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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, 2021, 33: 044008. 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, 2021, 33: 044008. 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, 2021, 33: 044008. 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, 2021, 33: 044008. 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 began 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
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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
  • 刊出日期:  2021-05-02

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