DOI: 10.57760/sciencedb.hjs.00287
title: A Prototype System for Intelligent Accelerator Operation Monitoring at CSNS Based on Machine Learning
title: 基于机器学习方法的CSNS加速器智能值班员样机系统

description: [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.
description: [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.