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Graph-based Grouping of Statistical Dependent Alarms in Automated Production Systems

机译：自动化生产系统中基于图表的统计相关警报分组

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Causal relations between sources of industrial alarms can result in alarm floods, leading to a large number of simultaneously occurring alarms. Hence, various approaches exist to detect such alarm floods in historical alarm data with the purpose of operator support, root cause analysis and predictive maintenance. However, such approaches often suffer from randomly occurring, non-related alarms, resulting in invalid data patterns. Furthermore, the high amount of different alarm messages limits the approaches’ capabilities due to high computational costs. To overcome both problems, this paper introduces a graph-based approach to automatically split historical alarm data into groups of statistically depending alarms. The resulting groups, based on the conditional probability between alarms, can be extracted automatically, showing promising results for further data-mining approaches analyzing the groups’ dynamics individually. The developed method is evaluated based on historical alarm data recorded from a real industrial manufacturing plant.
机译：工业警报源之间的因果关系可能导致警报泛滥，从而导致大量同时发生的警报。因此，存在各种方法来检测历史警报数据中的此类警报泛滥，目的是为操作员提供支持，根本原因分析和预测性维护。但是，这样的方法经常遭受随机发生的，不相关的警报，从而导致无效的数据模式。此外，由于高昂的计算成本，大量的不同警报消息限制了方法的功能。为了克服这两个问题，本文介绍了一种基于图的方法，可将历史警报数据自动分为统计相关的警报组。根据警报之间的条件概率，可以自动提取生成的组，显示有希望的结果，可用于进一步的数据挖掘方法来单独分析组的动态。基于从真实的工业制造工厂记录的历史警报数据对开发的方法进行评估。

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