用自然语言处理分析加速器日志，自动识别异常事件序列。
Unsupervised Anomaly Detection in ALS EPICS Event Logs
- 将日志当作文本，用嵌入技术转为向量表示
- 通过神经网络实时计算异常得分，发现偏离正常行为的事件
- 适合需要快速定位故障前兆的工业系统运维人员
本文提出一种针对先进光源（ALS）的自动化故障分析框架，处理其EPICS控制系统生成的实时事件日志。将日志条目视为自然语言，利用语义嵌入技术将其转换为上下文向量表示。基于正常运行数据训练的序列感知神经网络，对每个事件实时分配异常分数。该方法能够标记出与基线行为的偏差，帮助操作员迅速识别导致复杂系统故障的关键事件序列。
原文摘要 · Abstract (English)
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.
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