An attention transfer entropy based causality analysis with applications in rooting short-term disturbances for chemical processes.

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.

Stream-data-clustering based adaptive alarm threshold setting approaches for industrial processes with multiple operating conditions.

The setting of alarm thresholds is a critical concern of alarm management systems in industrial processes. Conventional alarm thresholds less consider changes of operating conditions in production processes, which degrades the effectiveness of alarm management systems. In response to this problem, this paper proposes an adaptive alarm threshold setting approach based on stream data clustering (SDC). Firstly, we develop a stream data clustering algorithm termed as a-DenStream algorithm which realizes industrial flow data clustering through online micro-clustering and offline integration. Subsequently, we develop the C-BOUND algorithm to extract the edges of the clustering results. In response to alarms associated with multiple operating conditions, segmentations are conducted to set alarm threshold groups and build a multi-condition alarm threshold model. Consequently, an adaptive alarm threshold setting method based on model matching is created. The effectiveness of the proposed method is demonstrated by experiments on a coal gasification chemical process. The proposed method provides a potential application for industrial processes with multiple operating conditions alarm managements.