Anomaly detection in KOMAC high-power systems using transformer-based conditional variational autoencoder

This study applies a transformer-based conditional variational autoencoder (T-CVAE) model for anomaly detection in pulse waveform signals from the High Voltage Converter Modulator (HVCM) and Klystron at the Korea Multipurpose Accelerator Complex (KOMAC). Building upon prior work using CVAE models fo...

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Bibliographic Details
Published in:Journal of the Korean Physical Society Vol. 87; no. 7; pp. 883 - 891
Main Authors: Kim, Gi-Hu, Jeong, Hae-Seong, Kim, Han-Sung, Kwon, Hyeok-Jung, Kim, Dong-Hwan
Format: Journal Article
Language:English
Published: Seoul The Korean Physical Society 01.10.2025
Springer Nature B.V
한국물리학회
Subjects:
Anomalies
Datasets
Mathematical and Computational Physics
Normal distribution
Particle and Nuclear Physics
Physics
Physics and Astronomy
Plasmas and Phenomenology
Research - Fluids
Spallation
Theoretical
Voltage converters
Waveforms
물리학
High-voltage convertor modulator
Anomaly detection
KOMAC
Transformer-based conditional variational autoencoder
ISSN:0374-4884, 1976-8524
Online Access:Get full text
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Summary:This study applies a transformer-based conditional variational autoencoder (T-CVAE) model for anomaly detection in pulse waveform signals from the High Voltage Converter Modulator (HVCM) and Klystron at the Korea Multipurpose Accelerator Complex (KOMAC). Building upon prior work using CVAE models for anomaly detection in Spallation Neutron Source (SNS) accelerators, the T-CVAE model was tailored to the specific characteristics of KOMAC data by optimizing hyperparameters and leveraging transformer-based architectures for enhanced feature extraction. Experimental results demonstrate that the model effectively learns the distribution of normal signals, as validated through boxplots, the receiver operation characteristics (ROC) curve and kernel density estimation (KDE) analyses. Anomalies are detected through significant reconstruction loss differences between normal and abnormal signals. By reliably identifying pre-fault conditions, the proposed system offers a promising approach to improving operational reliability and minimizing unplanned downtime in KOMAC's proton linear accelerator.
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ISSN:0374-4884
1976-8524
DOI:10.1007/s40042-025-01339-0