基于制导策略框架的动态概率风险评价软件平台研究
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摘要
概率风险评价(Probabilistic Risk Assessment简称PRA)是一个确保系统安全的分析过程。随着动态系统的规模和硬件、软件及人因之间相互作用的复杂程度日益增加,由传统PRA方法列举风险假想存在着极大的难度。在过去15年里,很多动态概率风险评价(Dynamic Probabilistic Risk Assessment简称DPRA)方法已作为辅助手段,用于处理大型复杂动态系统。
本文在对DPRA常用方法研究的基础上提出基于制导策略的DPRA框架,即通过工程认识来指导仿真,由此进一步提高效率、获取更高的精度。工程认识体现在计划程序上,将生成的计划作为蓝图指导仿真；调度程序通过控制时间和随机事件来指导仿真。仿真期间,随机事件在支点处被载入调度程序,由调度程序决定是否予以仿真,调度程序原则上偏重更有价值的事件。载入事件的价值取决于研究过程中获取的信息增益及重要度。获取信息的价值由平均信息量来衡量、重要度则是基于对该工程的判断及认识。仿真结果将在运行中记录并归类。计划程序从仿真结果中获取经验数据并升级,继续指导下一步仿真。
为实现基于制导策略的DPRA框架,本文开发了DPRAP(Probabilistic Risk Assessment Platform)。该软件平台包括友好的人机界面,设有DPRA模型数据库,用以辅助建立仿真模型。将工程认识载入计划程序中,可自动生成计划；随后调度程序将根据制定的计划指导仿真。通过仿真生成事故事件序列,从而估算出系统末状态的失效概率。
Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of the dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate the risky scenarios by the traditional PRA methods. Over the past 15 years, a host of DPRA methods have been proposed to serve as supplemental tools to traditional PRA to deal with complex dynamic systems.
A new dynamic probabilistic risk assessment framework is proposed in this dissertation. In this framework a new exploration strategy is employed. The engineering knowledge of the system is explicitly used to guide the simulation to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the "Planner" which is responsible for generating plans as a high level map to guide the simulation. A scheduler is responsible for guiding the simulation by controlling the timing and occurrence of the random events. During the simulation the possible random events are proposed to the scheduler at branch points. The scheduler decides which events are to be simulated. Scheduler would favor the events with higher values. The value of a proposed event depends on the information gain from exploring that scenario, and the importance factor of the scenario. The information gain is measured by the information entropy, and the importance factor is based on the engineering judgment. The simulation results are recorded and grouped for later studies. The planner may "learn" from the simulation results, and update the plan to guide further simulation.
DPRAP(Dynamic Probabilistic Risk Assessment Platform) is the software package which implements the new methodology. It provides the users with a friendly interface and a rich DPRA library to aid in the construction of the simulation mode. The engineering knowledge can be input into the Planner, which would generate a plan automatically. The scheduler would guide the simulation according to the plan. The simulation generates many accident event sequences and estimates of the end state probabilities.
本文在对DPRA常用方法研究的基础上提出基于制导策略的DPRA框架,即通过工程认识来指导仿真,由此进一步提高效率、获取更高的精度。工程认识体现在计划程序上,将生成的计划作为蓝图指导仿真；调度程序通过控制时间和随机事件来指导仿真。仿真期间,随机事件在支点处被载入调度程序,由调度程序决定是否予以仿真,调度程序原则上偏重更有价值的事件。载入事件的价值取决于研究过程中获取的信息增益及重要度。获取信息的价值由平均信息量来衡量、重要度则是基于对该工程的判断及认识。仿真结果将在运行中记录并归类。计划程序从仿真结果中获取经验数据并升级,继续指导下一步仿真。
为实现基于制导策略的DPRA框架,本文开发了DPRAP(Probabilistic Risk Assessment Platform)。该软件平台包括友好的人机界面,设有DPRA模型数据库,用以辅助建立仿真模型。将工程认识载入计划程序中,可自动生成计划；随后调度程序将根据制定的计划指导仿真。通过仿真生成事故事件序列,从而估算出系统末状态的失效概率。
Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of the dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate the risky scenarios by the traditional PRA methods. Over the past 15 years, a host of DPRA methods have been proposed to serve as supplemental tools to traditional PRA to deal with complex dynamic systems.
A new dynamic probabilistic risk assessment framework is proposed in this dissertation. In this framework a new exploration strategy is employed. The engineering knowledge of the system is explicitly used to guide the simulation to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the "Planner" which is responsible for generating plans as a high level map to guide the simulation. A scheduler is responsible for guiding the simulation by controlling the timing and occurrence of the random events. During the simulation the possible random events are proposed to the scheduler at branch points. The scheduler decides which events are to be simulated. Scheduler would favor the events with higher values. The value of a proposed event depends on the information gain from exploring that scenario, and the importance factor of the scenario. The information gain is measured by the information entropy, and the importance factor is based on the engineering judgment. The simulation results are recorded and grouped for later studies. The planner may "learn" from the simulation results, and update the plan to guide further simulation.
DPRAP(Dynamic Probabilistic Risk Assessment Platform) is the software package which implements the new methodology. It provides the users with a friendly interface and a rich DPRA library to aid in the construction of the simulation mode. The engineering knowledge can be input into the Planner, which would generate a plan automatically. The scheduler would guide the simulation according to the plan. The simulation generates many accident event sequences and estimates of the end state probabilities.
引文
[1]白晋华.确定论分析方法与概率安全评价方法的比较.核工程研究与设计,2004(11).
[2]冯炳良.概率安全分析评价-来源于核电,服务于核电.核电-大亚湾核电PSA专刊,2006：2-8.
[3]朱继洲.核反应堆安全分析.西安：西安交通大学出版社,2000：127-128.
[4]童节娟.风险技术在核电站运行和管理中的应用研究[D].北京：清华大学核能技术设计研究院,2000:1-109.
[5]简志敏.控制系统故障树自动建造的一种新方法[D].北京：清华大学自动化系,1995：11-27.
[6]谢钢.故障树自动建树专家系统研究[D].北京：清华大学核能技术设计研究院,1992:1-13.
[7]于文革.核电站保护系统概率风险评价研究[D].哈尔滨工程大学,2003.
[8]韩冰.故障树辅助建树专家系统的研究[D].北京：清华大学核能技术设计研究院,2002:1-88.
[9]Tatsuya Taminami, Kazumi Furuhashi, etc. Development of PSA Support Tool "FT-FREE" at Nuclear Power plants-Automatic FT Generation Function-In A Mosleh, R.A. Bari, eds. Proceedings of the 4th International Conference on Probabilistic Safety Assessment andManagement. 1998,9:623-630.
[10]Siu, N. Risk Assessment for Dynamic-Systems-An Overview. Reliability Engineering & System Safety.1994.43(1).
[11]Cojazzi, G.The DYLAM approach for the dynamic reliability analysis of systems. Reliability Engineering & System Safety,1996.52(3).
[12]Aldemir, T,& Zio, E. New Domain of Application:Discussion Group Ⅱ. Paper presented at the Fifth International Workshop on Dynamic Reliability: Future Directions 1998.
[13]Shults, B & Kuipers, B. Proving properties of continuous systems:qualitative
simulation and temporal logic. Artificial Intelligence Journal,1997,92.
[14]Hsueh, K.S & Mosleh, A. The development and application of the accident dynamic simulator for dynamic probabilistic risk assessment of nuclear power plants. Reliability Engineering & System Safety,1996,52(3).
[15]Mason, R. L, Gunst, R. F & Hess, J. L. Statistical Design and Analysis of Experiments:With Applications to Engineering and Science (Second editioned.),2003.
[16]Devooght, J.& Smidts, C. Probabilistic Reactor Dynamics. The Theory of Continuous Event Trees. Nuclear Science and Engineering,1992,111(3).
[17]Dubi, A. Analytic approach & Monte Carlo methods for realistic systems analysis. Mathematics and Computers In Simulation,1998,47:2-5.
[18]茆定远,薛大知.核电站PSA分析中可靠性数据处理的贝叶斯方法.核动力工程,2000,10(1).
[19]Marseguerra, M., Zio, E.& Cadini, F. Biased Monte Carlo unavailability analysis for systems with time-dependent failure rates.2002,76(1).
[20]Chaloner, K.,& Verinelli, I. Bayesian Experimental Design:A Review. Statistical Science,1995,10(3).
[21]Loredo, T. J. Bayesian Adaptive Exploration. Paper presented at the Bayesian Inference And Maximum Entropy Methods In Science And Engineering:23rd International Workshop.2003.
[22]Say, A. C. C.& Akin, H. L. Sound and complete qualitative simulation is impossible. Artificial Intelligence,2003,149(2).
[23]Shannon, C. Mathematical theory of communication. The Bell Labs Technical Journal,1948,27.
[24]Lindley, D. V. On the Measure of Information Provided by an Experiment. Annals of Statistics,1956,27(4).
[25]Dugan, J. B., Bavuso, S. J.& Boyd, M. A. Dynamic Fault-Tree Models For Fault-Tolerant Computer-Systems. Ieee Transactions On Reliability,1992, 41(3).
[26]Mosleh, A.& Chang, Y. H. Model-based human reliability analysis: prospects and requirements. Reliability Engineering & System Safety,2004, 83(2).
[27]Dubi, A. Analytic approach & Monte Carlo methods for realistic systems analysis. Mathematics And Computers In Simulation,47(2-5):243-269.
[28]Fowler, M.& Scott, K. UML Distilled:A Brief Guide to the Standard Object (Second Edition ed.):Addison Wesley.1999.
[29]Gamma, E. Helm, R. Johnson, R.& Vlissides, J. Design Patterns:Elements of reusable object orientated software (1st edition ed.):Addison-Wesley Professional.1995.
[30]张明礼,吴继伟.可靠性与风险分析程序系统及其在概率安全分析中的应用.核动力工程,2000(1).
[31]杨志军,张源芳.核电站安全参数显示系统的研究与设计.核动力工程,2000,21(1)：77-80.
[32]吴启明.载人航天系统安全性风险评估的ESD方法研究及软件实现[D].国防科技大学,2005.
[33]周春城.事件序列图方法及其应用研究[D].国防科技大学,2003.
[34]韩明.FTA法和重要度分析在某系统可靠性中的应用.运筹于管理,2000,1：58-63.
[35]F.A.Patterson-Hine,Joanne Bechta Dugan.Modular Techinques for Dynamic Fault-tree Analysis Proceedings Annual Reliability and Maintainability Symposium,1992:363-369.
[36]蒋乐天,徐国治.故障树模型与Markov模型的关系与转换.系统工程与电子技术.2005(1)：1441-1444.
[37]高顺川.动态故障树分析方法及其实现[D].国防科技大学.2005.
[38]王少萍,陶建峰,崔明山.独立子树等效时变可用度分析.北京航空航天大学学报.2004(30)：1137-114.
[39]Marko Cepin,Borut Mavko. A dynamic fault tree.Reliability Engineering and System Safety,2002(75).
[40]高顺川,冯静等.基于威布尔分布的动态故障树定量分析方法[J].2005,5：28-31.
[41]华小洋,胡宗武等.模糊故障树分析方法[J].机械强度.1998,20(1)：35-40.
[42]程明华,姚一平.动态故障树分析方法在软、硬件容错计算机系统中的应用.航空学报2000(21)：34-37.
[43]Chanda R S,Bhattacharijee P K.A Reliability Approach to Transmission Expansion Planning Using Fuzzy Fault-tree Model[J].Electric Power Systems Research,1998,45(2):101-108.
[44]Antonio C F, Nelson F F.Fuzzy FTA:A Fuzzy Fault Tree System for Uncertainty Analysis[J]. Annals of Nuclear Energy,1999,26(6):523-532.
[45]Athanassion B,Fragiskos B.Fuzzy Fault Tree Analysis as a Mechanism for Technical Support to Small/Megium Electroplaters on a Quasi Online/Real-time Basis [A].IEEE ICIT 2003[C].Maribor,2003:36-41.
[46]赵艳萍,贡文伟.模糊故障树分析及其应用研究[J].中国安全科学学报.2001,11(6)：31-36.
[47]宋华,王行仁等.T-S模糊故障树分析方法.控制与决策.2005,20(8)：854-859.
[48]高明,陈文振等.基于离散化修正模糊算子的模糊故障树新析[J].系统工程于电子技术,2006,28(5)：783-787.
[49]谢斌.贝叶斯网络在可靠性分析中的应用[D].西南交通大学,2004.
[50]沈祖培,郑涛.复杂系统可靠性的GO法精确算法[J].清华大学学报,2002,42(5)：569-572.
[51]沈祖培,黄祥瑞.GO法原理及应用-一种系统可靠性分析方法.清华大学出版社.2005.
[52]张力.人因分析：需要、问题和发展趋势.系统工程理论与实践,2001,21(6)：13-19.
[53]Swain A D. Human analysis:need, status, trends and limitations. ReliabilityEngineering and System Safety,1990,29:301-314.
[2]冯炳良.概率安全分析评价-来源于核电,服务于核电.核电-大亚湾核电PSA专刊,2006：2-8.
[3]朱继洲.核反应堆安全分析.西安：西安交通大学出版社,2000：127-128.
[4]童节娟.风险技术在核电站运行和管理中的应用研究[D].北京：清华大学核能技术设计研究院,2000:1-109.
[5]简志敏.控制系统故障树自动建造的一种新方法[D].北京：清华大学自动化系,1995：11-27.
[6]谢钢.故障树自动建树专家系统研究[D].北京：清华大学核能技术设计研究院,1992:1-13.
[7]于文革.核电站保护系统概率风险评价研究[D].哈尔滨工程大学,2003.
[8]韩冰.故障树辅助建树专家系统的研究[D].北京：清华大学核能技术设计研究院,2002:1-88.
[9]Tatsuya Taminami, Kazumi Furuhashi, etc. Development of PSA Support Tool "FT-FREE" at Nuclear Power plants-Automatic FT Generation Function-In A Mosleh, R.A. Bari, eds. Proceedings of the 4th International Conference on Probabilistic Safety Assessment andManagement. 1998,9:623-630.
[10]Siu, N. Risk Assessment for Dynamic-Systems-An Overview. Reliability Engineering & System Safety.1994.43(1).
[11]Cojazzi, G.The DYLAM approach for the dynamic reliability analysis of systems. Reliability Engineering & System Safety,1996.52(3).
[12]Aldemir, T,& Zio, E. New Domain of Application:Discussion Group Ⅱ. Paper presented at the Fifth International Workshop on Dynamic Reliability: Future Directions 1998.
[13]Shults, B & Kuipers, B. Proving properties of continuous systems:qualitative
simulation and temporal logic. Artificial Intelligence Journal,1997,92.
[14]Hsueh, K.S & Mosleh, A. The development and application of the accident dynamic simulator for dynamic probabilistic risk assessment of nuclear power plants. Reliability Engineering & System Safety,1996,52(3).
[15]Mason, R. L, Gunst, R. F & Hess, J. L. Statistical Design and Analysis of Experiments:With Applications to Engineering and Science (Second editioned.),2003.
[16]Devooght, J.& Smidts, C. Probabilistic Reactor Dynamics. The Theory of Continuous Event Trees. Nuclear Science and Engineering,1992,111(3).
[17]Dubi, A. Analytic approach & Monte Carlo methods for realistic systems analysis. Mathematics and Computers In Simulation,1998,47:2-5.
[18]茆定远,薛大知.核电站PSA分析中可靠性数据处理的贝叶斯方法.核动力工程,2000,10(1).
[19]Marseguerra, M., Zio, E.& Cadini, F. Biased Monte Carlo unavailability analysis for systems with time-dependent failure rates.2002,76(1).
[20]Chaloner, K.,& Verinelli, I. Bayesian Experimental Design:A Review. Statistical Science,1995,10(3).
[21]Loredo, T. J. Bayesian Adaptive Exploration. Paper presented at the Bayesian Inference And Maximum Entropy Methods In Science And Engineering:23rd International Workshop.2003.
[22]Say, A. C. C.& Akin, H. L. Sound and complete qualitative simulation is impossible. Artificial Intelligence,2003,149(2).
[23]Shannon, C. Mathematical theory of communication. The Bell Labs Technical Journal,1948,27.
[24]Lindley, D. V. On the Measure of Information Provided by an Experiment. Annals of Statistics,1956,27(4).
[25]Dugan, J. B., Bavuso, S. J.& Boyd, M. A. Dynamic Fault-Tree Models For Fault-Tolerant Computer-Systems. Ieee Transactions On Reliability,1992, 41(3).
[26]Mosleh, A.& Chang, Y. H. Model-based human reliability analysis: prospects and requirements. Reliability Engineering & System Safety,2004, 83(2).
[27]Dubi, A. Analytic approach & Monte Carlo methods for realistic systems analysis. Mathematics And Computers In Simulation,47(2-5):243-269.
[28]Fowler, M.& Scott, K. UML Distilled:A Brief Guide to the Standard Object (Second Edition ed.):Addison Wesley.1999.
[29]Gamma, E. Helm, R. Johnson, R.& Vlissides, J. Design Patterns:Elements of reusable object orientated software (1st edition ed.):Addison-Wesley Professional.1995.
[30]张明礼,吴继伟.可靠性与风险分析程序系统及其在概率安全分析中的应用.核动力工程,2000(1).
[31]杨志军,张源芳.核电站安全参数显示系统的研究与设计.核动力工程,2000,21(1)：77-80.
[32]吴启明.载人航天系统安全性风险评估的ESD方法研究及软件实现[D].国防科技大学,2005.
[33]周春城.事件序列图方法及其应用研究[D].国防科技大学,2003.
[34]韩明.FTA法和重要度分析在某系统可靠性中的应用.运筹于管理,2000,1：58-63.
[35]F.A.Patterson-Hine,Joanne Bechta Dugan.Modular Techinques for Dynamic Fault-tree Analysis Proceedings Annual Reliability and Maintainability Symposium,1992:363-369.
[36]蒋乐天,徐国治.故障树模型与Markov模型的关系与转换.系统工程与电子技术.2005(1)：1441-1444.
[37]高顺川.动态故障树分析方法及其实现[D].国防科技大学.2005.
[38]王少萍,陶建峰,崔明山.独立子树等效时变可用度分析.北京航空航天大学学报.2004(30)：1137-114.
[39]Marko Cepin,Borut Mavko. A dynamic fault tree.Reliability Engineering and System Safety,2002(75).
[40]高顺川,冯静等.基于威布尔分布的动态故障树定量分析方法[J].2005,5：28-31.
[41]华小洋,胡宗武等.模糊故障树分析方法[J].机械强度.1998,20(1)：35-40.
[42]程明华,姚一平.动态故障树分析方法在软、硬件容错计算机系统中的应用.航空学报2000(21)：34-37.
[43]Chanda R S,Bhattacharijee P K.A Reliability Approach to Transmission Expansion Planning Using Fuzzy Fault-tree Model[J].Electric Power Systems Research,1998,45(2):101-108.
[44]Antonio C F, Nelson F F.Fuzzy FTA:A Fuzzy Fault Tree System for Uncertainty Analysis[J]. Annals of Nuclear Energy,1999,26(6):523-532.
[45]Athanassion B,Fragiskos B.Fuzzy Fault Tree Analysis as a Mechanism for Technical Support to Small/Megium Electroplaters on a Quasi Online/Real-time Basis [A].IEEE ICIT 2003[C].Maribor,2003:36-41.
[46]赵艳萍,贡文伟.模糊故障树分析及其应用研究[J].中国安全科学学报.2001,11(6)：31-36.
[47]宋华,王行仁等.T-S模糊故障树分析方法.控制与决策.2005,20(8)：854-859.
[48]高明,陈文振等.基于离散化修正模糊算子的模糊故障树新析[J].系统工程于电子技术,2006,28(5)：783-787.
[49]谢斌.贝叶斯网络在可靠性分析中的应用[D].西南交通大学,2004.
[50]沈祖培,郑涛.复杂系统可靠性的GO法精确算法[J].清华大学学报,2002,42(5)：569-572.
[51]沈祖培,黄祥瑞.GO法原理及应用-一种系统可靠性分析方法.清华大学出版社.2005.
[52]张力.人因分析：需要、问题和发展趋势.系统工程理论与实践,2001,21(6)：13-19.
[53]Swain A D. Human analysis:need, status, trends and limitations. ReliabilityEngineering and System Safety,1990,29:301-314.