A method for detecting causal relationships between industrial alarm variables using Transfer Entropy and K2 algorithm. (October 2021)

• Record Type: Journal Article
• Authors: Abreu, Rute Souza de; Nunes, Yuri Thomas; Guedes, Luiz Affonso; Silva, Ivanovitch
• Is Part Of: Journal of process control. Volume 106(2021)
• Page Start: 142
• Page End: 154
• Publication Date: 2021-10
• Subjects: Bayesian networks -- Causal relationships -- Transfer Entropy; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2021.09.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: 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. Highlights: Joining of Transfer Entropy and Bayesian Networks for detecting causal relationships. A method for alarm flooding reduction. Production of a machine-learned pre-order for K2 algorithm.