Gi-Hu Kim, Hae-Seong Jeong, Han-Sung Kim, Hyeok-Jung Kwon, Dong-Hwan Kim
{"title":"Anomaly detection in KOMAC high-power systems using transformer-based conditional variational autoencoder","authors":"Gi-Hu Kim, Hae-Seong Jeong, Han-Sung Kim, Hyeok-Jung Kwon, Dong-Hwan Kim","doi":"10.1007/s40042-025-01339-0","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":677,"journal":{"name":"Journal of the Korean Physical Society","volume":"87 on","pages":"883 - 891"},"PeriodicalIF":0.9000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Physical Society","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s40042-025-01339-0","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The Journal of the Korean Physical Society (JKPS) covers all fields of physics spanning from statistical physics and condensed matter physics to particle physics. The manuscript to be published in JKPS is required to hold the originality, significance, and recent completeness. The journal is composed of Full paper, Letters, and Brief sections. In addition, featured articles with outstanding results are selected by the Editorial board and introduced in the online version. For emphasis on aspect of international journal, several world-distinguished researchers join the Editorial board. High quality of papers may be express-published when it is recommended or requested.