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| URL | https://doi.org/10.18429/JACoW-ICALEPCS2025-TUPD113 |
|---|---|
| Title | Unsupervised Anomaly Detection in ALS EPICS Event Logs |
| Authors |
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| Abstract | This paper introduces an automated fault analysis framework for the Advanced Light Source (ALS) that processes real-time event logs from its EPICS control system. By treating log entries as natural language, we transform them into contextual vector representations using semantic embedding techniques. A sequence-aware neural network, trained on normal operational data, assigns a real-time anomaly score to each event. This method flags deviations from baseline behavior, enabling operators to rapidly identify the critical event sequences that precede complex system failures. |
| Paper | download: TUPD113.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 20th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'25) |
| Series | |
| Location | Chicago, IL, USA |
| Date | 20-26 Sep 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | Kent Wootton (ANL), Jeffery Biss (FNAL), Gustavo Bruno (ANL), Jeff Kirchman (ANL), Kelly Jaje (ANL), Denise Skiadopoulos (ANL), SuYin Grass Wang (ANL) |
| Online ISBN | 978-3-95450-255-4 |
| Online ISSN | 2226-0358 |
| Received | 05 September 2025 |
| Revised | 24 September 2025 |
| Accepted | 29 October 2025 |
| Issued | 25 November 2025 |
| DOI | 10.18429/JACoW-ICALEPCS2025-TUPD113 |
| Pages | 763-768 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |