A Prototype System for Intelligent Accelerator Operation Monitoring at CSNS Based on Machine Learning 候选 · 未审核

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[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 m

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落地页

https://www.scidb.cn/detail?dataSetId=160c0113184549a49c568f9616793b0c

发布年份

2025

发布方

Science Data Bank

设备类型

power_electronics_device

PHM 任务

fault_diagnosis fault_detection anomaly_detection

分发点