作者：
Pengcheng Li;Xiao Jin;Yanxing Wang;Jianhua Chen;Licao Dai
期刊：
Advances in Intelligent Systems and Computing,2020年 956: 214-224 ISSN：2194-5357
通讯作者：
Dai, L.
作者机构：
Human Factor InstituteUniversity of South ChinaHengyangPeople’s Republic of China;School of Nuclear Science and TechnologyUniversity of South ChinaHengyangPeople’s Republic of China;[Dai L.; Wang Y.; Jin X.; Li P.; Chen J.] Human Factor Institute, University of South China, Hengyang, Hunan 421001, China, School of Nuclear Science and Technology, University of South China, Hengyang, Hunan 421001, China
通讯机构：
[Dai, L.] H;Human Factor Institute, China
会议名称：
AHFE International Conference on Human Error, Reliability, Resilience, and Performance, 2019
会议时间：
24 July 2019 through 28 July 2019
会议论文集名称：
Advances in Human Error, Reliability, Resilience, and Performance
关键词：
Nuclear power plant;Shared situation awareness;Simulator experiment;Situation awareness
期刊：
Advances in Intelligent Systems and Computing,2020年956:199-213 ISSN：2194-5357
通讯作者：
Dai, L.
作者机构：
[Zuo G.; Chen J.; Dai L.] Human Factor Institute, University of South China, Hengyang, Hunan, China
通讯机构：
[Dai, L.] H;Human Factor Institute, China
会议论文集名称：
Advances in Human Error, Reliability, Resilience, and Performance
关键词：
Nuclear Power Plant (NPP);Situation Awareness (SA);Team
摘要：
Endsley (1995) systematically put forward the theory of Situation Awareness (SA). The theory emphasizes people’s perception and understanding of current operating environment information, and prediction of future state under certain time and space conditions. Endsley and Jones (2012) further proposed a SA-Oriented Design system. With the deepening of SA research, it has become the frontier of engineering psychology research. At present, Nuclear Power Plants (NPP) are highly computerized, the complexity of its main control room has increased, man-machine interaction is frequent, tasks are complex and changeable, time pressure is high, and cognitive load is heavy, so the workload of operators is greatly increased. In this complex technical system, the accuracy of Team Situation Awareness (TSA) of NPP is an important factor for high quality decision-making and efficient operation. If the problem of real-time and accurate SA measurement can be solved, it will be helpful to the design of Real-Time Adaptive man-machine interaction. Firstly, this paper analyses the elements and types of Team Situation Awareness of nuclear power plant, and develops a team SA measurement scale. Then, in different accident scenarios, the level of team SA of different types was measured by the simulation experiment, and the influencing factors were analyzed. These provide theoretical and experimental support for improving TSA of NPP.
摘要：
Is EOM behavior a stochastic process? If it is, does this randomness come with certain conditions? Are there some environmental factors playing a role? Does the failure of EOMs can be expressed by a probability number? How should HRA consider EOM in a framework of PSA? Through the experiments on a DCS simulator of a nuclear power plant, the researchers tried to come up answers to the above issues. The research results show that the EOM behavior of nuclear power plant is random no considering the influence factors, while in the case of data groupings, EOM behavior is not completely random. Some factors, such as age, training time and working hours, have a certain influence on the randomness of EOM behavior, which also provides direction for reducing human error. Of course, due to the limitations of the experimental conditions, the correctness of the conclusion remains to be further verified.
摘要：
Shearing machines are the key pieces of equipment for spent–fuel reprocessing in commercial reactors. Once a failure happens and is not detected in time, serious consequences will arise. It is very important to monitor the shearing machine and to diagnose the faults immediately for spent–fuel reprocessing. In this study, an intelligent condition monitoring approach for spent nuclear fuel shearing machines based on noise signals was proposed. The approach consists of a feature extraction based on wavelet packet transform (WPT) and a hybrid fault diagnosis model, the latter combines the advantage on dynamic–modeling of hidden Markov model (HMM) and pattern recognition of artificial neural network (ANN). The verification results showed that the approach is more effective and accurate than that of the isolated HMM or ANN.
摘要：
为实现电力系统工作流程的智能性，结合本体、语义推理、数据挖掘等技术，设计面向电力系统应用的语义工作流推理框架；建立该框架下工作流及其涉及的电力系统资源的本体概念和关系模型，描述电力生产需求信息、工作流和系统资源本体之间的联系；从最基本的推理元素出发，基于语义网规则语言构造工作流生成的触发推理流程和规则；运用Prot g 和Jess推理引擎实现工作流触发推理的应用模型，通过推理实验验证本方法的有效性。
