分类： 核科学技术 >> 粒子加速器 提交时间： 2021-12-31
Ma, Jin-Ying
Qiu, Feng
Shi, Long-Bo
Zhu, Zheng-Long
Jiang, Tian-Cai
Xue, Zong-Heng
Jin, Ke-An
Chen, Qi
Xu, Cheng-Ye
Ding, Xing-Hao
Gao, Zheng
Sun, Lie-Peng
Huang, Gui-Rong
He, Yuan
摘要： Precise measurements of the cavity forward (Vf) and reflected signals (Vr) are essential for characterizing other key parameters such as the cavity detuning and forward power. In practice, it is challenging to measure Vf and Vr precisely because of crosstalk between the forward and reflected channels (e.g., coupling between the cavity reflected and forward signals in a directional coupler with limited directivity). For DESY, a method based on the cavity differential equation was proposed to precisely calibrate the actual Vf and Vr. In this study, we verified the validity and practicability of this approach for the Chinese ADS front-end demo superconducting linac (CAFe) facility at the Institute of Modern Physics and a compact energy recovery linac (cERL) test ma#2;chine at KEK. At the CAFe facility, we successfully calibrated the actual Vf signal using this method. The result demonstrated that the directivity of directional couplers might seriously affect the accuracy of Vf measurement. At the cERL facility, we calibrated the Lorentz force detuning (LFD) using the actual Vf. Our study confirmed that the precise calibration of Vf significantly improves the accuracy of the cavity LFD measurement.
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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