【作者】 李鹏程；

【导师】 陈国华；

【作者基本信息】 华南理工大学 ， 化工过程机械， 2011， 博士

【摘要】 核电厂的仪控(Instrumentation and control, I&C)系统由传统的模拟控制发展到数字化控制,使技术系统、人-机界面、规程等影响人因可靠性的情境环境发生变化,改变了操纵员的认知行为模式,出现新的失误机理。传统的人因可靠性分析(Human reliability analysis,HRA)模型和方法难以满足数字化控制系统中操纵员的认知行为失误和可靠性分析的要求。开展数字化控制系统中的HRA技术研究是当前国际上研究的前沿和热点问题。基于当前HRA方法的局限性,本文以数字化控制系统中HRA为研究对象,重点开展以下研究工作:(1)核电厂数字化控制系统中的人因失误因果模型。基于Rasmussen的技能、规则、知识(Skill-based、rule-based、knowdge-based, SRK)三级行为模型对操纵员认知行为模型进行扩展,分析核电厂数字化控制系统中操纵员的认知行为和人因失误。通过对数字化控制系统中的情境环境特征分析,识别核电厂数字化控制系统中典型的行为形成因子(Performance shaping factor, PSF)。基于人因事件的分析,进一步识别核电厂普遍存在的主要组织缺陷以及PSF之间的相关关系。通过上述研究,基于系统理论提出从组织因素、情境状态因素、个体因素及人因失误建立人因失误因果概念模型,并分析情境环境因素与个体因素、个体因素与人因失误的影响关系,为人因失误和可靠性分析提供理论基础和指导性框架。(2)考虑情境环境因果关系的HRA方法。基于人因失误因果概念模型,建立考虑情境环境因果关系的HRA方法,模拟PSF之间的因果影响关系,克服传统HRA方法中由于PSF分类的非完全独立和非正交性带来重复计算的局限性。采用模糊方法处理HRA模型中节点变量的先验和条件概率,减少专家主观判断带来的不确定性,使人因失误概率(Human error probability, HEP)的量化更符合实际。并且考虑组织管理因素对人因可靠性的影响,构建整合组织管理因素的HRA的模糊贝叶斯网络分析方法,提高人因可靠性分析的质量。并通过贝叶斯网络的诊断推理,识别影响人因失误的主要贡献因子,为人因失误的预防提供决策支持。(3)顾及失误恢复的操纵员行为相关性分析方法。相比传统的模拟控制系统,数字化控制系统中操纵员行为之间的相关性更为普遍。识别核电厂数字化控制系统中影响操纵员行为相关性的因素,建立操纵员行为相关性分析的工作模型。提出一种基于模糊逻辑理论识别数字化控制系统中操纵员行为相关性水平的方法,从而考虑失误恢复因子对人因可靠性的影响,进一步完善HRA模型,对HEP进行修正,使结果更符合实际,提高人因可靠性分析的质量。(4)整合失误影响的人因失误风险评价/预测方法。对传统只考虑HEP来量化人因失误风险的方法进行改进,综合考虑人因失误概率(HEP)、失误影响概率(Error-effect probability, EEP)、失误后果严重度(Error cosequence severity, ECS),建立一种基于自适应神经-模糊推理系统(ANFIS)的核电厂人因失误风险评价方法,并且在数据获取时考虑因子的相对权重,使评价更为合理,是一种简单、实用、可靠的人因失误风险预测工具。更多还原

【Abstract】 The instrumentation and control (I&C) system of nuclear power plants is transformed from traditional analog control to digital control, which makes the contextural factors influencing human reliability changed, such as technology systems, human-machine interface, procedure et al, operators’cognitive and action modes also changed, then new human error mechanism emerges. The traditional human reliability analysis models pose problem for digital control systems. Technology of HRA in digital control system turns to be hotspot at present. Aiming at the limitations mentioned above, the paper focus on the HRA in digital control system and the main research work can be conducted as follows:(1) The causal model of human error of digital control system for nuclear power plants.The operators’cognitive model is expanded on the basis of Rasmussen’s SRK cognitive framework, the cognitive activities and human errors are analyzed in digital control system for NPPs. The typical performance shaping factors (PSFs) of digital control system for NPPs are identified by the analysis of contextual features of digital control system. Based on the analytical results of human factor events, the prevalence of organizational defects and correlation relationships of PSFs are futher identified. Through the studies related above, the causal conceptual model of human error is built from organizational factors, situational factors, individual factors and human errors based on system theory, and the influencing relationships and degree (types) between human errors and individual factors, situational factors and individual factors are analyzed, which provides a theoretical model and guiding framework for human error analysis and HRA.(2) The HRA method considering the contextual causal relationships.Based on the established human error causal conceptual model, the HRA method considering the contextual causal relationships is built to simulate causal relationships between PSFs. It overcomes the shortcomings that double counting of the effects of PSFs on human reliability because of the dependent and nonorthogonal of PSFs in the traditional HRA methods. The prior probability and conditional probability of node variables in HRA model are obtained by fuzzy method in order to reduce uncertainties due to the expert’s subjective judgments, which makes the quantification of human error probability (HEP) more realistic. Furthermore considering the effects of organizational and management factors on human reliability, the fuzzy Bayesian network method of HRA integrating organizational and management factors is established, the quality of HRA is improved. By the diagnostic reasoning of Bayesian network, the main contributing factors causing human error are identified, which provides decision-making support for the prevention of human errors.(3) The method of dependency of operators’activities considering error recovery.Compared with the traditional analog control system, the dependency of operators’activities is more prevalent in digital control system. Based on the analysis of the characteristics of human factors in digital control system, the influencing factors on the dependency of operators’activities are identified, and the work model for analysing the dependency of operators’activities is established. A fuzzy logic-based approach for analysing the dependency of operators’activities is proposed to assess the dependency level of operators’activities. Namely, it considers the effects of error recovery factors on human reliability to futher improve HRA model and revise the HEP, which makes the results more practical and improves the quality of HRA.(4) The technique of human error risk assessment integrating error effects.The traditional technique of human error risk which only considering HEP to quantify human error risk is modified. Human Error Probability (HEP), Error-Effect Probability (EEP) and Error Consequence Severity (ECS) are integrated to develop an adaptive network-fuzzy inference system (ANFIS)-based approach for assessing human error risk. Furthermore considering the relative weight to obtain data used to model, which makes the evaluation more reasonable. It is a simple, practical and reliable risk prediction tool of human errors in short.更多还原