论文标题
使用图嵌入检测相关警报
Detection of Correlated Alarms Using Graph Embedding
论文作者
论文摘要
工业警报系统最近在网络复杂性和警报数量方面取得了长足的进步。在这些系统中，复杂性和警报数量的增加提出了降低系统效率并引起对操作员不信任的挑战，这可能会导致广泛的损害。警报效率低下的一个促成因素是相关警报。这些警报不包含新信息，而只会使操作员感到困惑。本文试图提出一种基于人工智能方法来帮助操作员的新方法来检测相关的警报。所提出的方法基于图形嵌入和警报聚类，从而检测到相关警报。为了评估所提出的方法，对田纳西州著名的 - 东方过程进行了案例研究。
Industrial alarm systems have recently progressed considerably in terms of network complexity and the number of alarms. The increase in complexity and number of alarms presents challenges in these systems that decrease system efficiency and cause distrust of the operator, which might result in widespread damages. One contributing factor in alarm inefficiency is the correlated alarms. These alarms do not contain new information and only confuse the operator. This paper tries to present a novel method for detecting correlated alarms based on artificial intelligence methods to help the operator. The proposed method is based on graph embedding and alarm clustering, resulting in the detection of correlated alarms. To evaluate the proposed method, a case study is conducted on the well-known Tennessee-Eastman process.
