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

High Power Laser and Particle Beams, Volume. 37, Issue 7, 074003(2025)

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

Tongke Zeng^1,2 and Jianping Dai^1、*

Author Affiliations

¹Institute of High Energy Physics, Chinese Academy of sciences, Beijing 100049, China

²University of Chinese Academy of Sciences, Beijing 100049, China

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References (16)

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Figures & Tables(8)

Fig. 1. Typical faults of BEPCII RF system

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

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Fig. 3. Reproduced image processed by RGB modeling

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Fig. 4. Schematic of edge detection using gradient

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Fig. 5. Distribution of BEPCII SRF cavity fault data

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

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Table 1. Validation accuracy of machine learning algorithms
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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

Table 2. Classification of BEPCII SRF cavity faults
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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

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Tongke Zeng, Jianping Dai. Application of machine learning in BEPCII superconduction radio-frequency cavity fault analysis[J]. High Power Laser and Particle Beams, 2025, 37(7): 074003

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Paper Information

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Received: Aug. 20, 2024

Accepted: May. 14, 2025

Published Online: Jul. 18, 2025

The Author Email: Jianping Dai (jpdai@ihep.ac.cn)

DOI:10.11884/HPLPB202537.240270

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology