, 44(2):187-198.
[75] Lu C J, Meeker W Q, Escobar L A. A Comparison of Degradation and Failure-time analysis Methods for Estimating a Time-failure Distribution [J]. Statistical Sinica, 1996, 6: 531-546.
[76]姚增起.系统退化和系统可靠性研究[D]. [博士学位论文],合肥:中国科学院自动化研究所, 1988.
[77]庄东辰.退化失效模型及其统计分析[D]. [博士学位论文],上海:华东师范大学研究生院, 1994.
[78] Chao M T. Degradation Analysis and Related Topics: Some Thoughts and a Review, Proc. Natl. Sci. Counc. ROC(A), 1999, 23(5):555-566.
[79] Lu J C, Park J, Yang Q. Statistical Ineference of A Time-to-Failure Distribution Derived From Linear Degradation Data [J]. Technometrics, 1997, 39:391-400.
[80] Huang W. Relibaility Analysis Considering Product Perofmrance Degradation [D]. [P.h.D Thesis], The Universiyt of Arizona, 2002.
[81] Su C, Lu J C, Chen D, et. al. A random Coefficient Degradation Model With Random Sample Size [J]. Lieftime Data Analysis, 1999, 5:173-183.
[82] Robinson M, Crowder M. Bayesian Methods for a Growth-curve Degradation Model with Repeated Measures [J]. Lieftime Data Analysis, 2000, 6:357-374.
[83] Wu S Y, Tsai T R. Estimation of Time-to-failure Distribution Derived From a Degradation Model Using Fuzzy Clustering [J]. Quality and Reliability Engineering Intenrational, 2000, 16: 261-267.
[84] Gopikrishnan A. Reliability Inference Based on Degradation and Time to FailureData: Some Models, Mehtods and Efficiency Comparisions [D]. [P.h.D Thesis], The University of Michigan, 2004.
[85] Meeker W Q, Escobar L A. Statistical Methods for Reliability Data [M]. John Wiley&Sons, 1998.
[86] Lu C J, Meeker W Q. Using Degradation Measures to Estimate a Time-to-failure Distribution [J]. Technometrics, 1993, 35(2):161-174.
[87] Wilson S P, Taylor D. Reliability Assessment From Fatigue Micro-crack Data [J], IEEE Transactions On Reliability. 1997, 46(2): 165-171.
[88] Place C S, Sturtt J E, Allsopp K, et al. Reliability prediction of Helicopter Transmission System Using Stress-strength Interference with Underlying Damage Accumulation [J]. Quality and Reliability Engineering International, 1999, 15:69-78.
[89] Takeda E, Suzuki N. An Emprical Model for Device Degradation due to Hot-Carrier Injection [J]. IEEE Trans. On Elect Device Lett EDL, 1983, 4:111.
[90] Chan C K, Boulanger M, Tortorella M. Analysis of Parameter-degradation Data Using Life-data Analysis Programs [A]. Proceedings Annual Reliability and Maintainability Symposium [C], 1994, 288-291.
[91] Carey M B Koenig, R H. Reliability Assessment Based on Accelerated Degradation: A Case Sutdy [J]. IEEE Transactions On Reliability, 1991, 40(5):499-506.
[92] Meeker W Q, LuValle M J. An Accelerated Life Test Model Based on Relibaility Kinetics [J]. Technometrics, 1995, 37:133-146.
[93] Ramirez J G, Gore W L, Johnston G. New Methods for Modeling Relibaility Using Degradation Data [J]. Statistics Data Analysis and Data Mining, 2001, 263-226.
[94] Nelson, W B. Anaiysis of Performance-degradation data from accelerated tests [J]. IEEE Tansactions on Reliablity, 1981, 30(2):149-154.
[95] E Ioannides, E Beghini, G Bergling, et al. Cleanliness and its importance for bearing performance [J]. Ball Bearing Journal, 1993:242-248.
[96]张永强,刘琦,周经伦..小子样条件下基于指数退化轨道的性能可靠性评定[J].装备指挥技术学院学报, 2007, (1).
[97]邓爱民,陈循,张春华等.基于性能退化数据的可靠性评估[J].宇航学报, 2006, (3).
[98]庄钊文,郁文贤,王浩等.信息融合技术在可靠性评估中的应用[J].系统工程与电子技术, 2000, 22(3):75-80.
[99] Buehler R J. Confidence limits for the product of two binomial parameters [J]. Journal of American Statistical Association. 1957, 52: 482-493.
[100] Crowder M J, Kimber A C, Smith R L, Sweeting T J. Statistical Analysis of Reliability Data [M]. Norwell: Chapman&Hall, 1991.
[101] Easterling R G. Approximate confidence intervals for system reliability [J]. Journal of American Statistical Association. 1972, 67:220-222.
[102] Winterbottom A. Asymptotic expansions to improve large sample confidence intervals for system reliability [J]. Biometrica, 1980, 67:351-357.
[103] Winterbottom A. The interval estimation of system reliability from component test data [J]. Operations Research. 1984.
[104] Mann N L , Grubbs F E. Approximate optimum confidence bounds for system reliabilitybased on component test data [J]. Technometrics, 1974, 16:335-347.
[105] Madansky A. Approximate confidence limits for the reliability of series and parallel systems [J]. Technometrics, 1965, 7: 495-503.
[106] Martz H F, Duran B S. A comparison of three methods for calculating lower confidence limits on system reliability using binomial component data [J]. IEEE Transactions on Reliability, 1985, R-34:143-154.
[107] Willits C J, Dietz D C , Moore A H. Series-system reliability estimation using very small binomial samples [J]. IEEE Transactions on Reliability, 1997, 46(2):296-302.
[108]刘军,王士俊.串联系统的可靠性下限评估方法研究[J].系统工程与电子技术, 1998, 4:68-74.
[109] Levy L J, Moore A H. A Monte Carlo technique for obtaining system reliability confidence limits from component test data. IEEE Transactions on Reliability, 1967, R34:69-72.
[110] Rice R E, Moore A H. A Monte Carlo technique for Confidence limits of system reliability using pass-fail data [J]. IEEE Transactions on Reliability, 1987, R36: 109-112.
[111] Chao A , Huwang L-C. A modified Monte Carlo technique for confidence limits of system reliability using pass-fail data [J]. IEEE Transactions on Reliability, 1987, R36:109-112.
[112] Martz H F, Waller R. Bayesian Reliability Analysis [M]. John Wiley&Sons Press, 1982.
[113] Abdelwahid A R, Winterbottom A. The approximation of system reliability posterior distributions [J]. Journal of statistical Planning and Inference, 1987, 16:267-275.
[114] Springer M D, Thompson W E. Bayesian confidence limits for the product of n binomial parameters [J]. Bimetrika, 1966, 53(3/4): 611-613.
[115] Tann J, Gupta A K. On the distribution of the product of independent beta random variables [J]. Statistics&Probability Letters, 1984, 2:165-16.
[116] Wofe J E. Bayesian reliability assessment from test data [A]. Proc. Ann. Reliability &Maintainability Symp[C], 1976:411-419.
[117] Springer M D. The Algebra of Random Variables [M]. John Wiley&Sons, New York: 1979.
[118] Thompson W E, Haynes R D. On the reliability, availability and Bayes confidence intervals for multi-comonent systems [J]. Naval Reseach Logistics Quarterly, 1980, 27:345-358.
[119] Tang J, Tang K, Moskowitz H. Bayes credibility intervals for reliability of series systems with very reliable components [J]. IEEE Transactions on Reliability, 1994, 43(1): 132-137.
[120] Martz H F, Waller R A, Fickas E T. Bayesian reliability analysis of series systems of binomial subsystems and components [J]. Technometrics, 1988, 30:143-154.
[121]赵勇辉,程侃,于丹.利用先验信息修正经典限的可靠性评估方法[J].系统工程理论与实践,2002,(5).
[122]张金槐.复杂系统可靠性的Bayes-Easterling综合方法[J].模糊系统与数学, 1997, (3).
[123]冯蕴雯,冯元生.极小子样高可靠性成败型产品试验的贝叶斯评估方法研究[J].机械科学与技术.1999, 18(2): 198-200.
[124]张士峰,樊树江,王慧频.复杂系统的Bayes可靠性评估[J].航天控制, 2000,(2).
[125] Speis A , Healey G. Analytical and experimental study of the performance of Markov random field applied to textured images using small samples [J]. IEEE Trans Image Processing, 1996, 53:447-458.
[126] Fulton J W, Abernethy R B. Likelihood adjustment: a simple method for berrer forecasting from small samples [A]. Proceedings of the Annual Reliability and Maintainability Symposium [C], 2000:150-1549.
[127] Davison C R, Birk A M. Development of Fault Diagnosis and Failure Prediction Techniques for Small Gas Turbine Engines [A]. ASME [C], 2001-GT-548.
[128] Zaita. Performance Deterioration modeling in aircraft gas turbine engines [J]. Journal of Engineering for Gas Turbine Engine&Power , 1998, 120(2):344-349.
[129] Roemer M J. Development of diagnostic and prognostic technologies for aerospace health management applications [A]. 2001 IEEE Aerospace Conference [C], Vol.6, Big Sky, Montanna, USA, 2001: 3139-3147.
[130] Vittal S, Hajela P, Joshi A. Review of Approaches to gas turbine life management [A]. Proceeding of 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference [C]. New York. 2004:1-11.
[131] Torre M. New ASME Turbine Conference Focuses on Aero Engine Life Management. http://www.asme.org/NewsPublicPolicy/PressReleases/New_Turbine_Conference.cfm. 2004-1-5/2008-5-8.
[132] Enright M P, Huyse L, Mcclung R C, et al. Probabilistic Methodology for Life Prediction of Aircraft Turbine Rotors [A]. Proceeding of Engineering, Construction, and Operations in Challenging Environments: Earth & Space 2004 [C]. Houston, TX. 2004:453-460.
[133]吴学亮,叶新农.军用航空发动机寿命控制方法[J].航空科学技术, 1996(5):19-22.
[134]赵福星.以耐久性与损伤容限为中心的现役发动机寿命控制方法[J].航空动力学报, 2001, 16(4):305-308.
[135]李建国,叶新农,徐可君.涡扇发动机寿命管理研究[J].海军航空工程学院学报, 2005, 20(4):468-472.
[136] Jardine a K S, Anderson P M, Mann D S. Applications of the Weibull proportional hazards Model to Aircraft and Marine Engine Failure Data [J]. Quality & Reliability Engineering International, 1987, 3:77-82.
[137] Suarez E L, Duffy M I, Gamache R N, et al. Jet engine life prediction systems integrated with prognostics health management [A]. Proceeding of IEEE Aerospace Conference [C]. 2004:3596- 3602.
[138]王通北,陈美英.军用航空发动机的可靠性和寿命[J].航空发动机, 1994(1):36-47.
[139]张连祥.提高航空发动机可靠性的途径[J].航空发动机, 2002(2):17-21.
[140]张宝诚,刘孝安.航空发动机可靠性和经济性[M].北京:国防工业出版社, 1998.
[141]张逸民.航空涡轮发动机翻修寿命与可靠性分析[J].航空学报, 1981, 2(4):44-51.
[142]郝英.基于智能技术的民航发动机故障诊断和寿命预测研究[D]. [博士学位论文],南京:南京航空航天大学, 2006.
[143]徐可君,江龙平.军用航空发动机可靠性和寿命管理[J].中国工程科学, 2003, 5(1):82-88.
[144]戎翔.民航发动机健康管理中的寿命预测与维修决策方法研究[D]. [博士学位论文],南京:南京航空航天大学, 2008.
[145] Pate-Cornell M e. Uncertainties in risk analysis: six level of treatment [J]. Reliability Engineering and System Safety, 1996,54 :2-3.
[146] Coolen F P A, Newby M J. Bayesian reliability analysis with imprecise prior probabilities [J]. Reliability Engineering and System Safety, 1994, 43: 75-85.
[147] Coolen F P A. On Bayesian reliability analysis with informative priors and censoring [J]. Reliability Engineering and System Safety, 1996, 53: 91-98.
[148] Lichtenstein S, Newman J R, Empirical scaling of common verbal phrases associated with numerical probabilities [J]. Psychonometric Science, 9(10):563-564.
[149] Erto P. New practical Bayes estimates for the 2-parameter Weibull distribution [J]. Microelectronics reliability, 1994, 34(5):789-802.
[150] Selby M A, Shoukri M M. Bayes comparison of 2 lognormal reliability functions [J]. IEEE Tranactions on Reliability, 1990, 39(3).
[151] Elperin T, Gertsbakh I. Bayes credibility estimation of an Exponential parameter for random censoring&incomplete information [J]. IEEE Transactions on Reliability, 1990,39(2).
[152] Arturo J F. Bayesian inference from type II doubly censored Raleigh data [J]. Statistics&probability letters, 2000, 48:393-399.
[153]周桃庚.贝叶斯方法在光电系统可靠性中的应用研究[D]. [博士学位论文],北京:北京理工大学, 2002.
[154]宋保维,毛昭勇,王雯琴等.基于故障树分析的鱼雷可靠性评定方法[J].系统仿真学报, 2007, (10).
[155] Savchuk V P, Martz H F. Bayes Reliability Estimation Using Multiple Sources of PriorInformation: Binomial Sampling [J]. IEEE Transactions on Reliability, 1994, 43(1): 138-144.
[156] D Dubois , H Prade. Possibility theory and data fusion in poorly informed environments [J]. Control Eng. Practice, 1994, 811-823.
[157] Didier Dubios, Henri Prade. Possibility theory in information fusion. NMR2002:103-116.
[158] S A Sandri, D Dubois, H W Kalfsbeek. Elicitation, assessment and pooling of expert judgements using possibility theory [J]. IEEE Transactions on Fussy Systems, 1995, 3(3):313-315.
[159] Philippe Smets. The transferable belief model for expert judgments and reliability problems [J]. Reliability Engineering and System System Safety, 1992, 38: 59-66.
[160] Barlow R E, Scheuer E M. Reliability growth during a development testing program [J]. Technometrics, 1966, 8:53-60.
[161] Thomas A, Mazzuchi, Refik Soyer. A Bayesian method for assessing product reliability during development testing [J]. IEEE Transactions on Reliability, 1993, 42(2):503-509.
[162]张洪才.小子样复杂机械系统可靠性分析与预测理论与应用的研究[D]. [博士学位论文],济南:山东大学, 2003.
[163]王浩,庄钊文.基于模糊测度准则的可靠性评估方法[J].系统工程与电子技术, 2000, 22(12):93-96.
[164]张士峰. Bayes小子样理论及其在武器系统评估中的运用研究[D]. [博士学位论文],国防科学技术大学, 2000.
[165]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]. [博士学位论文],国防科学技术大学, 2003.
[166]刘琦.液体火普发动机可靠性增长试验评定方法研究[D]. [博士学位论文],国防科学技术大学, 2003.
[167]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, (1).
[168]冯静,周经伦.基于Bayes-模糊逻辑算子的小子样可靠性信息融合方法[J].航空动力学报, 2008, (9).
[169]冯静,周经伦,孙权. Bayes分析中多源验前信息融合的ML-II方法[J].数学的实践与认识, 2006, (6).
[170]王华伟.液体火箭发动机可靠性增长管理研究[D]. [博士学位论文],国防科学技术大学, 2003.
[171]张金槐.多层验前信息场合下多维动态参数Bayes估计的稳健性分析[J].质量与可靠性, 2006, (1).
[172]孙有朝.指数型单元混联系统可靠性综合的信息论方法[J].航空动力学报, 2005, (5).
[173] Vapnik V. The nature of statistical learning theory. Springer, Berlin Heidelberg New York, 1995.
[174] Sugumaran V, Sabareesh G R, Ramachandran K I. Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine [J]. Expert Systems with Applications, 2008, 34(4):3090-3098.
[175] Hong Jin-Hyuk, Min Jun-Ki, Cho Ung-Keun,et al. Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers [J]. Pattern Recognition, 2008, 41(2):662-671.
[176] Lu Wei, Chung Fu-Lai, Lu Hongtao, Choi Kup-Sze. Detecting fake images using watermarks and support vector machines [J]. Computer Standards&Interfaces, 2008, 30(3):132-136.
[177] Lau K W, Wu Q H. Local prediction of non-linear time series using support vector regression [J]. Pattern Recognition, 2008, 41(5):1539-1547.
[178] Rocco C M, Moreno J A. Fast Monte Carlo reliability evaluation using support vector machine [J]. Reliability Engineering and system Safety, 2002, 76:237-243.
[179] Chakguy P, Theodore B Trafalis, S Raman. Support Vector Regression for Determination of Minimum Zone [A]. Transactions of the ASME [C], 2003, 125:736-739.
[180] Hurtado J E, Alvarez D A. Classification approach for reliability analysis with stochastic finite-element modeling [J]. Journal of structural engineering, 2003, 8:1141-1149.
[181] Hong Wei-Chiang, Pai Ping-Feng. Predicting engine reliability by support vector machines [J]. Int J Adv Manuf Technol, 2005, 17 (4):2340-2348.
[182] Pai Ping-Feng. System reliability forecasting by support vector machines with genetic algorithms [J]. Mathematical and Computer Modelling, 2006, 43:262-274.
[183]李洪双,吕震宙.支持向量回归机在结构可靠性分析中的应用[J].航空学报, 2007, 28 (l):94-99.
[184]马超,吕震宙.结构可靠性分析的支持向量机分类迭代算法[J].中国机械工程, 2007, 18(7):816-819.
[185]余国林,吴海桥,丁运亮.威布尔分布在飞机系统使用可靠性评估中的应用[J].航空维修与工程,2006, (2).
[186]麻晓敏,张士杰,胡丽琴等.可靠性数据威布尔分析中秩评定算法改进研究[J].核科学与工程, 2007, (2).
[187] Lin Kuanming, Lin Chihjen. A Study on Reduced Support Vector Machines [J]. IEEE Transactions on Neural Networks, 2003, 14(6):1449-1459.
[188] Vapnik V N. Statistical Learning Theory [M]. New York: John Wiley & Sons, 1998.
[189] Kruif B J, Vries T J. On Using a Support Vector Machine in Learning Feed-Forward Control [C]. Proceeding of Advanced Intelligent.Como, Italy, 2005.
[190] Chen Chiachuang, Su Shunfeng. Robust Support Vector Regression Networks for Function Approximation with Outliers [J]. IEEE Transaction on Neural Networks, 2002, 13(6):1322-1330.
[191] Mackay D J C. Practical Bayesian Framework for Backprop Networks [J]. Neural Computation, 1992, 4(3): 448-472.
[192]孙有朝,左洪福,施军.发动机可靠性多级综合的信息熵法第一近似限[J].航空动力学报, 2002, (4).
[193]王润生.信息融合[M].北京:科学出版社, 2007.
[194] MTU. Engine maintenance cost [A]. Engine Year book [M], 2008.
[195]袁志发,宋世德.多元统计分析[M].北京:科学出版社, 2009.
[196]张恒喜,朱家元,郭基联.军用飞机型号发展工程导论[M].北京:国防工业出版社, 2004.
[197]刘思峰,谢乃明.灰色系统理论及其应用[M].北京:科学出版社, 2008.
[198]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京:清华大学出版社, 2005
[199] Bates J M. Granger C W J. Combination forecasts [J]. Operations Research Quarterly, 1969, 20(4):451~468.
[200] Kennedy J, Eberhart R C. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks. Perth, Australia, 1995:1942~1948.
[201] Trelea I. C. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 2003, 85(6): 317~325
[202]潘峰,陈杰,辛斌等.粒子群优化方法若干特性分析[J].自动化学报, 2009, (7).
[203] Clerc M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation 1999. Washington DC, IEEE Press, 1999.
[204]常士基.现代民用航空维修工程管理[M].太原:山西科学技术出版社, 2002