基于传递熵与改进K2算法生成报警传播网络

Generation of Alarm Propagation Network Based on Transfer Entropy and Improved K2 Algorithm

工业报警变量数量增多所导致的“报警泛滥”问题,严重影响了报警系统的应有功能.针对此问题,提出一种从过程报警数据集中学习报警变量传递关系的方法.首先,利用传递熵具有准确衡量一阶或多阶自相关性变量间因果关系的特点,识别变量间的因果关系.其次以变量间熵大小为准则,保留传递信息量较大的节点,最后考虑变量在不同状态所占时间比重对K2算法进行改进,学习得到最终的报警传播网络.通过在田纳西伊斯曼过程数据集上的验证发现,该方法能够对报警发生的根本原因做出判断并较好地学习得到报警传播路径.

The problem of“alarm flooding”caused by the increase in the number of industrial alarm variables has seriously affected the proper functions of alarm systems.In response,this study proposes a method to learn the transfer relationship of alarm variables from the process alarm data set.First,leveraging the ability of transfer entropy to accurately measure the causal relationship between variables with first-order or multi-order autocorrelation,the study identifies the causal relationship between variables.Second,depending on the entropy value between variables,the nodes that transmit a large amount of information are reserved.Finally,the K2 algorithm is improved by considering the time proportions of variables in different states,and the final alarm propagation network is obtained by learning.The verification on the Tennessee Eastman process data set reveals that the method can judge the root cause of the alarm and well achieve alarm propagation paths through learning.