航天器发射工程过程可靠性与任务灾难评估建模研究
|
摘要
航天器发射是航天工程建造的最后阶段，其可靠性与灾难评估在避免工程灾难中处于重要地位。近年来，航天产品可靠性不断改善，但由于航天工程固有的开拓性和复杂性，工程灾难时有发生。当前，航天器发射工程可靠性与灾难评估仍处于成长阶段，关于全系统的灾难评估文献报道较少，尤其是关于人因、组织和过程可靠性的研究成果几乎检索不到，因此，综合利用相关学科研究的新成果，结合航天器发射工程技术和管理的特点，研究适合工程本身的理论和方法，构建能全面融各类灾难症候的可靠性和灾难评估模型具有重大理论和现实意义。
本文在总结分析国内外相关研究成果的基础上，以工程实践为背景，结合数据仿真结果，构建并验证了人因可靠性、组织可靠性、过程可靠性和工程灾难评估模型，主要工作、创新与结论如下：
（1）在人因可靠性和组织可靠性的论域上存在着不可替代因素和补偿因素，通过集成这两类因素的状态可以达到考察人因可靠性和组织可靠性的目的。基于航天器发射工程实践数据和知识，定义补偿和不可替代因素，提出补偿和不可替代因素状态合成方法（the Composition for State of Irreplaceable and CompensatoryFactors，CSICF），讨论航天器发射工程人因和组织可靠性的因素空间，构建人因可靠性和组织可靠性模型，并用X-51发射工程飞控指令发送活动的数据分析检验模型的可行性与有效性，初步解决了航天器发射工程人因与组织可靠性量化评估问题。
（2）从航天产品进场至运载器点火发射，航天器发射工程各子过程的状态变化呈现出Markov性，且状态转移概率只与系统在状态上的停留时间相关，因此，可以使用连续时间马尔科夫链（Continuous-time Markov Chain，CTMC）来描述航天器发射工程组织过程。在综合考察航天产品、地面设备和工程组织的任务可靠性基础上，依据航天器发射组织过程约束关系，构建航天器发射工程组织过程的CTMC模型并给出其性能参数的计算方法。利用互模拟等价关系（BisimulationEquivalence Relation，BER），对航天器发射组织过程的多个CTMC并发模型进行状态空间压缩，提出工程组织全过程的可靠性计算模型，并通过对X-51任务计划的分析，进一步说明所提模型的使用方法和实际用途。
（3）对航天器发射工程灾难症候的有效获取和综合利用，可以提高工程灾难评估的准确性，降低灾难预测的模糊性。依据航天器发射工程各系统间的依存关系构建故障依存关系树（Failure Dependency Tree，FDT），并以此为框架，分别利用证据理论和Bayesian网络推理讨论症候信息融合和症候信息推理两类方法，构建航天器发射工程灾难评估模型。基于证据理论的症候信息融合方法可使不同领域专家采用不同观察手段所获取的各种局部的、不完整、不确定的灾难症候相互补充、消除冗余，达到降低不确定性目的。基于Bayesian网络的症候信息推理方法可有效避免症候信息融合方法易忽略少数正确意见的弊端，二者结合使用可以有效保证航天器发射工程灾难评估导向清晰、正确的结论。STS-51L灾难评估的实例分析，进一步验证了本文所提灾难评估模型的有效性。
The reliability and disaster assessment of spacecraft launch which is in the finalstage of the construction of aerospace engineering plays an important role in avoidingengineering disaster. In recent years, the reliability of space products continues to beimproved，but the natural pioneering and complexity of aerospace engineering result indisasters from time to time. Moreover, the study of reliability and disaster assessment ofspacecraft launch engineering (SLE) is still under growth, and we have yet to seeconcrete results from the research for human, organization and process reliability.Therefor, comprehensive using recent research results from related disciplines,researching theory and method for the spacecraft launch engineering, establishingmodel with all-round combining symptoms of extreme events in order to effectivelyestimate the reliability and disaster in a SLE is viteal important in theory and reality.
On the basis of the summary and analysis of relevant research results at home andabroad, considering the engineer practice background, associating the simulation data,this paper establishs and verifies the human reliability, the organization reliability, thescheduler reliability and the engineering disaster assessment model for SLE. The mainwork, contribution and conclusion are as follows:
(1) There exist irreplaceable and compensatory facts in the discourse domain ofhuman reliability and organization reliability. The purpose of examining human andorganization reliability by the composition of state of irreplaceable and compensatoryfactors can be attained. Based on field data and knowledge on SLE practice, this paperdiscusses the factor space of human and organization reliability in SLE, and proposesthe method of the composition of state of irreplaceable and compensatory factors(CSICF), and establishes assessment models of human and organization reliability inSLE, and validates the effectiveness and feasibility of the proposed method through theworking team reliability assessment for the flight control command delivery activity inthe mission XL-51, and initially solves the methodological issues for how toquantitatively assess human and organization reliability in SLE.
(2) From aerospace products into the launch site to the vehicle ignition, Changesabout the states of sub-processes present the Markov property and state transfer ratesonly relate to residence time of system in the state. Therefor, we can applying the modelof continuous-time Markov chain (CTMC) to simulate organization processes of a SLE.On the basis of the comprehensive study the mission reliability of aerospace productsand ground equipments and organization reliability, according to the constraintrelationship between sub-processes, this paper constructs the model of CTMC fororganization processes of SLE and gives the calculation method of the model parameters.Whereafter, the paper discusses the problems of how to compress the state space of multi-CTMC concurrency model by using bisimulation equivalence relation (BER), andpresents a model of how to calculate the reliability of the engineering organizationoverall process. Finally, through the X-51mission schedul analysis, the paper furtherillustrates that the model is of practical use and method of use.
(3) Effective access and comprehensive utillization of disaster symptoms in SLEcan improve the accurancy of the disaster assessment and reduce the fuzziness of thedisaster forecast. According to the dependencies between systems in SLE, this paperdesigns failure dependency tree (FDT). And to take the FDT as a framework, the paperdiscusses the symptom information fusion method (SIFM) based on evidence theory,and studies the symptom network inference method (SNIM) by using Bayesian networkinference, and constructs the disaster assessment model of SLE. The SIFM based onevidence theory can make all kinds of partial, incomplete, uncertain disaster symptomsacquired by experts in different fields with different methods mutual compensation, toeliminate redundant, to reduce uncertainty. The SNIM based on Bayesian networkinference can effectively avoid the drawback that the SIFM easily leads to the minorityopinion being ignored. A combination of both can effectively guarantee the clear andcorrect result for disaster assessment in SLE. The case analysis for STS-51L disasterassessment verifies the effectiveness of disaster assessment model proposed by thispaper.
本文在总结分析国内外相关研究成果的基础上，以工程实践为背景，结合数据仿真结果，构建并验证了人因可靠性、组织可靠性、过程可靠性和工程灾难评估模型，主要工作、创新与结论如下：
（1）在人因可靠性和组织可靠性的论域上存在着不可替代因素和补偿因素，通过集成这两类因素的状态可以达到考察人因可靠性和组织可靠性的目的。基于航天器发射工程实践数据和知识，定义补偿和不可替代因素，提出补偿和不可替代因素状态合成方法（the Composition for State of Irreplaceable and CompensatoryFactors，CSICF），讨论航天器发射工程人因和组织可靠性的因素空间，构建人因可靠性和组织可靠性模型，并用X-51发射工程飞控指令发送活动的数据分析检验模型的可行性与有效性，初步解决了航天器发射工程人因与组织可靠性量化评估问题。
（2）从航天产品进场至运载器点火发射，航天器发射工程各子过程的状态变化呈现出Markov性，且状态转移概率只与系统在状态上的停留时间相关，因此，可以使用连续时间马尔科夫链（Continuous-time Markov Chain，CTMC）来描述航天器发射工程组织过程。在综合考察航天产品、地面设备和工程组织的任务可靠性基础上，依据航天器发射组织过程约束关系，构建航天器发射工程组织过程的CTMC模型并给出其性能参数的计算方法。利用互模拟等价关系（BisimulationEquivalence Relation，BER），对航天器发射组织过程的多个CTMC并发模型进行状态空间压缩，提出工程组织全过程的可靠性计算模型，并通过对X-51任务计划的分析，进一步说明所提模型的使用方法和实际用途。
（3）对航天器发射工程灾难症候的有效获取和综合利用，可以提高工程灾难评估的准确性，降低灾难预测的模糊性。依据航天器发射工程各系统间的依存关系构建故障依存关系树（Failure Dependency Tree，FDT），并以此为框架，分别利用证据理论和Bayesian网络推理讨论症候信息融合和症候信息推理两类方法，构建航天器发射工程灾难评估模型。基于证据理论的症候信息融合方法可使不同领域专家采用不同观察手段所获取的各种局部的、不完整、不确定的灾难症候相互补充、消除冗余，达到降低不确定性目的。基于Bayesian网络的症候信息推理方法可有效避免症候信息融合方法易忽略少数正确意见的弊端，二者结合使用可以有效保证航天器发射工程灾难评估导向清晰、正确的结论。STS-51L灾难评估的实例分析，进一步验证了本文所提灾难评估模型的有效性。
The reliability and disaster assessment of spacecraft launch which is in the finalstage of the construction of aerospace engineering plays an important role in avoidingengineering disaster. In recent years, the reliability of space products continues to beimproved，but the natural pioneering and complexity of aerospace engineering result indisasters from time to time. Moreover, the study of reliability and disaster assessment ofspacecraft launch engineering (SLE) is still under growth, and we have yet to seeconcrete results from the research for human, organization and process reliability.Therefor, comprehensive using recent research results from related disciplines,researching theory and method for the spacecraft launch engineering, establishingmodel with all-round combining symptoms of extreme events in order to effectivelyestimate the reliability and disaster in a SLE is viteal important in theory and reality.
On the basis of the summary and analysis of relevant research results at home andabroad, considering the engineer practice background, associating the simulation data,this paper establishs and verifies the human reliability, the organization reliability, thescheduler reliability and the engineering disaster assessment model for SLE. The mainwork, contribution and conclusion are as follows:
(1) There exist irreplaceable and compensatory facts in the discourse domain ofhuman reliability and organization reliability. The purpose of examining human andorganization reliability by the composition of state of irreplaceable and compensatoryfactors can be attained. Based on field data and knowledge on SLE practice, this paperdiscusses the factor space of human and organization reliability in SLE, and proposesthe method of the composition of state of irreplaceable and compensatory factors(CSICF), and establishes assessment models of human and organization reliability inSLE, and validates the effectiveness and feasibility of the proposed method through theworking team reliability assessment for the flight control command delivery activity inthe mission XL-51, and initially solves the methodological issues for how toquantitatively assess human and organization reliability in SLE.
(2) From aerospace products into the launch site to the vehicle ignition, Changesabout the states of sub-processes present the Markov property and state transfer ratesonly relate to residence time of system in the state. Therefor, we can applying the modelof continuous-time Markov chain (CTMC) to simulate organization processes of a SLE.On the basis of the comprehensive study the mission reliability of aerospace productsand ground equipments and organization reliability, according to the constraintrelationship between sub-processes, this paper constructs the model of CTMC fororganization processes of SLE and gives the calculation method of the model parameters.Whereafter, the paper discusses the problems of how to compress the state space of multi-CTMC concurrency model by using bisimulation equivalence relation (BER), andpresents a model of how to calculate the reliability of the engineering organizationoverall process. Finally, through the X-51mission schedul analysis, the paper furtherillustrates that the model is of practical use and method of use.
(3) Effective access and comprehensive utillization of disaster symptoms in SLEcan improve the accurancy of the disaster assessment and reduce the fuzziness of thedisaster forecast. According to the dependencies between systems in SLE, this paperdesigns failure dependency tree (FDT). And to take the FDT as a framework, the paperdiscusses the symptom information fusion method (SIFM) based on evidence theory,and studies the symptom network inference method (SNIM) by using Bayesian networkinference, and constructs the disaster assessment model of SLE. The SIFM based onevidence theory can make all kinds of partial, incomplete, uncertain disaster symptomsacquired by experts in different fields with different methods mutual compensation, toeliminate redundant, to reduce uncertainty. The SNIM based on Bayesian networkinference can effectively avoid the drawback that the SIFM easily leads to the minorityopinion being ignored. A combination of both can effectively guarantee the clear andcorrect result for disaster assessment in SLE. The case analysis for STS-51L disasterassessment verifies the effectiveness of disaster assessment model proposed by thispaper.
引文
[1]谢红卫等.装备试验评估中的变动统计问题与方法[J].宇航学报,2010,31(11):2427-2437.
[2] Imai M. Kaizen: The key to Japan’s Competitive Success [M]. McGraw-Hill, NewYork,1986.
[3] Yacov Y Haimes. Risk modeling, Assessment, and Management (Third Edition)[M]. Hoboken: John Wiley&Sons,2009.
[4] David M. Harland, Ralph D. Lorenz. Space Systems Failures: Disasters andRescues of Satellites, Rockets and Space Probes [M]. Springer,2007.
[5] David J. Shayler. Disasters and Accidents in Manned Spaceflight. Springer,2005.
[6] Lodon D.修习数学[M].徐诚浩译,上海:上海科学技术出版社,1995.
[7]邹心遥,姚若河.小子样统计理论及IC可靠性评估[J].控制与决策,2008,23(3):241-245.
[8]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,1981,9:70-74.
[9]张金槐.分布参数可变的Bayes估计[J].飞行器测控学报,2001,20(4):34-38.
[10]张金槐.多层验前信息下多维动态参数的Bayes试验分析[J].飞行器测控学报,2005,24(1):51-54.
[11]张士峰,杨万君.异总体统计问题的Bayes分析[J].战术导弹技术,2003:33-37.
[12] Barlow R E, Scheuer E M. Reliability growth during a development testingprogram[J]. Technometrics,1966,8(1):53-60.
[13] Mazzuchi T A, Soyer R. A Bayes method for assessing product reliability duringdevelopment testing[J]. IEEE Transactions on Reliability,1993,42(3):503-510.
[14] Calabria R, Pulcini G. Bayes inference for the modulated power law process[J].Comm. Statist.-Theory and Methods,1997,26(10):2421-2438.
[15] Hamada M S, Wilson A G, Reese C S, et al. Bayesian Reliability[M]. SpringerPress,2008.
[16] Tan F, Jiang F, Bae S J. Generalized linear mixed models for reliability analysis ofmulti-copy repairable systems[J]. IEEE Transactions on reliability,2007,56(1):106-114.
[17] Li Q, Wang H, Liu J. Small sample Bayesian analyses in assessment of weaponperformance[J]. Journal of Systems Engineering and Electronics,2007,18(3):545-550.
[18]李鹏波,谢红卫,张金槐.考虑验前信息可信度时的Bayes估计[J].国防科技大学学报,2003,25(4):107-110.
[19] Yan Z Q, Li X X, Xie H W, et al. Bayesian synthetic evaluation of multistagereliability growth with instant and delayed fix modes[J]. Journal of SystemEngineering and Electronics,2008,19(6):1287-1294.
[20]菜洪,张士峰,张金槐. Bayes试验分析与评估[M].长沙:国防科学技术大学出版社,2004.
[21]傅惠民.整体推断的极大似然方法[J].机械强度,2002,24(1):1-5.
[22] Berger J O. Statistical decision theory and Bayesian analysis[M]. Springer-VerlagPress,1985.
[23] Liu J S. Monte carlo strategies in scientific computing[M]. Springer-Verlag Press,1985.
[24]张力.概率安全评价中人因可靠性分析技术[M].北京:原子能出版社,2006.
[25] Rasmussen J. Information processing and human machine interaction An approachto cognitive engineering[M]. Amsterdam North-Holland1986.
[26] Reason J．Human error[M]. New York Cambridge University Press,1990.
[27] Hollnagel E. Cognitive reliability and error analysis method[M]. Oxford, UK:Elsevier Science Ltd.,1998.
[28] Rouse W B, Rouse S H. analysis and classification of human error[J]. IEEETransactions on systems Man and Cybernetics1983,13(4):539-549.
[29]李鹏程,陈国华,张力等.人因可靠性分析技术的研究进展与发展趋势[J].原子能科学技术,2011,45(3):229-340.
[30] Sasou K, Nagasake A, Yukimachi T. A study on the operating team activity of anuclear power plant under abnormal operating conditions[J]. Safety Science1993,81:143-156.
[31] Sasou K, Takano K, Yoshimura S. Modeling of a team’s decision-making processJ. Safety Science1996,24(1):13-33.
[32] Shu Yufei, Furuta K, Kondo S. Team performance modeling for HRA in dynamicsituations[J]. Reliability Engineering and System Safety200278111-121.
[33] Reason J. Managing the risks of organizational accidents[M]. Aldershot: AshgatePub Ltd,1997.
[34] Naval Safety Center. Human factors analysis and classification system—HFACSDOT/FAA/AM—00/7[R]. Washington D. C.: Office of Aviation Medicine,2000.
[35] Leveson N. A new accident model for engineering safety systems[J]. SafetyScience,2004,42237-270.
[36] Mohaghegh Z, Mosleh A. Incorporating organizational factors into probabilisticrisk assessment of complex socio-technical systems: Principles and theoreticalfoundations[J]. Safety Science,2009,47:1139-1158.
[37] Mohaghegh Z, Kazemi R, Mosleh A. Incorporating organizational factors intoprobabilistic risk assessment of complex socio-technical systems: A hybridtechnique formalization[J]. Reliability Engineering and System Safety,2009,941000-1018.
[38] DHILLONBS. Human reliability with human factors [M]. Oxford, UK: PergamonBooks Inc.,1986.
[39] Liew L. Human Reliability Evaluation for a Keyboarding Task [D]. MississippiState University,1999.
[40] Kirwan B. A Guide to Practical Human Reliability Assessment [M]. Taylor&Francis,1994.
[41] Staal M A, Stress. Cognition and Human Performance: A Literature Review andConceptual Framework [R]. NASAP TM-2004–212824,2004.
[42] Swain A D, Guttmann H E. A handbook of human reliability analysis withemphasis on nuclear power plant applications, NUREG/CR-1278[R]. WashingtonD.C: USNRC,1983.
[43] Williams J C. A Data-based Method for Assessing and Reducing Human Error toImprove Operational Per formance [C]//Proc. IEEE4th Human Factor and PowerPlants,1988:436-453.
[44] Hanaman G W, Spurgin A J, Lukic Y. Human Cognitive Reliability model forPRA analysis (NUS24531)[R]. Draft EPRI Report, Electric Power ResearchInstitute,1984.
[45] Wreathall J. Operator Action Trees. An Approach to Quantifying Operator ErrorProbability during Accident Sequences [R]. NUS24159. San Diego, CA: NUSCorporation,1982.
[46] USNRC. Reactor safety study-an assessment of accident risks in US commercialnuclear power plants, NUREG-75/014, WASH-1400[R]. Washington D. C.:USNRC1975.
[47]童节娟,余少杰,赵军.核电站人员可靠性分析方法THERP中的班组因素[J].清华大学学报(自然科学版).2010,50(9):1425-1428.
[48] Swain A D. Accident sequence evaluation program human reliability analysisprocedure NUREG/CR-4772[R]. Washington D. C.: USNRC1987.
[49] Barriere M. Technical Basis and Implementation Guidelines for a Technique forHuman Event Analysis (ATHEANA)(NUREG21624)[R]. Rev.1, Washington,DC: US Nuclear Regulatory Commission,2000.
[50] Forester J, Bley D, Cooper S E. Expert Elicitation Approach for PerformingATHEANA Quantification [J]. Reliability Engineering and System Safety,2004,83(2):207–220.
[51] Marseguerra M., Zio E., Librizzi M. Quantitative Developments in the CognitiveReliability and Error Analysis Method (CREAM) for the Assessment of HumanPerformance [J]. Annals of Nuclear Energy,2006,10(33):894-910
[52] Bieder C, Le Bot P, Desmares E, et al. Mermos: EDF's New Advanced HRAMethod [C]//International4th Conference on Probabilistic Safety Assessment andManagement,1998:129-134.
[53] Chang Y H J, Mosleh A. Cognitive modeling and dynamic probabilistic simulationof operating crew response to complex system accidents[J]. Reliability engineeringand System Safety,2007,92:1014-1101.
[54] BORING R L. Dynamic human reliability analysis: Benefits and challenges ofsimulating human performance [R/OL].2007. http://www.inl.gov/technicalpublications/Documents/3735688.pdf.
[55] WOODS D D, ROTH E M, POPLE H. Cognitive environment simulation: Anartificial intelligence system for human performance assessment,NUREG/CR-4862[R]. Washington D. C.: USNR1987.
[56] SMIDTS C, SHEN S H, MOSLEH A. The IDA cognitive model for the analysis ofnuclear power plant operator response under accident conditions Part1Problemsolving and decision making model [J]. Reliability Engineering and System Safety1997,55:51-71.
[57] ZIOE. Reliability engineering Old problems and new challenges [J]. ReliabilityEngineering and System Safety,2009,94:125-141.
[58] B. Wilpert, P. Klumb. Reliability and Safety in Hazardous Work System:Approaches to Analysis and Design [M]. Psychology Press,1993.
[59] Martha Grabowski, Karlene H R. Human and Organizational Error in LargeSystem [R]. IEEE. Transaction on System: Man and Cybemetics-part A Systemand Human.1996,26(1).
[60] Marden P. Procedures in the Nuclear Industry [M]. London UK: Taylor&Franicis,1996.
[61] Nuelear Energy Ageny. Committee on the Safety of Nuclear Instailations.identification and assessment of organizational factors related to the Safety ofNPPs [R]. NEA/CSNI/R(98)17/VOL1,1999February.
[62] A Neal, M A Griffin, P M Hart. The impact of organizational climate on safetyclimate and individual behavior. Safety Science,2000,34:99-109.
[63]宋洪涛.考虑组织管理因素整体影响的人因可靠性分析方法研究[J].南华大学学报,2008,(5):50-56.
[64]肖东生.核电站安全的组织因素研究[D].中南大学,2010.
[65] W. S. Humphrey, W. L Sweet. A method for assessing the software engineeringcapability of contractors [R]. USA, SEI Technical Report (SEI-87-TR-23),1989.
[66]哈罗德·科兹纳,张增华,吕义怀译.项目管理的战略规划:项目管理成熟度模型的应用[M].北京:电子工业出版社,2002.
[67] James S. Pennypacker, Kevin P. Grant. Project management maturity: An industrybenchmark J]. Project Management Journal,2003,34(1):4-11.
[68] Young Hoon Kwak, C. WilliamIbbs. Project Management Process Maturity(PM)2Model [J]. Journal of Management In Engineering,2002,(7):150-155.
[69] PMI organization. Project Management Maturity Model Hand Book [R]. USA,PMI Global Congress,2003.
[70] IEEE Std1624-2008, IEEE Standard for Organizational ReliabilityCapability[S].
[71] Louis J Gullo, Diganta Das, Sanjay Tiku. Assessment of Organizational reliabilitycapability [J]. IEEE Components and Packaging Technologies, IEEE Transactionson2006,29(2):425-428.
[72] Tiku S., Azarian M., and Pecht M.. Using a Reliability Capability Maturity Modelto Benchmark Electronics Companies [J]. International Journal of Quality&Reliability Management vol.24, no.5(2007):pp.547–563.
[73] Sanjay Tiku, Michael Pecht. Validation of reliability capability evaluation modelusing a quantitative assessment process [J]. International Journal of Quality&Reliability Management,2010,27(8):938-952.
[74] XING L. reliability evaluation of phased-mission systems with imperfect faultcoverage and common-cause failures [J]. IEEE Transactions on Reliability,2007,56(1):58-56.
[75] XING L. reliability analysis of phased-mission systems with combinatorial phaserequirements [M]. Annual reliability and Maintainability Symposium.2001:344-51.
[76] TANG Z, DUGAN J B. BDD-based reliability analysis of phased-mission systemswith multimode failures [J]. IEEE Transactions on Reliability,2006,55(2):350-60.
[77] ZANG X, WANG D, SUN H, et al. A BDD-based algorithm for analysis ofmultistate systems with multistate components [J]. IEEE Transactions onComputers,2003,52(12):1608-18.
[78] Tang Zhihua, Xu Hong, Dugan J B. Reliability Analysis of Phased MissionSystems with Common Cause Failure[C]//Proc. of2005Annual Reliability&Maintainability Symposium. New York, USA: IEEE Press,2005:313-318.
[79] KIM K, PARK K S. phased-mission system reliability under Markov environment[J]. IEEE Transactions on Reliability,1994,43(2):301-9.
[80] ALAM M, SONG M, HESTER S L, et al. reliability analysis of phased-missionsystems: a practical approach[C]//Proceeding of the Annual Reliability&Maintainability Symposium. New York, USA: IEEE Press,2006:551-558.
[81] Murphy K E, CARTER C M, MALERICH A W. reliability analysis ofphased-mission system: a correct approach [C]//Proceeding of the AnnualReliability&Maintainability Symposium. New York, USA: IEEE Press,2007:7-12.
[82] MURA I, BONDAVALLI. Hierarchical modeling and evaluation ofphased-mission systems [J]. IEEE Transactions on Reliability,1999,48(4):360-8.
[83] MO Y C, SIEWIOREK D, YANG X Z. Mission reliability analysis offault-tolerant multiple-phased systems [J]. Reliability Engineering and SystemSafety,2008,93(7):1036-46.
[84]李岩,王社伟.一种新型的多阶段任务系统可靠性分析方法[J].计算机仿真.2008,25(1):100-4.
[85] Yong Ou, Leila Meshkat, Joanne Bechta Dugan. Multi-phase reliability analysisfor dynamic and static phase [C]//Proceedings Anuual Reliability andMaintainability Symposium. IEEE,2002:404-410.
[86] Wang D, TRIVEDI K S. Reliability Analysis of Phased-Mission System WithIndependent Component Repairs [J]. IEEE Transactions on Reliability,2007,56(3):540-50.
[87]吴尽召,王永祥,谭广平.交互式马尔科夫链[M].北京:科学出版社,2007.
[88] Sheldon M. Ross. Introduction to Probability Models(Ninth Edition)[M].Academic Press,2006.
[89] Rob J Van Glabbeek. The Linear time-branching time spectrum [M]. Inst. fürInformatik, Technische Universit t,1990.
[90] Clark E M, Long D E, Mcmillan K L. Compositional model checking [M]. Boston:MIT Press,1999.
[91] Baier C, Katoen J P, Hermanns H, et al. Simulation for Continuous-time MarkovChains [M]. Uni.Bonn: Technical Report,2002.
[92] Lowrance W. Of Acceptable Risk[C]. William Kaufmann, Los Altos,1976.
[93] Goovaerts M, Kaas R, Dhaene J. Economic capital allocation derived from riskmeasures [J]. Insurance Mathematics and Economics.2003,32:168-169.
[94] Embrechts P, Kluppelberg C, Mikosch T. Modelling extremal events for insuranceand finance [M]. New York: Springer.1997.
[95] Embrechts P. Extremes and integrated risk management [M]. London: Risk WatersGroup.2000.
[96] Galambos J. Characterizations of probability Distributions: A Unified Approachwith an Emphasis on Exponential and Related Models [M]. Springer-Verlag,Berlin.1978.
[97] Castillo E L. Extreme Value Theory in Engineering [M]. Academic Press, Boston.1988.
[98] McNeil A J, Saladin T. The peaks over thresholds method for estimating highquantities of loss distributions. In: Proceedings of28th International ASTINColloquium.1997.
[99] Asbeck E L, and Y Y Haimes. The Partitioned multi-objective risk method(PMRM)[J]. Large Scale System6(1),13-38.
[100]Michael S. Probability Risk Assessment Procedures Guide for NASAManagements and Practitioners [M]. Office of safety and Mission AssuranceNASA Headquarters, Washington, DC. March31,2002.
[101]ECSS-M-00-03B. Space project management-Risk management [S].2004.
[102]Steven L C et al. Optimizing the Design of Spacecraft Systems Using Risk asCurrency[C]. IEEE Aerospace Conference Proceedings，2002.
[103]Steven Pender. Managing Incomplete Knowledge: Why Risk Management Is notSufficient [J]. International Journal of project Management，2001，vol.19.
[104]Hyo-Nam Cho et al. A Risk Assessment Methodology for IncorporatingUncertainties Using Fuzzy Concepts [J]. Reliability Engineering and SystemSafety，2002，Vol.78:173-183.
[105]王立炜,冯卫宁.航天型号研制风险管理方法研究及应用[J].质量与可靠性，2010,1:4-8.
[106]李洪兴.因素空间理论与知识表示的数学框架(Ⅰ)[J].北京师范大学学报(自然科学版),1996,32(4):470-475.
[107]GB/T19001-2009,质量管理体系要求[S].
[108]Baier C, Katoen J P, Hermanns H, Haverkort B. Simulation for Continuous-timeMarkov Chains[M]. Uni.Bonn:Technical Report,2002.
[109]Desharnais J, Panangaden P. Continuous stochastic logic characterizesbisimulation of continuous-time Markov Chains[J]. J. Log. Algebr. Program,2003,56(1-2):99-115.
[110]Philippou A, Lee I, Sokolsky O. Weak bisimulation for probabilistic systems[C].11th international Conference on Concurrency Theory, Lecture Notes in ComputerScience,2000,1877:334-349.
[111]Segala R, Lynch N A. Probabilistic simulations for probabilistic processes[J].Nord. J. Comput,1995,2(2):250-273.
[112]Ishikawa A, Amagasa M, Shiga T. The max-min Delphi method and fuzzy Delphimethod via fuzzy integral [J]. Fuzzy Set and System,1993,55:241-253.
[113]Takehiro I. On consistent coalitions in group decision making with flexibledecision makers [J]. Applied Mathematics and Computation,2000,109:101-119.
[114]Lee H S. Optimal consensus of fuzzy opinions under group decision makingenvironment [J]. Fuzzy Set and Systems,2002,132(3):303-315.[J].
[115]Salo A A. Interactive decision aiding for group decision support [J]. EuropeanJournal of Operational Research,1995,84:134-149.
[116]Claussen J A, Kemper D, Kossmann C. Exploiting early sorting and earlypartitioning for decision support query processing [J]. The VLDB Journal,2000,9:190-213.
[117]Shafer G. A Mathematical Theory of Evidence [M]. Princeton: PrincetonUniversity Press,1976.
[118]Wang J, Yang J B, Sen P. Multi-person and multi-attribute design evaluationsusing evidential reasoning based on subjective safety and cost analyses [J].Reliability Engineering and System Safety,1996,52(2):113-128.
[119]Wang Y M, Yang J B, Xu D L, et al. Environmental impact assessment using theevidential reasoning approach [J]. European J of Operational Research,2006,174(3):1885-1913.
[120]Beynon M J. A method of aggregation in DS/AHP for group decision-making withthe non-equivalent importance of individuals in the group [J]. Computers&Operations Research,2005,32(7):1881-1896.
[121]Smets P. The Combination of Evidence in the Transferable Belief Model [J]. IEEETrans. On Pattern Analysis and Machine Intelligence,1990,12(5):447-458.
[122]Yager R R. On the Dempster-Shafer Framework and New Combination Rules [J].IEEE Trans. On System,1989,41(2):93-137.
[123]Horiuchi T. Decision Rule for Pattern Classification by Integrating IntervalFeature Values [J]. On Pattern Analysis and Machine Intelligence,1998,20(4):440-448.
[124]Lefevre E, Clot O, et al. A generic Framework for Resolving the Conflict in theCombination of Belief structures [C]. The3rd International conference oninformation fusion,2000,182-188.
[125]Dezert J, Foundations for A New Theory of Plausible and Paradoxical Reasoning[J]. Information&Security,2002,9(1):90-95.
[126]Murphy C K. Combining Belief Function when Evidence Conflicts[J]. DecisionSupport System,2000,29(1):1-9.
[127]Smarandache F, Dezert J. Advances and Applications of DSmT for informationFusion Vol II [M]. Rehoboth: American Research Press,2006.
[128]Smets P. Decision making in the TBM: The necessity of the pignistictransformation[J]. international Journal of Approximate Reasoning,2005,38,133-147.
[129]Chien C, Chen S, Lin Y. Using Bayesian Network for Fault Location onDistribution Feeder of Electrical Power Delivery System [J]. IEEE Transactions onPower Delivery,2002,17(13):785-793.
[130]Das S, Grey R, and Gonsalves P. Situation assessment via Bayesian beliefnetworks. In Proceedings of Fifth international Conference on Information Fusion,Annapolis, MD, July2002.
[131]Mikko Koivisto, Kismat Sood. Exact Bayesian Structure Discovery in BayesianNetworks [J]. Journal of Machine Learning Research5(May),2004,549-573.
[132]Gyftodimos E, Flach P A. Learning hierarchical Bayesian network for humanskill modeling [C]. Proc of the2003UK Workshop on Computational Intellgence.Bristol: University of Bristol,2003:55-62.
[133]Neville J. Statistical model and analysis techniques for learning relational data [D].Amherst: University of Massachusetts Amherst,2006.
[134]Murphy K P. Dynamic Bayesian networks: Representation, inference and learning[D].Berkeley: University of Colifornia,2002.
[135]Beerenwinkel N, Eriksson N, Sturmfels B. Conjunctive Bayesian network [J].Bemoulli,2007,13(4):893-909.
[136]NASA. System Engineering Handbook. NASA/SP-2007-6105Rev1,2007.
[137]栾恩杰.航天系统工程运行[M].北京:中国宇航出版社,2010.
[138]Roger L. Boyer, Teri L. Hamlin, et al. Use of Probabilistic Risk Assessment(PRA) in the Shuttle Decision Making Process. Long Beach, CA,2011.
[139]Yacov Y haimes. and P. Jiang. Leontief-based model of risk in complexinterconnected infrastructures [J]. Journal of infrastructures System2001,7(1):1-12.
[140]Wiegmann D A, Shappell S A. A Human Error Approach to Aviation AccidentAnalysis [M]. Burlington: Ashgate Publishing Company,2003.
[141]杨风暴,王肖霞. D-S证据理论的冲突证据合成方法[M].北京：国防工业出版社，2010.
[142]Sheldon M. Ross. Introduction to Probability Models(Ninth Edition)[M].Academic Press,2006.
[143]崔吉俊等.航天发射试验工程[M].北京:宇航出版社,2010.
[144]简祯富.决策分析与管理[M].北京：清华大学出版社，2007.
[145]Chien C. Modifying the inconsistency of Bayesian networks and a comparisionstudy for fault location on electricity distribution feeders. International journal ofOperational Research,1/2:188~203.
[146]Committee on Shuttle Criticality Review and Hazard Analysis of the Aeronauticsand Space Engineering Board. Post-Challenger Evaluation of Space Shuttle RiskAssessment and Management [R], National Research Council, National AcademyPress, Washington, DC.,1988.
[147]胡世祥.我国载人航天工程建设质量的总体思考与实践[J].载人航天，2003(1),4-9.
[148]谢静.组织的决策过程与信息的传播和解释[J].今传媒，2007(1)，30-31.
[2] Imai M. Kaizen: The key to Japan’s Competitive Success [M]. McGraw-Hill, NewYork,1986.
[3] Yacov Y Haimes. Risk modeling, Assessment, and Management (Third Edition)[M]. Hoboken: John Wiley&Sons,2009.
[4] David M. Harland, Ralph D. Lorenz. Space Systems Failures: Disasters andRescues of Satellites, Rockets and Space Probes [M]. Springer,2007.
[5] David J. Shayler. Disasters and Accidents in Manned Spaceflight. Springer,2005.
[6] Lodon D.修习数学[M].徐诚浩译,上海:上海科学技术出版社,1995.
[7]邹心遥,姚若河.小子样统计理论及IC可靠性评估[J].控制与决策,2008,23(3):241-245.
[8]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,1981,9:70-74.
[9]张金槐.分布参数可变的Bayes估计[J].飞行器测控学报,2001,20(4):34-38.
[10]张金槐.多层验前信息下多维动态参数的Bayes试验分析[J].飞行器测控学报,2005,24(1):51-54.
[11]张士峰,杨万君.异总体统计问题的Bayes分析[J].战术导弹技术,2003:33-37.
[12] Barlow R E, Scheuer E M. Reliability growth during a development testingprogram[J]. Technometrics,1966,8(1):53-60.
[13] Mazzuchi T A, Soyer R. A Bayes method for assessing product reliability duringdevelopment testing[J]. IEEE Transactions on Reliability,1993,42(3):503-510.
[14] Calabria R, Pulcini G. Bayes inference for the modulated power law process[J].Comm. Statist.-Theory and Methods,1997,26(10):2421-2438.
[15] Hamada M S, Wilson A G, Reese C S, et al. Bayesian Reliability[M]. SpringerPress,2008.
[16] Tan F, Jiang F, Bae S J. Generalized linear mixed models for reliability analysis ofmulti-copy repairable systems[J]. IEEE Transactions on reliability,2007,56(1):106-114.
[17] Li Q, Wang H, Liu J. Small sample Bayesian analyses in assessment of weaponperformance[J]. Journal of Systems Engineering and Electronics,2007,18(3):545-550.
[18]李鹏波,谢红卫,张金槐.考虑验前信息可信度时的Bayes估计[J].国防科技大学学报,2003,25(4):107-110.
[19] Yan Z Q, Li X X, Xie H W, et al. Bayesian synthetic evaluation of multistagereliability growth with instant and delayed fix modes[J]. Journal of SystemEngineering and Electronics,2008,19(6):1287-1294.
[20]菜洪,张士峰,张金槐. Bayes试验分析与评估[M].长沙:国防科学技术大学出版社,2004.
[21]傅惠民.整体推断的极大似然方法[J].机械强度,2002,24(1):1-5.
[22] Berger J O. Statistical decision theory and Bayesian analysis[M]. Springer-VerlagPress,1985.
[23] Liu J S. Monte carlo strategies in scientific computing[M]. Springer-Verlag Press,1985.
[24]张力.概率安全评价中人因可靠性分析技术[M].北京:原子能出版社,2006.
[25] Rasmussen J. Information processing and human machine interaction An approachto cognitive engineering[M]. Amsterdam North-Holland1986.
[26] Reason J．Human error[M]. New York Cambridge University Press,1990.
[27] Hollnagel E. Cognitive reliability and error analysis method[M]. Oxford, UK:Elsevier Science Ltd.,1998.
[28] Rouse W B, Rouse S H. analysis and classification of human error[J]. IEEETransactions on systems Man and Cybernetics1983,13(4):539-549.
[29]李鹏程,陈国华,张力等.人因可靠性分析技术的研究进展与发展趋势[J].原子能科学技术,2011,45(3):229-340.
[30] Sasou K, Nagasake A, Yukimachi T. A study on the operating team activity of anuclear power plant under abnormal operating conditions[J]. Safety Science1993,81:143-156.
[31] Sasou K, Takano K, Yoshimura S. Modeling of a team’s decision-making processJ. Safety Science1996,24(1):13-33.
[32] Shu Yufei, Furuta K, Kondo S. Team performance modeling for HRA in dynamicsituations[J]. Reliability Engineering and System Safety200278111-121.
[33] Reason J. Managing the risks of organizational accidents[M]. Aldershot: AshgatePub Ltd,1997.
[34] Naval Safety Center. Human factors analysis and classification system—HFACSDOT/FAA/AM—00/7[R]. Washington D. C.: Office of Aviation Medicine,2000.
[35] Leveson N. A new accident model for engineering safety systems[J]. SafetyScience,2004,42237-270.
[36] Mohaghegh Z, Mosleh A. Incorporating organizational factors into probabilisticrisk assessment of complex socio-technical systems: Principles and theoreticalfoundations[J]. Safety Science,2009,47:1139-1158.
[37] Mohaghegh Z, Kazemi R, Mosleh A. Incorporating organizational factors intoprobabilistic risk assessment of complex socio-technical systems: A hybridtechnique formalization[J]. Reliability Engineering and System Safety,2009,941000-1018.
[38] DHILLONBS. Human reliability with human factors [M]. Oxford, UK: PergamonBooks Inc.,1986.
[39] Liew L. Human Reliability Evaluation for a Keyboarding Task [D]. MississippiState University,1999.
[40] Kirwan B. A Guide to Practical Human Reliability Assessment [M]. Taylor&Francis,1994.
[41] Staal M A, Stress. Cognition and Human Performance: A Literature Review andConceptual Framework [R]. NASAP TM-2004–212824,2004.
[42] Swain A D, Guttmann H E. A handbook of human reliability analysis withemphasis on nuclear power plant applications, NUREG/CR-1278[R]. WashingtonD.C: USNRC,1983.
[43] Williams J C. A Data-based Method for Assessing and Reducing Human Error toImprove Operational Per formance [C]//Proc. IEEE4th Human Factor and PowerPlants,1988:436-453.
[44] Hanaman G W, Spurgin A J, Lukic Y. Human Cognitive Reliability model forPRA analysis (NUS24531)[R]. Draft EPRI Report, Electric Power ResearchInstitute,1984.
[45] Wreathall J. Operator Action Trees. An Approach to Quantifying Operator ErrorProbability during Accident Sequences [R]. NUS24159. San Diego, CA: NUSCorporation,1982.
[46] USNRC. Reactor safety study-an assessment of accident risks in US commercialnuclear power plants, NUREG-75/014, WASH-1400[R]. Washington D. C.:USNRC1975.
[47]童节娟,余少杰,赵军.核电站人员可靠性分析方法THERP中的班组因素[J].清华大学学报(自然科学版).2010,50(9):1425-1428.
[48] Swain A D. Accident sequence evaluation program human reliability analysisprocedure NUREG/CR-4772[R]. Washington D. C.: USNRC1987.
[49] Barriere M. Technical Basis and Implementation Guidelines for a Technique forHuman Event Analysis (ATHEANA)(NUREG21624)[R]. Rev.1, Washington,DC: US Nuclear Regulatory Commission,2000.
[50] Forester J, Bley D, Cooper S E. Expert Elicitation Approach for PerformingATHEANA Quantification [J]. Reliability Engineering and System Safety,2004,83(2):207–220.
[51] Marseguerra M., Zio E., Librizzi M. Quantitative Developments in the CognitiveReliability and Error Analysis Method (CREAM) for the Assessment of HumanPerformance [J]. Annals of Nuclear Energy,2006,10(33):894-910
[52] Bieder C, Le Bot P, Desmares E, et al. Mermos: EDF's New Advanced HRAMethod [C]//International4th Conference on Probabilistic Safety Assessment andManagement,1998:129-134.
[53] Chang Y H J, Mosleh A. Cognitive modeling and dynamic probabilistic simulationof operating crew response to complex system accidents[J]. Reliability engineeringand System Safety,2007,92:1014-1101.
[54] BORING R L. Dynamic human reliability analysis: Benefits and challenges ofsimulating human performance [R/OL].2007. http://www.inl.gov/technicalpublications/Documents/3735688.pdf.
[55] WOODS D D, ROTH E M, POPLE H. Cognitive environment simulation: Anartificial intelligence system for human performance assessment,NUREG/CR-4862[R]. Washington D. C.: USNR1987.
[56] SMIDTS C, SHEN S H, MOSLEH A. The IDA cognitive model for the analysis ofnuclear power plant operator response under accident conditions Part1Problemsolving and decision making model [J]. Reliability Engineering and System Safety1997,55:51-71.
[57] ZIOE. Reliability engineering Old problems and new challenges [J]. ReliabilityEngineering and System Safety,2009,94:125-141.
[58] B. Wilpert, P. Klumb. Reliability and Safety in Hazardous Work System:Approaches to Analysis and Design [M]. Psychology Press,1993.
[59] Martha Grabowski, Karlene H R. Human and Organizational Error in LargeSystem [R]. IEEE. Transaction on System: Man and Cybemetics-part A Systemand Human.1996,26(1).
[60] Marden P. Procedures in the Nuclear Industry [M]. London UK: Taylor&Franicis,1996.
[61] Nuelear Energy Ageny. Committee on the Safety of Nuclear Instailations.identification and assessment of organizational factors related to the Safety ofNPPs [R]. NEA/CSNI/R(98)17/VOL1,1999February.
[62] A Neal, M A Griffin, P M Hart. The impact of organizational climate on safetyclimate and individual behavior. Safety Science,2000,34:99-109.
[63]宋洪涛.考虑组织管理因素整体影响的人因可靠性分析方法研究[J].南华大学学报,2008,(5):50-56.
[64]肖东生.核电站安全的组织因素研究[D].中南大学,2010.
[65] W. S. Humphrey, W. L Sweet. A method for assessing the software engineeringcapability of contractors [R]. USA, SEI Technical Report (SEI-87-TR-23),1989.
[66]哈罗德·科兹纳,张增华,吕义怀译.项目管理的战略规划:项目管理成熟度模型的应用[M].北京:电子工业出版社,2002.
[67] James S. Pennypacker, Kevin P. Grant. Project management maturity: An industrybenchmark J]. Project Management Journal,2003,34(1):4-11.
[68] Young Hoon Kwak, C. WilliamIbbs. Project Management Process Maturity(PM)2Model [J]. Journal of Management In Engineering,2002,(7):150-155.
[69] PMI organization. Project Management Maturity Model Hand Book [R]. USA,PMI Global Congress,2003.
[70] IEEE Std1624-2008, IEEE Standard for Organizational ReliabilityCapability[S].
[71] Louis J Gullo, Diganta Das, Sanjay Tiku. Assessment of Organizational reliabilitycapability [J]. IEEE Components and Packaging Technologies, IEEE Transactionson2006,29(2):425-428.
[72] Tiku S., Azarian M., and Pecht M.. Using a Reliability Capability Maturity Modelto Benchmark Electronics Companies [J]. International Journal of Quality&Reliability Management vol.24, no.5(2007):pp.547–563.
[73] Sanjay Tiku, Michael Pecht. Validation of reliability capability evaluation modelusing a quantitative assessment process [J]. International Journal of Quality&Reliability Management,2010,27(8):938-952.
[74] XING L. reliability evaluation of phased-mission systems with imperfect faultcoverage and common-cause failures [J]. IEEE Transactions on Reliability,2007,56(1):58-56.
[75] XING L. reliability analysis of phased-mission systems with combinatorial phaserequirements [M]. Annual reliability and Maintainability Symposium.2001:344-51.
[76] TANG Z, DUGAN J B. BDD-based reliability analysis of phased-mission systemswith multimode failures [J]. IEEE Transactions on Reliability,2006,55(2):350-60.
[77] ZANG X, WANG D, SUN H, et al. A BDD-based algorithm for analysis ofmultistate systems with multistate components [J]. IEEE Transactions onComputers,2003,52(12):1608-18.
[78] Tang Zhihua, Xu Hong, Dugan J B. Reliability Analysis of Phased MissionSystems with Common Cause Failure[C]//Proc. of2005Annual Reliability&Maintainability Symposium. New York, USA: IEEE Press,2005:313-318.
[79] KIM K, PARK K S. phased-mission system reliability under Markov environment[J]. IEEE Transactions on Reliability,1994,43(2):301-9.
[80] ALAM M, SONG M, HESTER S L, et al. reliability analysis of phased-missionsystems: a practical approach[C]//Proceeding of the Annual Reliability&Maintainability Symposium. New York, USA: IEEE Press,2006:551-558.
[81] Murphy K E, CARTER C M, MALERICH A W. reliability analysis ofphased-mission system: a correct approach [C]//Proceeding of the AnnualReliability&Maintainability Symposium. New York, USA: IEEE Press,2007:7-12.
[82] MURA I, BONDAVALLI. Hierarchical modeling and evaluation ofphased-mission systems [J]. IEEE Transactions on Reliability,1999,48(4):360-8.
[83] MO Y C, SIEWIOREK D, YANG X Z. Mission reliability analysis offault-tolerant multiple-phased systems [J]. Reliability Engineering and SystemSafety,2008,93(7):1036-46.
[84]李岩,王社伟.一种新型的多阶段任务系统可靠性分析方法[J].计算机仿真.2008,25(1):100-4.
[85] Yong Ou, Leila Meshkat, Joanne Bechta Dugan. Multi-phase reliability analysisfor dynamic and static phase [C]//Proceedings Anuual Reliability andMaintainability Symposium. IEEE,2002:404-410.
[86] Wang D, TRIVEDI K S. Reliability Analysis of Phased-Mission System WithIndependent Component Repairs [J]. IEEE Transactions on Reliability,2007,56(3):540-50.
[87]吴尽召,王永祥,谭广平.交互式马尔科夫链[M].北京:科学出版社,2007.
[88] Sheldon M. Ross. Introduction to Probability Models(Ninth Edition)[M].Academic Press,2006.
[89] Rob J Van Glabbeek. The Linear time-branching time spectrum [M]. Inst. fürInformatik, Technische Universit t,1990.
[90] Clark E M, Long D E, Mcmillan K L. Compositional model checking [M]. Boston:MIT Press,1999.
[91] Baier C, Katoen J P, Hermanns H, et al. Simulation for Continuous-time MarkovChains [M]. Uni.Bonn: Technical Report,2002.
[92] Lowrance W. Of Acceptable Risk[C]. William Kaufmann, Los Altos,1976.
[93] Goovaerts M, Kaas R, Dhaene J. Economic capital allocation derived from riskmeasures [J]. Insurance Mathematics and Economics.2003,32:168-169.
[94] Embrechts P, Kluppelberg C, Mikosch T. Modelling extremal events for insuranceand finance [M]. New York: Springer.1997.
[95] Embrechts P. Extremes and integrated risk management [M]. London: Risk WatersGroup.2000.
[96] Galambos J. Characterizations of probability Distributions: A Unified Approachwith an Emphasis on Exponential and Related Models [M]. Springer-Verlag,Berlin.1978.
[97] Castillo E L. Extreme Value Theory in Engineering [M]. Academic Press, Boston.1988.
[98] McNeil A J, Saladin T. The peaks over thresholds method for estimating highquantities of loss distributions. In: Proceedings of28th International ASTINColloquium.1997.
[99] Asbeck E L, and Y Y Haimes. The Partitioned multi-objective risk method(PMRM)[J]. Large Scale System6(1),13-38.
[100]Michael S. Probability Risk Assessment Procedures Guide for NASAManagements and Practitioners [M]. Office of safety and Mission AssuranceNASA Headquarters, Washington, DC. March31,2002.
[101]ECSS-M-00-03B. Space project management-Risk management [S].2004.
[102]Steven L C et al. Optimizing the Design of Spacecraft Systems Using Risk asCurrency[C]. IEEE Aerospace Conference Proceedings，2002.
[103]Steven Pender. Managing Incomplete Knowledge: Why Risk Management Is notSufficient [J]. International Journal of project Management，2001，vol.19.
[104]Hyo-Nam Cho et al. A Risk Assessment Methodology for IncorporatingUncertainties Using Fuzzy Concepts [J]. Reliability Engineering and SystemSafety，2002，Vol.78:173-183.
[105]王立炜,冯卫宁.航天型号研制风险管理方法研究及应用[J].质量与可靠性，2010,1:4-8.
[106]李洪兴.因素空间理论与知识表示的数学框架(Ⅰ)[J].北京师范大学学报(自然科学版),1996,32(4):470-475.
[107]GB/T19001-2009,质量管理体系要求[S].
[108]Baier C, Katoen J P, Hermanns H, Haverkort B. Simulation for Continuous-timeMarkov Chains[M]. Uni.Bonn:Technical Report,2002.
[109]Desharnais J, Panangaden P. Continuous stochastic logic characterizesbisimulation of continuous-time Markov Chains[J]. J. Log. Algebr. Program,2003,56(1-2):99-115.
[110]Philippou A, Lee I, Sokolsky O. Weak bisimulation for probabilistic systems[C].11th international Conference on Concurrency Theory, Lecture Notes in ComputerScience,2000,1877:334-349.
[111]Segala R, Lynch N A. Probabilistic simulations for probabilistic processes[J].Nord. J. Comput,1995,2(2):250-273.
[112]Ishikawa A, Amagasa M, Shiga T. The max-min Delphi method and fuzzy Delphimethod via fuzzy integral [J]. Fuzzy Set and System,1993,55:241-253.
[113]Takehiro I. On consistent coalitions in group decision making with flexibledecision makers [J]. Applied Mathematics and Computation,2000,109:101-119.
[114]Lee H S. Optimal consensus of fuzzy opinions under group decision makingenvironment [J]. Fuzzy Set and Systems,2002,132(3):303-315.[J].
[115]Salo A A. Interactive decision aiding for group decision support [J]. EuropeanJournal of Operational Research,1995,84:134-149.
[116]Claussen J A, Kemper D, Kossmann C. Exploiting early sorting and earlypartitioning for decision support query processing [J]. The VLDB Journal,2000,9:190-213.
[117]Shafer G. A Mathematical Theory of Evidence [M]. Princeton: PrincetonUniversity Press,1976.
[118]Wang J, Yang J B, Sen P. Multi-person and multi-attribute design evaluationsusing evidential reasoning based on subjective safety and cost analyses [J].Reliability Engineering and System Safety,1996,52(2):113-128.
[119]Wang Y M, Yang J B, Xu D L, et al. Environmental impact assessment using theevidential reasoning approach [J]. European J of Operational Research,2006,174(3):1885-1913.
[120]Beynon M J. A method of aggregation in DS/AHP for group decision-making withthe non-equivalent importance of individuals in the group [J]. Computers&Operations Research,2005,32(7):1881-1896.
[121]Smets P. The Combination of Evidence in the Transferable Belief Model [J]. IEEETrans. On Pattern Analysis and Machine Intelligence,1990,12(5):447-458.
[122]Yager R R. On the Dempster-Shafer Framework and New Combination Rules [J].IEEE Trans. On System,1989,41(2):93-137.
[123]Horiuchi T. Decision Rule for Pattern Classification by Integrating IntervalFeature Values [J]. On Pattern Analysis and Machine Intelligence,1998,20(4):440-448.
[124]Lefevre E, Clot O, et al. A generic Framework for Resolving the Conflict in theCombination of Belief structures [C]. The3rd International conference oninformation fusion,2000,182-188.
[125]Dezert J, Foundations for A New Theory of Plausible and Paradoxical Reasoning[J]. Information&Security,2002,9(1):90-95.
[126]Murphy C K. Combining Belief Function when Evidence Conflicts[J]. DecisionSupport System,2000,29(1):1-9.
[127]Smarandache F, Dezert J. Advances and Applications of DSmT for informationFusion Vol II [M]. Rehoboth: American Research Press,2006.
[128]Smets P. Decision making in the TBM: The necessity of the pignistictransformation[J]. international Journal of Approximate Reasoning,2005,38,133-147.
[129]Chien C, Chen S, Lin Y. Using Bayesian Network for Fault Location onDistribution Feeder of Electrical Power Delivery System [J]. IEEE Transactions onPower Delivery,2002,17(13):785-793.
[130]Das S, Grey R, and Gonsalves P. Situation assessment via Bayesian beliefnetworks. In Proceedings of Fifth international Conference on Information Fusion,Annapolis, MD, July2002.
[131]Mikko Koivisto, Kismat Sood. Exact Bayesian Structure Discovery in BayesianNetworks [J]. Journal of Machine Learning Research5(May),2004,549-573.
[132]Gyftodimos E, Flach P A. Learning hierarchical Bayesian network for humanskill modeling [C]. Proc of the2003UK Workshop on Computational Intellgence.Bristol: University of Bristol,2003:55-62.
[133]Neville J. Statistical model and analysis techniques for learning relational data [D].Amherst: University of Massachusetts Amherst,2006.
[134]Murphy K P. Dynamic Bayesian networks: Representation, inference and learning[D].Berkeley: University of Colifornia,2002.
[135]Beerenwinkel N, Eriksson N, Sturmfels B. Conjunctive Bayesian network [J].Bemoulli,2007,13(4):893-909.
[136]NASA. System Engineering Handbook. NASA/SP-2007-6105Rev1,2007.
[137]栾恩杰.航天系统工程运行[M].北京:中国宇航出版社,2010.
[138]Roger L. Boyer, Teri L. Hamlin, et al. Use of Probabilistic Risk Assessment(PRA) in the Shuttle Decision Making Process. Long Beach, CA,2011.
[139]Yacov Y haimes. and P. Jiang. Leontief-based model of risk in complexinterconnected infrastructures [J]. Journal of infrastructures System2001,7(1):1-12.
[140]Wiegmann D A, Shappell S A. A Human Error Approach to Aviation AccidentAnalysis [M]. Burlington: Ashgate Publishing Company,2003.
[141]杨风暴,王肖霞. D-S证据理论的冲突证据合成方法[M].北京：国防工业出版社，2010.
[142]Sheldon M. Ross. Introduction to Probability Models(Ninth Edition)[M].Academic Press,2006.
[143]崔吉俊等.航天发射试验工程[M].北京:宇航出版社,2010.
[144]简祯富.决策分析与管理[M].北京：清华大学出版社，2007.
[145]Chien C. Modifying the inconsistency of Bayesian networks and a comparisionstudy for fault location on electricity distribution feeders. International journal ofOperational Research,1/2:188~203.
[146]Committee on Shuttle Criticality Review and Hazard Analysis of the Aeronauticsand Space Engineering Board. Post-Challenger Evaluation of Space Shuttle RiskAssessment and Management [R], National Research Council, National AcademyPress, Washington, DC.,1988.
[147]胡世祥.我国载人航天工程建设质量的总体思考与实践[J].载人航天，2003(1),4-9.
[148]谢静.组织的决策过程与信息的传播和解释[J].今传媒，2007(1)，30-31.