基于深度学习的CSNS加速器预警系统样机

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。

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

To send out early warnings before some failures of the China Spallation Neutron Source (CSNS) accelerator, the feature models of the CSNS accelerator vacuums and drift tube linac (DTL) temperatures have been established based on deep learning, and a prototype of an early warning system has been developed. This prototype of an early warning system was built based on the experimental physics and industrial control system (EPICS) architecture, and it is mainly composed of three parts: training, recognition and information release. Python was adopted for program design and development, and functions such as training samples acquisition, deep learning networks design and training, online recognition and information release have been realized. The test results show that the accuracy of this prototype can reach 98.4% for the test set generated based on the historical data of the CSNS accelerator vacuums and DTL temperatures, and the anomalies of the CSNS accelerator vacuums and DTL temperatures can be recognized based on the real-time data, and the early warnings can be sent out, which proves its feasibility and effectiveness.