机器学习在BEPCII超导腔故障分析中的应用

曾童科; 戴建枰

doi:10.11884/HPLPB202537.240270

机器学习在BEPCII超导腔故障分析中的应用

doi: 10.11884/HPLPB202537.240270

曾童科^{1, 2,},
戴建枰^1, ,

1.
中国科学院高能物理研究所，北京 100049
2.
中国科学院大学，北京 100049

基金项目: 国家自然科学基金面上项目(12275286)

详细信息

作者简介:
曾童科，zengtk@ihep.ac.cn

通讯作者:
戴建枰，jpdai@ihep.ac.cn。

中图分类号: TL507
计量
- 文章访问数: 44
- HTML全文浏览量: 17
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2024-08-20
- 修回日期: 2025-05-13
- 录用日期: 2025-05-14
- 网络出版日期: 2025-05-24

Application of machine learning in BEPCII superconduction radio-frequency cavity fault analysis

Zeng Tongke^{1, 2
,},
Dai Jianping^{1
, ,}

1.
Institute of High Energy Physics, Chinese Academy of sciences, Beijing 100049, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

摘要

摘要: 超导腔故障的传统分析方法依赖先验知识，人工和时间成本较高，准确性和一致性较差，并且在处理复杂设备和大量数据时效率较低。机器学习技术能够提高故障分类的准确性和效率，减少主观判断造成的人为误差。研究了基于机器学习算法的超导腔故障分类方法，即，基于BEPCII运行过程中产生的超导腔故障图片数据，通过图片信息提取、特征选择与优化、机器学习算法训练、利用K折交叉验证分析模型准确率与一致性等，实现了对超导腔故障的分类。研究结果表明，随机森林算法、支持向量机与Bagging分类算法在处理故障图片时有更好的分类效果，有监督学习方法的准确性和一致性明显高于无监督学习。研究中实现的故障分类达到了较高的准确率和一致性，有助于快速高效地区分BEPCII超导腔上发生的故障，同时为其他加速器中超导腔故障的诊断提供参考。
- 超导腔故障 /
- 诊断方法 /
- 图片识别 /
- 机器学习 /
- 故障分类
Abstract: Traditional methods for analyzing superconduction radio-frequency (SRF) cavity faults rely on a priori knowledge, featuring high labor and time costs, poor accuracy and low consistency. They are less efficient when dealing with complex devices and large amounts of data. In this paper, a method for classifying SRF cavity faults based on machine learning algorithms is investigated. Using the SRF cavity fault data generated during the operation of BEPCII, the classification of SRF cavity faults is achieved through image information extraction, feature selection and optimization, machine learning algorithm training, and analyzing the accuracy and consistency of the model via K-fold cross validation. The results of the study show that Random Forest, Support Vector Machine and Bagging algorithms have better classification results when dealing with faulty pictures. The accuracy and consistency of supervised learning methods are significantly higher than those of unsupervised learning. The fault classification realized in this research achieves high accuracy and consistency. It enables to quickly and efficiently distinguish the faults occurring on the SRF cavity of BEPCII. It also provides a reference for the diagnosis of SRF cavity faults in other particle accelerators.
- superconduction radio-frequency cavity faults /
- diagnostic methods /
- image recognition /
- machine learning /
- faults classification

HTML全文

图 1 BEPCII高频系统的典型故障

Figure 1. Typical faults of BEPCII RF system

下载: 全尺寸图片幻灯片

图 2 BEPCII高频系统外部原因引起的超导腔故障

Figure 2. Faults caused by external causes of the BEPCII RF system

下载: 全尺寸图片幻灯片

图 3 RGB模型处理后的复现图像

Figure 3. Reproduced image processed by RGB modeling

下载: 全尺寸图片幻灯片

图 4 利用梯度的边沿检测效果图

Figure 4. Schematic of edge detection using gradient

下载: 全尺寸图片幻灯片

图 5 BEPCII超导腔故障数据分布情况

Figure 5. Distribution of BEPCII SRF cavity fault data

下载: 全尺寸图片幻灯片

图 6 K折交叉验证后各分类算法的混淆矩阵

notes: ①—SRF cavity faults caused by failure of RF hardwares ; ②—SRF cavity faults caused by malfunction of the cavity tuner; ③—SRF cavity faults caused by LLRF loop oscillation due to excessive beam injection; ④—SRF cavity faults caused by incident power jitter; ⑤—SRF cavity faults caused by beam loss due to other systems

Figure 6. Confusion matrix for each classification algorithm after K-fold cross-validation

下载: 全尺寸图片幻灯片

表 1 机器学习算法分类验证精度

Table 1. Validation accuracy of machine learning algorithms

method	accuracy/%	Kappa coefficient
Support Vector Method	96.296	0.913
Random Forest	98.378	0.872
Decision Tree	94.444	0.916
Bagging Classifier	98.148	0.955
K-means	88.235	0.179
K-Nearest Neighbor	86.275	0.385

下载: 导出CSV

表 2 BEPCII超导腔故障分类情况

Table 2. Classification of BEPCII SRF cavity faults

method	accuracy/%	Kappa coefficient
Support Vector Method	96.638	0.886
Random Forest	96.722	0.926
Decision Tree	88.893	0.723
Bagging Classifier	95.702	0.933
K-means	77.796	～0
K-Nearest Neighbor	78.013	～0

下载: 导出CSV

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