核电厂人因可靠性分析的几个问题研究
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摘要
人因可靠性分析是核电厂概率安全评价中必不可少的一部分,良好的人因可靠性分析有助于更准确地评价系统安全,预防和减少大规模复杂人机系统中的人因事故。然而对人的认知行为机理认识的不足和人因失误数据的不规范性和匮乏一直困扰着人因可靠性分析的研究和应用。但人因可靠性分析在众多应用领域有着非常紧迫的需求。因此在这种背景下,研究显得更具开放性和探索性。我们在长期从事人因可靠性相关研究的基础上,参与了国内多家核电厂的人因可靠性分析工作。在开展人因可靠性研究和工程实践的过程中,我们遇到了很多问题,在处理和解决这些问题的同时形成了一些新的想法,提出了一些新的解决方案,希望从一些不同的角度来分析和研究人因可靠性,以期获得更为广泛、深刻的认识,从而推动人因可靠性研究和应用不断向前发展。本文所做的主要研究工作如下。
系统分析和研究了核电厂主控室操纵员的行为,并引入统一建模语言对操纵员的行为进行工作流建模,通过对操纵员行为的工作流建模不仅可以准确、形象地反映操纵员的行为特性和内在约束,实现对操纵员行为的可靠性分析。在前面建立的操纵员行为模型的基础上,采用了蒙特卡罗方法对操纵员的人因可靠性进行仿真。对仿真中的关键问题如仿真模型建立,仿真流程设计,仿真误差分析和仿真规模确定等进行了深入的理论分析并给出了合理的解决方案。并以核电厂安全评价中广为应用的人因可靠性分析方法为原型,给出了人因可靠性仿真的实现实例。
根据人因失误的机理和特点提出了一个分层的人因可靠性概率因果模型,采用贝叶斯网建立了人因可靠性影响因素之间的因果关系。采用了分层方法进行建模,充分利用了条件独立性以降低模型的复杂度,同时分层机制也符合人因可靠性的内在要求。在模型的定量化方面,设计了一个简单易行的模型参数计算方法。该模型可以用于工程化的人因可靠性分析,也可扩展为人因行为理论研究模型。另外,为了提高贝叶斯网推理计算的的效率,对贝叶斯网推理运算中用到的变量消元法进行了研究并提出了一个改进算法,新算法同时考虑了消元过程中删除的边和增加的边,通过降低图的复杂度来控制消元成本。随机仿真实验表明新算法是有效的。
针对人因可靠性分析中的数据匮乏问题,建立了层次化的人因可靠性数据分类体系,该分类体系包括人因失误模式和人因失误影响因素。采用分层结构的人因可靠性数据分类体系有利于数据的搜集和分析,树形层次化的分类体系使得人因数据的分类更为清晰,更易于对数据库进行扩展,减少了数据条目之间的交叉和重叠,增强了数据库的实用性和可用性。在理论分析的基础上,设计实现了一个人因可靠性数据库应用系统,数据库中存放了上述数据分类体系和相关数据,同时还存储了一些来源于其他人因可靠性分析方法的数据,以及国内外一些核电厂的人因失误数据和人因失误事件数据,系统提供了各种检索和数据管理功能,同时借助于系统的外推模块可以实现人因可靠性定性和定量分析。该系统的实现也为今后的研究和应用提供一些基础性的支持。
Human reliability analysis (HRA) is an essential part of probabilistic safety assessment of nuclear power plant (NPP). Good human reliability analysis leads to more accurate assessment of system safety; it can also prevent and reduce human error in large, complex human-machine system. However, the lack of understanding of human cognitive mechanism and the lack of of human error data has been plagued HRA research and application for years. But human reliability analysis has very urgent needs. Therefore, in this context, the study appears to be more open and more exploratory. We have long been engaged in human reliability research and application, including HRA in several domestic nuclear power plants. We encountered many problems in our work; we also formed some new ideas and found some new solutions in handling and solving these problems. The major research work done as follows.
Firstly, we analyzed the behavior of operators in NPP systematically, introduced the Unified Modeling Language for building workflow model of the behavior of operators. Workflow model of operators can accurately reflect the property of behavior and internal constraints. Moreover, we studied simulation method of human reliability, including some key problems in human reliability simulation, such as simulation modeling, simulation algorithm design, random numbers generation, error analysis, and simulation size decision. An example of human reliability simulation is presented referring to a widely used HRA method. Simulation results show that simulation is more effective in dealing with time interface, error propagation and sensitivity analysis. Simulation makes it possible to study human performance from different angles.
Secondly, we proposed a hierarchical causal model of human reliability. Bayesian network is used to build the causal link between different factors. Hierarchical structure is used in the causal model, which leads to a clear and simplified relationship between performance influence factors. Computational cost is also reduced largely. In the quantification of the model, we designed a simplified method to calculating the impact of factors; weights of factors were converted to conditional probability. We also proposed a novel variable elimination algorithm to improve reasoning performance of Bayesian network. In the novel algorithm, the added edges and the removed edges during elimination are considered; some methods of reducing graph complexity and controlling elimination cost are used to improve performance. Simulation results show that the new algorithm is effective.
Thirdly, in order to solve the problem of lack of data in human reliability analysis, a human reliability database system was developed. A hierarchic architecture of human reliability data was built, which included two parts, one was human error mode classification architecture, and the other was performance shape factor classification architecture. Based on theoretical analysis, a human reliability database system was implemented; useful data was stored in this database. The extrapolation module of this system can support qualitative and quantitative analysis of human reliability. This system is useful for data collecting and data analysis of human reliability, which can provide basic support for future research.
系统分析和研究了核电厂主控室操纵员的行为,并引入统一建模语言对操纵员的行为进行工作流建模,通过对操纵员行为的工作流建模不仅可以准确、形象地反映操纵员的行为特性和内在约束,实现对操纵员行为的可靠性分析。在前面建立的操纵员行为模型的基础上,采用了蒙特卡罗方法对操纵员的人因可靠性进行仿真。对仿真中的关键问题如仿真模型建立,仿真流程设计,仿真误差分析和仿真规模确定等进行了深入的理论分析并给出了合理的解决方案。并以核电厂安全评价中广为应用的人因可靠性分析方法为原型,给出了人因可靠性仿真的实现实例。
根据人因失误的机理和特点提出了一个分层的人因可靠性概率因果模型,采用贝叶斯网建立了人因可靠性影响因素之间的因果关系。采用了分层方法进行建模,充分利用了条件独立性以降低模型的复杂度,同时分层机制也符合人因可靠性的内在要求。在模型的定量化方面,设计了一个简单易行的模型参数计算方法。该模型可以用于工程化的人因可靠性分析,也可扩展为人因行为理论研究模型。另外,为了提高贝叶斯网推理计算的的效率,对贝叶斯网推理运算中用到的变量消元法进行了研究并提出了一个改进算法,新算法同时考虑了消元过程中删除的边和增加的边,通过降低图的复杂度来控制消元成本。随机仿真实验表明新算法是有效的。
针对人因可靠性分析中的数据匮乏问题,建立了层次化的人因可靠性数据分类体系,该分类体系包括人因失误模式和人因失误影响因素。采用分层结构的人因可靠性数据分类体系有利于数据的搜集和分析,树形层次化的分类体系使得人因数据的分类更为清晰,更易于对数据库进行扩展,减少了数据条目之间的交叉和重叠,增强了数据库的实用性和可用性。在理论分析的基础上,设计实现了一个人因可靠性数据库应用系统,数据库中存放了上述数据分类体系和相关数据,同时还存储了一些来源于其他人因可靠性分析方法的数据,以及国内外一些核电厂的人因失误数据和人因失误事件数据,系统提供了各种检索和数据管理功能,同时借助于系统的外推模块可以实现人因可靠性定性和定量分析。该系统的实现也为今后的研究和应用提供一些基础性的支持。
Human reliability analysis (HRA) is an essential part of probabilistic safety assessment of nuclear power plant (NPP). Good human reliability analysis leads to more accurate assessment of system safety; it can also prevent and reduce human error in large, complex human-machine system. However, the lack of understanding of human cognitive mechanism and the lack of of human error data has been plagued HRA research and application for years. But human reliability analysis has very urgent needs. Therefore, in this context, the study appears to be more open and more exploratory. We have long been engaged in human reliability research and application, including HRA in several domestic nuclear power plants. We encountered many problems in our work; we also formed some new ideas and found some new solutions in handling and solving these problems. The major research work done as follows.
Firstly, we analyzed the behavior of operators in NPP systematically, introduced the Unified Modeling Language for building workflow model of the behavior of operators. Workflow model of operators can accurately reflect the property of behavior and internal constraints. Moreover, we studied simulation method of human reliability, including some key problems in human reliability simulation, such as simulation modeling, simulation algorithm design, random numbers generation, error analysis, and simulation size decision. An example of human reliability simulation is presented referring to a widely used HRA method. Simulation results show that simulation is more effective in dealing with time interface, error propagation and sensitivity analysis. Simulation makes it possible to study human performance from different angles.
Secondly, we proposed a hierarchical causal model of human reliability. Bayesian network is used to build the causal link between different factors. Hierarchical structure is used in the causal model, which leads to a clear and simplified relationship between performance influence factors. Computational cost is also reduced largely. In the quantification of the model, we designed a simplified method to calculating the impact of factors; weights of factors were converted to conditional probability. We also proposed a novel variable elimination algorithm to improve reasoning performance of Bayesian network. In the novel algorithm, the added edges and the removed edges during elimination are considered; some methods of reducing graph complexity and controlling elimination cost are used to improve performance. Simulation results show that the new algorithm is effective.
Thirdly, in order to solve the problem of lack of data in human reliability analysis, a human reliability database system was developed. A hierarchic architecture of human reliability data was built, which included two parts, one was human error mode classification architecture, and the other was performance shape factor classification architecture. Based on theoretical analysis, a human reliability database system was implemented; useful data was stored in this database. The extrapolation module of this system can support qualitative and quantitative analysis of human reliability. This system is useful for data collecting and data analysis of human reliability, which can provide basic support for future research.
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