Advances in technology allowed the fast and easy creation and configuration of industrial alarms. The growth in the number of alarms, however, brought some problems to the alarm management systems, for example, alarm flooding. In this paper, it is propose a new method to detect the causal relationships between industrial alarm variables using Transfer Entropy theory and the structural learning of the Bayesian networks K2 Algorithm as its fundamental basis. This work argues that the detection of these relationships can help to reduce the number of alarms in the alarm management systems, avoiding the overloading of operators in case of plant failures. To validate the proposal, a case study using the well-known plant-wide simulator Tennessee Eastman Process was performed. In this study case the graph of the causal relationships obtained by the proposed method was compatible with the system functioning.

技术的进步使工业警报的创建和配置变得快速而轻松。但是，告警数量的增长也给告警管理系统带来了一些问题，比如告警泛洪。本文以传递熵理论和贝叶斯网络K2算法的结构学习为基础，提出了一种检测工业报警变量因果关系的新方法。这项工作认为，检测这些关系有助于减少警报管理系统中的警报数量，避免在工厂发生故障时操作员过载。为了验证该提议，我们使用著名的全厂模拟器田纳西伊士曼工艺进行了案例研究。