留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于深度学习的CSNS加速器预警系统样机

何泳成 张玉亮 王林 金大鹏 吴煊 康明涛 郭凤琴 朱鹏

何泳成, 张玉亮, 王林, 等. 基于深度学习的CSNS加速器预警系统样机[J]. 强激光与粒子束. doi: 10.11884/HPLPB202133.200340
引用本文: 何泳成, 张玉亮, 王林, 等. 基于深度学习的CSNS加速器预警系统样机[J]. 强激光与粒子束. doi: 10.11884/HPLPB202133.200340
He Yongcheng, Zhang Yuliang, Wang Lin, et al. Prototype of an early warning system based on deep learning for the CSNS accelerator[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202133.200340
Citation: He Yongcheng, Zhang Yuliang, Wang Lin, et al. Prototype of an early warning system based on deep learning for the CSNS accelerator[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202133.200340

基于深度学习的CSNS加速器预警系统样机

doi: 10.11884/HPLPB202133.200340
基金项目: 中国科学院高能物理研究所谢家麟基金项目(Y9546150U2)
详细信息
    作者简介:

    何泳成(1986—),男,硕士,高级工程师,从事加速器控制研究;heyongcheng@ihep.ac.cn

  • 中图分类号: TL507

Prototype of an early warning system based on deep learning for the CSNS accelerator

  • 摘要: 为了能在中国散裂中子源(CSNS)加速器的部分故障发生前发出预警信息,利用深度学习建立了基于CSNS加速器真空度和漂移管直线加速器(DTL)温度的特征模型,开发了一套CSNS加速器预警系统样机。该样机基于实验物理及工业控制系统(EPICS)架构搭建,主要由训练、识别和信息发布3部分组成,采用Python进行程序设计开发,实现了训练样本获取、深度学习网络设计和训练、在线识别和信息发布等功能。测试结果表明,该样机对基于CSNS加速器真空度和DTL温度历史数据生成的测试集的准确率达98.4%,且能根据实时数据识别出CSNS加速器真空度和DTL温度的异常,并能发出预警信息,证明了其可行性和有效性。
  • 图  1  CSNS加速器的部分故障发生前相应真空度和温度的变化

    Figure  1.  Changes of the corresponding vacuums and temperature before some failures of the CSNS accelerator

    图  2  基于深度学习的CSNS加速器预警系统样机工作原理

    Figure  2.  The principles of the prototype of an early warning system for the CSNS accelerator

    图  3  基于深度学习的CSNS加速器预警系统样机结构图

    Figure  3.  Structural diagram of the prototype of an early warning system for the CSNS accelerator

    图  4  其中的6个训练样本的X矩阵的三维图

    Figure  4.  The 3-dimensional plots of the X matrix of 6 training samples

    图  5  设计建立的深度学习网络结构

    Figure  5.  The designed architecture of the deep neural network

    图  6  基于深度学习的CSNS加速器预警系统样机界面

    Figure  6.  The operator interface of the prototype of an early warning system for the CSNS accelerator

    图  7  在发生LEBTGV01真空阀门联锁前预警级别逐渐升高

    Figure  7.  The warning level increased gradually before the LEBTGV01 vacuum valve interlock

    图  8  在发生QM234流量开关联锁前超过8 d预警级别开始升高

    Figure  8.  The warning level had begun to rise more than 8 days before the QM234 flow switch interlock

    表  1  图形工作站的主要信息

    Table  1.   The main information of the graphics workstation

    CPUMemoryGPUOperating systemPythonTensorFlow
    Intel Xeon E5-2678v3×2128 GBRTX 2080Ti×2Ubuntu 18.04.1Anaconda 2019.071.14.0
    下载: 导出CSV
  • [1] Wei Jie, Chen Hesheng, Chen Yanwei, et al. China spallation neutron source: design, R& D, and outlook[J]. Nuclear Instruments & Methods In Physics Research Section A-Accelerators Spectrometers Detectors And Associated Equipment, 2009, 600(1): 10-13.
    [2] Wei Jie, Fu Shinian, Tang Jingyu, et al. China spallation neutron source - an overview of application prospects[J]. Chinese Physics C, 2009, 33(11): 1033-1042. doi: 10.1088/1674-1137/33/11/021
    [3] Wang Sheng, Fang Shouxian, Fu Shinian, et al. Introduction to the overall physics design of CSNS accelerators[J]. Chinese Physics C, 2009, 33(S2): 1-3. doi: 10.1088/1674-1137/33/S2/001
    [4] Fu S N, Chen H S, Chen Y W, et al. Status of CSNS project[C]//Proceedings of IPAC2013.2013: 3995–3999.
    [5] Chen H, Wang X L. China's first pulsed neutron source[J]. Nature Materials, 2016, 15(7): 689-691. doi: 10.1038/nmat4655
    [6] Liu Huachang, Peng Jun, Gong Keyun, et al. The design and construction of CSNS drift tube linac[J]. Nuclear Inst. and Methods in Physics Research, A, 2018, 911: 131-137. doi: 10.1016/j.nima.2018.10.034
    [7] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nautre, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
    [8] Lecun Y, Kavukcuoglu K, Farabet C. Convolutional Networks and Applications in Vision[C]//International Symposium on Circuits and Systems Nano-Bio Circuit Fabrics and Systems (ISCAS 2010). 2010.
    [9] Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[J]. Communications of the ACM, 2017, 60(6): 84-90. doi: 10.1145/3065386
    [10] Lecun Y, Bottou L, Bengio Y. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791
    [11] Zhang Yuliang, Jin Dapeng, Zhu Peng, et al. The accelerator control system of CSNS[J]. Radiation Detection Technology and Methods, 2020, 4: 1-14. doi: 10.1007/s41605-019-0147-6
    [12] Zhang Yuliang, Kang Mingtao, Jin Dapeng, et al. The run management system for CSNS[J]. Radiation Detection Technology and Methods, 2019, 3(3): 35-37. doi: 10.1007/s41605-019-0116-0
    [13] 康明涛, 黄涛, 张玉亮, 等. CSNS加速器真空控制系统的设计与实现[J]. 强激光与粒子束, 2020, 32:084001. (Kang Mingtao, Huang Tao, Zhang Yuliang, et al. Design and implementation of vacuum control system of China Spallation Neutron Source[J]. High Power Laser and Particle Beams, 2020, 32: 084001
    [14] 何泳成, 李刚, 金大鹏, 等. CSNS漂移管直线加速器水冷联锁系统设计[J]. 核电子学与探测技术, 2017, 37(6):585-589. (He Yongcheng, Li Gang, Jin Dapeng, et al. The Design of CSNS Drift Tube Linac Water Cooling Interlock System[J]. Nuclear Electronics & Detection Technology, 2017, 37(6): 585-589 doi: 10.3969/j.issn.0258-0934.2017.06.006
    [15] 赵籍九, 尹兆升. 粒子加速器技术[M]. 北京: 高等教育出版社, 2006.

    Zhao Jijiu, Yin Zhaosheng. Particle accelerator technology[M]. Beijing: Higher Education Press, 2006.
    [16] Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015: 1-9.
    [17] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016: 770-778.
    [18] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//28th Conference on Neural Information Processing Systems (NIPS). 2014.
    [19] Sainath T N, Kingsbury B, Saon G, et al. Deep convolutional neural networks for large-scale speech tasks[J]. Neural Networks, 2015, SI(64): 39-48.
    [20] Sanner M F. Python: A programming language for software integration and development[J]. Journal of Molecular Graphics & Modelling, 1999, 17(1): 57-61.
  • 加载中
计量
  • 文章访问数:  37
  • HTML全文浏览量:  22
  • PDF下载量:  2
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-12-16
  • 修回日期:  2021-01-25
  • 网络出版日期:  2021-03-24

目录

    /

    返回文章
    返回