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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/2509.13621 |
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| _version_ | 1866915497992257536 |
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| author | Sulc, Antonin Hellert, Thorsten Hunt, Steven |
| author_facet | Sulc, Antonin Hellert, Thorsten Hunt, Steven |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13621 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Anomaly Detection in ALS EPICS Event Logs Sulc, Antonin Hellert, Thorsten Hunt, Steven Machine Learning 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. |
| title | Unsupervised Anomaly Detection in ALS EPICS Event Logs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.13621 |