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

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.