17–22 May 2026
C.I.D
Europe/Zurich timezone

Physics-Informed Autonomous Fault Diagnosis Agent for SSRF Using Large Language Models

MOP6606
18 May 2026, 16:00
2h
C.I.D

C.I.D

Deauville, France
Poster Presentation MC6.D13: Instrumentation: Artificial Intelligence Poster session

Speaker

Yihao Gong (Shanghai Synchrotron Radiation Facility)

Description

To advance autonomous operation at the Shanghai Synchrotron Radiation Facility (SSRF), we developed a diagnostic agent using a locally deployed Qwen3-30B model. Unlike black-box predictors, this agent employs physics-informed context engineering by langgraph to emulate expert reasoning: it iteratively excludes healthy subsystems, identifies parameter drifts, and validates hypotheses by referencing historical fault records. The system is designed to handle general accelerator anomalies, ranging from transient trips to long-term drifts, providing a secure, intelligent, and modular framework that facilitates continuous upgrades. Currently, a lightweight supervisory layer continuously monitors beam status and triggers the agent only upon anomaly detection, ensuring efficient, on-demand diagnosis.

In which format do you inted to submit your paper? LaTeX

Author

Yihao Gong (Shanghai Synchrotron Radiation Facility)

Co-authors

Liyuan Tan (Shanghai Institute of Applied Physics) Shouzhi Xuan (Shanghai Advanced Research Institute, Chinese Academy of Sciences) Shunqiang Tian (Shanghai Advanced Research Institute, Chinese Academy of Sciences) Xinzhong Liu (Shanghai Advanced Research Institute)

Presentation materials

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