Qi Chu, Li Jince, Li Hongguang
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
ISA Trans. 2023 May;136:284-296. doi: 10.1016/j.isatra.2022.10.023. Epub 2022 Oct 29.
The transfer entropy (TE) based causality analysis is able to provide a typical solution for fault rooting of industrial processes. However, short-term disturbances that occur during nominal operations of chemical processes are usually neglected because of the fixed time window of TE for global data distributions. Inspired by the selective attention idea, we propose attention transfer entropy (ATE) that helps to locate prominent targets. Concerning temporal features of industrial time series, prior knowledge is employed for constructing an interpretable model. We verify the reliability and effectiveness of the method with coal gasification process data. Additionally, the algorithm is compared to conventional causality analysis methods, proving that ATE enjoys excellent performances in rooting short-term disturbances with lower calculation burden.
基于转移熵（TE）的因果关系分析能够为工业过程的故障溯源提供一种典型解决方案。然而，由于TE针对全局数据分布的固定时间窗口，化学过程正常运行期间出现的短期干扰通常被忽略。受选择性注意思想的启发，我们提出了注意力转移熵（ATE），它有助于定位突出目标。考虑到工业时间序列的时间特征，利用先验知识构建一个可解释模型。我们用煤气化过程数据验证了该方法的可靠性和有效性。此外，将该算法与传统因果分析方法进行比较，证明ATE在溯源短期干扰方面具有优异性能，且计算负担更低。
