Application of Machine Vision Technology in Intelligent Testing of Nuclear Power DCS Human-Machine Interaction

被引:0
|
作者
Wu, Yao [1 ]
Li, Ming-gang [1 ]
Sun, Xiao-qi [1 ]
机构
[1] China Techenergy Co Ltd, Beijing, Peoples R China
来源
NEW ENERGY POWER GENERATION AUTOMATION AND INTELLIGENT TECHNOLOGY, VOL 2 | 2024年 / 1250卷
关键词
Nuclear Distributed Control System; Machine Vision; Intelligent Testing; Image Recognition;
D O I
10.1007/978-981-97-7055-7_16
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Visual human-machine interaction (HMI) devices are very important in the interaction process between the nuclear Distributed Control System (DCS) and the power plant operators because of their design correctness and integrity, which affect the operational safety of the nuclear power system. Therefore, visual human-machine interaction devices should be sufficiently tested before leaving the factory. Meanwhile, the current problems of manual inspection of these devices are low efficiency and high human error. Therefore, this paper introduces machine vision technology and proposes an intelligent testing method for human-machine interaction device that can replace human to achieve the following requirements, such as automatic observation, automatic information recognition, automatic operation, and automatic judgment. The detailed information about the specific identification research on DCS interactive images is also described in this paper. Moreover, the developed intelligent testing devices, after application in typical projects, which realized fully unmanned automatic testing of nuclear power DCS human-machine interaction equipment. In addition, the application practice shows that the solutions and devices proposed in this study can significantly avoid human errors, improve test quality and efficiency. They have good application value for improving the quality of DCS equipment and ensuring the safety of nuclear power operation.
引用
收藏
页码:182 / 194
页数:13
相关论文
共 50 条
  • [43] Human-machine Interaction Based on Voice
    Lu, Jia-ni
    Qian, Hua
    Xiao, Ai-ping
    Shi, Miao-wen
    CONFERENCE ON MODELING, IDENTIFICATION AND CONTROL, 2012, 3 : 583 - 588
  • [44] Automating design with intelligent human-machine integration
    Yin, Yue H.
    Nee, Andrew Y. C.
    Ong, S. K.
    Zhu, Jian Y.
    Gu, Pei H.
    Chen, Lien J.
    CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2015, 64 (02) : 655 - 677
  • [45] Human-Machine Intelligence: Frigates are Intelligent Organisms
    Hasbach, Jonas D.
    Witte, Thomas E. F.
    2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2021, : 1495 - 1500
  • [46] KNOWLEDGE REPRESENTATION FOR HUMAN-MACHINE INTERACTION
    Koit, Mare
    Roosmaa, Tiit
    Oim, Haldur
    KEOD 2009: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON KNOWLEDGE ENGINEERING AND ONTOLOGY DEVELOPMENT, 2009, : 396 - +
  • [47] GAMIFICATION AND HUMAN-MACHINE INTERACTION: A SYNTHESIS
    Marache-Francisco, Cathie
    Brangier, Eric
    TRAVAIL HUMAIN, 2015, 78 (02): : 165 - 189
  • [48] Motion Estimation for Human-Machine Interaction
    Phade, G. M.
    Uddharwar, Prerna D.
    Dhulekar, P. A.
    Gandhe, S. T.
    2014 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), 2014, : 149 - 154
  • [49] Cognitive load and human-machine interaction
    Canes, Jose
    Di Stasi, Leandro
    Antoli, Adoracion
    Alvarez, Vanessa
    Madrid, Rafael, I
    INTERNATIONAL JOURNAL OF PSYCHOLOGY, 2008, 43 (3-4) : 762 - 762
  • [50] Sensing good human-machine interaction
    不详
    IIE SOLUTIONS, 2000, 32 (12): : 10 - 10