A Prototype System for Intelligent Accelerator Operation Monitoring at CSNS Based on Machine Learning

[Background]: In accelerator operations, ensuring stable performance is critical for supporting scientific research, particularly for complex systems such as the China Spallation Neutron Source (CSNS). Traditional threshold-based alarm mechanisms often struggle to detect certain intricate anomalies, especially those with complex or transient patterns, leading to gaps in monitoring and increased challenges for operators during fault diagnosis. These undetected anomalies can significantly lower operational efficiency and delay fault resolution.[Purpose]: This study aims to develop an intelligent monitoring system for CSNS accelerators to detect complex anomalies and enhance fault detection reliability.[Methods]: A machine learning-based framework was proposed to improve anomaly detection in accelerator operations. The method employed unsupervised algorithms to analyze operational data, with a focus on jitter-type anomalies that are challenging for traditional alarms to capture. Cooling water temperature variables were selected as the research objects. The workflow involved data preprocessing, feature extraction, and the application of unsupervised learning models to detect deviations from normal operational patterns. To validate the method, a prototype system for intelligent accelerator monitoring was developed, incorporating real-time data analysis and anomaly detection capabilities.[Results]: The proposed method successfully detected jitter-type anomalies in various operational datasets, such as cooling water temperatures and power supply parameters, demonstrating its generalizability across different subsystems. Additionally, the prototype system was deployed and validated in the CSNS operational environment, where it effectively identified anomalies.[Conclusions]: This machine learning-based anomaly detection approach improves the accuracy and reliability of monitoring in accelerator operations. By addressing the limitations of traditional methods, it provides a more effective and scalable solution for real-time anomaly detection. The prototype system demonstrates the feasibility of implementing intelligent monitoring for complex accelerator systems, contributing to the stability and efficiency of their operation.

[背景]：在加速器运行场景中，保障系统稳定运行对支撑科研工作至关重要，对于中国散裂中子源（China Spallation Neutron Source, CSNS）这类复杂大科学装置而言尤为如此。传统基于阈值的告警机制往往难以识别部分复杂异常，尤其是模式复杂或瞬时性的异常，这会导致监测存在盲区，并在故障诊断阶段为运维人员带来更大挑战。这类未被及时发现的异常会显著降低运行效率，拖延故障排查与解决进度。
[目的]：本研究旨在为CSNS加速器开发一套智能监测系统，以实现复杂异常的精准识别，并提升故障检测的可靠性。
[方法]：本研究提出了一种基于机器学习的框架，以优化加速器运行中的异常检测任务。该方法采用无监督算法对运行数据进行分析，重点针对传统告警难以捕捉的抖动类异常展开研究。研究选取冷却水温度变量作为分析对象，整体流程涵盖数据预处理、特征提取，以及通过无监督学习模型识别偏离正常运行模式的异常信号。为验证所提方法的有效性，本研究开发了一套加速器智能监测原型系统，集成了实时数据分析与异常检测功能。
[结果]：所提方法成功在多类运行数据集（如冷却水温度、供电参数）中识别出抖动类异常，证明了其在不同子系统间的泛化能力。此外，该原型系统已在CSNS实际运行环境中部署并验证，可有效识别各类异常。
[结论]：该基于机器学习的异常检测方法提升了加速器运行监测的准确性与可靠性。通过弥补传统方法的局限性，其为实时异常检测提供了更高效、可扩展的解决方案。本原型系统验证了为复杂加速器系统部署智能监测的可行性，有助于提升装置运行的稳定性与效率。