Dynamic Causal Analysis with Operator-Centric Visualization for Managing Industrial Alarm Floods

Managing industrial alarm floods is a challenge that demands analytical tools that assist human operators online. While existing methods offer some insight into causality analysis, they often fall short of addressing dynamic causal dependencies, particularly during the plant-wide propagation of abnormal situations. Additionally, evolving alarm floods, indicative of escalating disturbances, have received limited attention. This paper introduces a novel approach for analyzing causality in alarm floods. By combining a data-driven nonlinear causality estimator with supplementary process information and operator knowledge, the proposed method effectively assesses dynamic causal dependencies among relevant process variables. Moreover, we present an operator-centric tool that visualizes these dependencies, enabling operators to comprehend and respond to evolving situations more effectively. The potential of our approach is exemplified using the "Tennessee-Eastman" process, which highlights its capability in analyzing complex and time-varying causal relationships.