Alarm Flood Analysis by Hierarchical Clustering of the Probabilistic Dependency between Alarms

I. Weiß, J. Kinghorst, Thomas Kröger, Mina Fahimi Pirehgalin, B. Vogel‐Heuser
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引用次数: 5

Abstract

Pattern detection in alarm data aroused great interest in research activities in recent years. Reducing alarm floods, which arise of causal dependencies of equipment and their alarms in automated production systems, is aimed to decrease alarm rates and aggregate information to valuable notifications for the operator. However, common alarm flood analysis often lacks robustness against random occurring alarms or interfering alarm patterns, which disturb the known structure of sequences. Therefore, this contribution introduce a preprocessing step, calculating the dependencies of alarms probabilistically. This approach meet the fuzziness of alarm systems regarding precision in time domain and interfering alarm signals. The results, based on two different industrial data sets, reveal high accurateness of the clusters defined by the proposed method. Alarm patterns can be detected even though the sequences are interrupted and interfered by further alarms or further causal dependent alarm floods.
基于告警间概率依赖的分层聚类分析
近年来,报警数据中的模式检测引起了人们的极大兴趣。在自动化生产系统中,由于设备及其报警器的因果关系而产生的报警泛滥,减少报警泛滥的目的是降低报警率,并将信息汇总为对操作员有价值的通知。然而,常见的报警洪水分析往往缺乏对随机发生的报警或干扰报警模式的鲁棒性,这些模式会干扰序列的已知结构。因此,本文引入了一个预处理步骤,以概率方式计算警报的依赖关系。该方法满足了报警系统在时域精度和报警信号干扰方面的模糊性。基于两个不同的工业数据集的结果表明,该方法定义的聚类具有较高的准确性。即使序列被进一步的警报或进一步因果依赖的警报洪水中断和干扰,也可以检测到警报模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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