关键词：
digital main control room;cognitive reliability;human factors issues;human errors;bayesian network
摘要：
Currently, there is a trend in nuclear power plants (NPPs) toward introducing digital and computer technologies into main control rooms (MCRs). Safe generation of electric power in NPPs requires reliable performance of cognitive tasks such as fault detection, diagnosis, and response planning. The digitalization of MCRs has dramatically changed the whole operating environment, and the ways operators interact with the plant systems. If the design and implementation of the digital technology is incompatible with operators' cognitive characteristics, it may have negative effects on operators' cognitive reliability. Firstly, on the basis of three essential prerequisites for successful cognitive tasks, a causal model is constructed to reveal the typical human performance issues arising from digitalization. The cognitive mechanisms which they impact cognitive reliability are analyzed in detail. Then, Bayesian inference is used to quantify and prioritize the influences of these factors. It suggests that interface management and unbalanced workload distribution have more significant impacts on operators' cognitive reliability.
作者机构：
[张力; 蒋建军; 皱萍萍; 李鹏程; 戴立操] Human Factors Institute, University of South China, Hengyang, Hunan, 421001, China;[张力] Hunan Technical College, Hengyang, Hunan, 421001, China;[王以群] Center for Research in Information Management, University of South China, Hengyang, Hunan, 421001, China;[彭玉元] Guangzhou Kangda Vocational Technical College, Guangzhou, 510663, China
通讯机构：
Human Factors Institute, University of South China, China
作者机构：
[Li Peng-cheng; Zhang Li; Dai Li-cao] Univ S China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.;[Li Peng-cheng; Chen Guo-hua] S China Univ Technol, Inst Safety Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China.
通讯机构：
[Li Peng-cheng] U;Univ S China, Human Factor Inst, Hengyang 421001, Hunan, Peoples R China.
关键词：
Human reliability analysis;Bayesian network;Human error;Organizational factors;Situational factors;Individual factors
摘要：
Organizational factors are the major root causes of human errors, while there have been no formal causal model of human behavior to model the effects of organizational factors on human reliability. The purpose of this paper is to develop a fuzzy Bayesian network (BN) approach to improve the quantification of organizational influences in HRA (human reliability analysis) frameworks. Firstly, a conceptual causal framework is built to analyze the causal relationships between organizational factors and human reliability or human error. Then, the probability inference model for HRA is built by combining the conceptual causal framework with BN to implement causal and diagnostic inference. Finally, a case example is presented to demonstrate the specific application of the proposed methodology. The results show that the proposed methodology of combining the conceptual causal model with BN approach can not only qualitatively model the causal relationships between organizational factors and human reliability but also can quantitatively measure human operational reliability, and identify the most likely root causes or the prioritization of root causes causing human error.
作者机构：
[周勇; 张力] Human Factor Institute, Nanhua University, Hengyang, Hunan, 421001, China;[周勇] Flight Technology College, Civil Aviation Flight University of China, Guanghan, Sichuan, 618307, China;[张力] Hunan Institute of Technology, Hengyang, Hunan, 421002, China
通讯机构：
Human Factor Institute, Nanhua University, China
关键词：
A weight association rules;Dynamic function;Human factor events;SGTR
摘要：
With human factor events rising in recent years, many researches begin to pay much attention to them. Especially, human factor events in nuclear power plant show more important than other human factor events. To effectively decrease human factor events, the authors propose the method of association rule analysis of human factor events in this paper. Association rule is one of the most popular data mining techniques applied to many scientific and industrial problems. Based on traditional methods, the authors propose a weight association rule based on statistics. Weight factors consist of inner and exterior human factors. In this paper, the authors propose a dynamic function and some methods with weight in order to assess support, confidence and correlation degree among human factor events. The proposed methods are tested by experiments. From results of experiments, we can easily find higher error rate events caused by human, higher confidence and correlation degree events among human factor events of steam generator tube rupture (SGTR) of nuclear power plant (NPP). (C) 2011 Elsevier Ltd. All rights reserved.
作者机构：
[Jiang, Jian-jun] Univ S China, Human Factors Inst, Econ Management Coll, Hengyang 421001, Hunan, Peoples R China.;[Zhang, Kun; Jiang, Jian-jun] Univ S China, Coll Nucl Sci & Technol, Hengyang 421001, Hunan, Peoples R China.;[Wang, Yi-qun] Univ S China, Ctr Res Informat Management, Hengyang 421001, Hunan, Peoples R China.;[Zhang, Li] HuNan Inst Technol, Hengyang 421001, Peoples R China.;[Jiang, Jian-jun; Peng, Yu-yuan] GuangZhou KangDa Vocat Tech Coll, Dept Comp Sci, Guangzhou 511363, Guangdong, Peoples R China.
通讯机构：
[Jiang, Jian-jun] U;Univ S China, Human Factors Inst, Econ Management Coll, Hengyang 421001, Hunan, Peoples R China.
关键词：
Digital main control room;Markov model;Probability distributed function;Probability of correlation degree
摘要：
Monitoring process is an important part in a high safety digital main control room of nuclear power plant (NPP), it is the source extracted information and found abnormal information in time. As the human factors events arisen from monitoring process recently take place more and more frequent, the authors propose a reliability Markov model to effectively decrease these abnormal events. The model mainly analyzes next monitoring object probability in terms of current information and plant state. The authors divide digital human-machine interface into two parts that are referred as logical homogeneous Markov and logical heterogeneous Markov. For the former, a series of methods of probability evaluation are proposed, such as, Markov transition probability with condition, probability distributed function with human factors, system state and alarm; for the latter, the authors propose the calculation of probability of correlation degree between last time and next time and probability calculation methods with multi-father nodes. The methods can effectively estimate the transition probability from a monitoring component to next monitoring component at time t, can effectively analyze which information is more important in next monitoring process and effectively find more useful information in time t + 1, so that the human factors events in monitoring process can greatly be decreased. (C) 2011 Elsevier Ltd. All rights reserved.
作者机构：
[张力; 赵明; 李鹏程; 戴立操] Human Factor Institute, University of South China, Hengyang, Hunan, 421001, China;[陈国华; 李鹏程] Institute of Safety Science and Engineering, South China University of Technology, Guangzhou, 510641, China;[戴立操] School of Info-physics and Geomatics Engineering, Central South University, Changsha, 410083, China;[张力] Hunan Institute of Technology, Hengyang, Hunan, 421001, China
通讯机构：
Human Factor Institute, University of South China, China
作者机构：
[张力; 戴立操; 李鹏程] Human Factor Institute, University of South China, Hengyang 421001, China;[张力] Hunan Institute of Technology, Hengyang 421001, China;[陈国华; 李鹏程] Institute of Safety Science and Engineering, South China University of Technology, Guangzhou 510641, China
通讯机构：
Human Factor Institute, University of South China, China
作者机构：
[Zhang, Li; Li, Peng-Cheng] Human Factor Institute, University of South China, Hengyang 421001, China;[Chen, Guo-Hua; Li, Peng-Cheng] Institute of Safety Science and Engineering, South China University of Technology, Guangzhou 510641, China;[Zhang, Li] Safety and Environmental Engineering Department, Hunan Institute of Technology, Hengyang 421001, China
通讯机构：
[Li, P.-C.] H;Human Factor Institute, University of South China, China
摘要：
Based on the MCM framework proposed by Hollnagel, an organization-oriented structured technique for analysis of human factor accident was developed. The technique was discussed from three points of model, classification scheme and method. Human error event model and human cognitive model are integrated into a reference model for cause analysis of human error, which indicates the developing process, causal mechanisms of human factor accident and the nature of human cognition. The action errors, cognitive errors, failures of error recovery and causal factors of human error are analyzed, resulting in a wider human error classification system used to analyze causes of human error. Finally, the operational steps are given, and the practical application of the proposed technique is explained through the actual example.
作者机构：
[张力; 戴立操; 李鹏程] Human Factor Institute, University of South China, Hengyang, Hunan, 421001, China;[李鹏程] Institute of Safety Science and Engineering, South China University of Technology, Guangzhou 510641, China;[张力] Hunan Institute of Technology, Hengyang, Hunan, 421001, China;[戴立操] School of Info-Physics and Geomatics Engineering Central South University, Changsha 410083, China;[黄卫刚] Daya Bay Nuclear Power Operations and Management Co., Ltd., Shenzhen, Guangdong, 518124, China
通讯机构：
Human Factor Institute, University of South China, China
作者机构：
[Zhang Li; Dai Licao; Li Pengcheng] Univ S China, Human Factor Inst, Hengyang 421001, Peoples R China.;[Dai Licao] Cent S Univ, Sch Infophys & Geomat Engn, Changsha 410083, Hunan, Peoples R China.;[Zhang Li] Hunan Inst Technol, Hengyang 421003, Peoples R China.
通讯机构：
[Dai Licao] U;Univ S China, Human Factor Inst, Hengyang 421001, Peoples R China.
关键词：
THERP plus HCR;HCR data modification;HRA event tree;Case study
摘要：
Human reliability analysis (HRA) is generally viewed as quite an important part in probabilistic safety assessment (PSA). In this paper, a THERP + HCR HRA model is presented to model the operators' post-accident behavior in Chinese nuclear power plants. The paper shows how the model is structured and how to consider and acquire the corresponding data, including HCR data modification and HRA event tree data. A case study is presented to make an illustration. (C) 2010 Elsevier Ltd. All rights reserved.
作者机构：
[张力; 肖东生; 李鹏程] Human Factor Institute, Nanhua University, Hunan Hengyang 421001, China;[陈国华; 李鹏程] Institute of Safety Science and Engineering, South China University of Technology, Guangzhou 510641, China;[张力] Hunan Institute of Technology, Hunan Hengyang 421001, China
通讯机构：
Human Factor Institute, Nanhua University, China
作者机构：
[张力; 赵明; 李鹏程; 戴立操] Human Factor Institute, Nanhua University, Hengyang 421001, China;[陈国华; 李鹏程] Institute of Safety Science and Engineering, South China University of Technology, Guangzhou 510641, China
通讯机构：
Human Factor Institute, Nanhua University, China
摘要：
In the system reliability and safety assessment, the focuses are not only the risks caused by hardware or software, but also the risks caused by "human error". There are uncertainties in the traditional human error risk assessment (e.g. HECA) due to the uncertainties and imprecisions in Human Error Probability (HEP), Error-Effect Probability (EEP) and Error Consequence Severity (ECS). While fuzzy logic can deal with uncertainty and imprecision. It is an efficient tool for solving problems where knowledge uncertainty may occur. The purpose of this paper is to develop a new Fuzzy Human Error Risk Assessment Methodology (FHERAM) for determining Human Error Risk Importance (HERI) as a function of HEP, EEP and ECS. The modeling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs (i.e. HEP, EEP, and ECS) and outputs (i.e. HERI) of a risk assessment system in the form of IF THEN rules. It is implemented on fuzzy logic toolbox of MATLAB using Mamdani techniques. A case example is presented to demonstrate the proposed approach. Results show that the method is more realistic than the traditional ones, and it is practicable and valuable. (C) 2010 Elsevier Ltd. All rights reserved.
