分类： 核科学技术 >> 粒子加速器 提交时间： 2023-06-18 合作期刊: 《Nuclear Science and Techniques》
WANG Qi-Sheng
YU Feng
HUANG Sheng
SUN Bo
ZHANG Kun-Hao
LIU Ke
WANG Zhi-Jun
XU Chun-yan
WANG Si-Sheng
YANG Li-Feng
PAN Qiang-Yan
LI Liang
ZHOU Huan
CUI Yin
XU Qin
Earnest Thomas
HE Jian-Hua
摘要： The macromolecular crystallography beamline BL17U1 at the Shanghai Synchrotron Radiation Facility (SSRF) is the first dedicated macromolecular crystallography (MX) beamline at a third-generation synchrotron in China. It utilizes an in-vacuum undulator as a source and is energy-tunable from 5 to 18 keV. The beamline was commissioned and opened for users in April 2009. The experimental station was upgraded in 2011 with an advanced detector, a high precision goniometer and an automatic sample exchanger for high efficient and high-throughput data collection of protein crystals. The current set-up allows for remote operation of sample mounting, centering and data collection of pre-frozen crystals. In recent two years, the number of PDB depositions from this beamline exceeds 330 each year. In this paper, we describe the complete BL17U1 beamline with upgraded end station and how it is managed for user community.
分类： 核科学技术 >> 粒子加速器 提交时间： 2025-02-03
Yang, Li-juan
Peng, Jia-Yi
Qiu, Feng
He, Yuan
Ma, Jin-Ying
Xue, Zong-Heng
Jiang, Tian-Cai
Zhu, Zheng-Long
Chen, Qi
Xu, Cheng-Ye
Yu, Jing-Wei
Ma, Zhen
Luo, Di-Di
Yang, Zi-Qin
Gao, Zheng
Sun, Lie-Peng
Zhang, Zhou-Li
Huang, Gui-Rong
Wang, Zhi-Jun
摘要： Superconducting radio-frequency (SRF) cavities are the core components of SRF linear accelerators, making their stable operation considerably important. However, the operational experience from different accelerator laboratories has revealed that SRF faults are the leading cause of short machine downtime trips. When a cavity fault occurs, system experts analyze the time-series data recorded by low-level RF systems and identify the fault type. However, this requires expertise and intuition, posing a major challenge for control-room operators. Here, we propose an expert feature--based machine learning model for automating SRF cavity fault recognition. The main challenge in converting the “expert reasoning” process for SRF faults into a “model inference” process lies in feature extraction, which is attributed to the associated multidimensional and complex time-series waveforms. Existing autoregression-based feature-extraction methods require the signal to be stable and autocorrelated, resulting in difficulty in capturing the abrupt features that exist in several SRF failure patterns. To address these issues, we introduce expertise into the classification model through reasonable feature engineering. We demonstrate the feasibility of this method using the SRF cavity of the China Accelerator Facility for superheavy Elements (CAFE2). Although specific faults in SRF cavities may vary across different accelerators, similarities exist in the RF signals. Therefore, this study provides valuable guidance for fault analysis of the entire SRF community.
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