Abstract Alarm messages in industrial processes are designed to draw attention to abnormalities that require timely assessment or intervention. However, in practice, alarms are arbitrarily and excessively defined by process operators resulting numerous nuisance and chattering alarms that are simply a source of distraction. Countless techniques are available for the retrospective filtering of alarm data, e.g., adding time delays and deadbands to existing alarm settings. As an alternative, in the present paper, instead of filtering or modifying existing alarms, a method for the design of alarm messages being informative for fault detection is proposed which takes into consideration that the occurring alarm messages originally should be optimal for fault detection and identification. This methodology utilizes a machine learning technique, the decision tree classifier, which provides linguistically well-interpretable models without the modification of the measured process variables. Furthermore, an online application of the defined alarm messages for fault identification is presented using a sliding window-based data preprocessing approach. The effectiveness of the proposed methodology is demonstrated in terms of the analysis of a well-known benchmark simulator of a vinyl-acetate production technology, where the complexity of the simulator is considered to be sufficient for the testing of alarm systems. Note to practitioners: Process-specific knowledge can be used to label historical process data to normal operating and fault-specific periods. Alarm generation should be designed to be able to detect and isolate faulty states. Using decision trees, optimal”cuts” or alarm limits for the purpose of fault classification can be defined utilizing a labelled dataset. The results apply to a variety of industries operating with online control systems, and especially timely in the chemical industry.

中文翻译：

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用于信息过程警报定义和基于警报的故障分类的决策树

摘要 工业过程中的警报消息旨在引起对需要及时评估或干预的异常的注意。然而，在实践中，警报是由过程操作员任意和过度定义的，导致了许多令人讨厌和颤抖的警报，这些警报只是分散注意力的一个来源。无数技术可用于警报数据的追溯过滤，例如，在现有警报设置中添加时间延迟和死区。作为替代方案，在本文中，不是过滤或修改现有警报，而是提出了一种为故障检测提供信息的警报消息设计方法，该方法考虑到发生的警报消息最初应该是故障检测和识别的最佳选择. 这种方法利用机器学习技术，决策树分类器，它提供了语言上很好解释的模型，而无需修改测量的过程变量。此外，使用基于滑动窗口的数据预处理方法呈现了用于故障识别的定义警报消息的在线应用。通过对著名的醋酸乙烯酯生产技术基准模拟器的分析，证明了所提出方法的有效性，其中模拟器的复杂性被认为足以测试警报系统。对从业者的注意：过程特定的知识可用于将历史过程数据标记为正常操作和特定故障时期。警报生成应设计为能够检测和隔离故障状态。使用决策树，可以使用标记数据集定义用于故障分类的最佳“削减”或警报限制。结果适用于使用在线控制系统运行的各种行业，尤其​​是在化工行业。