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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.13421 |
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| _version_ | 1866914094219526144 |
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| author | Lassalle, Charly Bonnay, Patrick Bouly, Frédéric Di Giacomo, Marco Ghribi, Adnan |
| author_facet | Lassalle, Charly Bonnay, Patrick Bouly, Frédéric Di Giacomo, Marco Ghribi, Adnan |
| contents | SPIRAL2 is a state-of-the-art superconducting linear accelerator for heavy ions. The radiofrequency operation of the linac can be disrupted by anomalies that affect its reliability. This work leverages fast, multivariate time series post-mortem data from the Low-Level Radio Frequency (LLRF) systems to differentiate anomaly groups. However, interpreting these anomalies traditionally relies on expert analysis, with certain behaviours remaining obscure even to experienced observers. By adopting the Time2Feat pipeline, this study explores the interpretability of anomalies through feature selection, paving the way for real-time state observers. Clustering dashboards are presented, allowing the use of multiple clustering algorithms easily configurable and tools to help for visualizing results. A case study on distinguishing electronic quenches and false quench alarms in postmortem data is highlighted. Thereby, a fast and reliable K-Nearest Neighbours (KNN) classifier is proposed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development of fault identification pipeline for SPIRAL2 LLRF data Lassalle, Charly Bonnay, Patrick Bouly, Frédéric Di Giacomo, Marco Ghribi, Adnan Accelerator Physics SPIRAL2 is a state-of-the-art superconducting linear accelerator for heavy ions. The radiofrequency operation of the linac can be disrupted by anomalies that affect its reliability. This work leverages fast, multivariate time series post-mortem data from the Low-Level Radio Frequency (LLRF) systems to differentiate anomaly groups. However, interpreting these anomalies traditionally relies on expert analysis, with certain behaviours remaining obscure even to experienced observers. By adopting the Time2Feat pipeline, this study explores the interpretability of anomalies through feature selection, paving the way for real-time state observers. Clustering dashboards are presented, allowing the use of multiple clustering algorithms easily configurable and tools to help for visualizing results. A case study on distinguishing electronic quenches and false quench alarms in postmortem data is highlighted. Thereby, a fast and reliable K-Nearest Neighbours (KNN) classifier is proposed. |
| title | Development of fault identification pipeline for SPIRAL2 LLRF data |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2510.13421 |