A Causal Fusion Inference Method for Industrial Alarm Root Cause Analysis Based on Process Topology and Alarm Event Data

Modern industrial systems are usually in large scale, consisting of massive components and variables that form a complex system topology. Owing to the interconnections among devices, a fault may occur and propagate to exert widespread influences and lead to a variety of alarms. Obtaining the root causes of alarms is beneficial to the decision supports in making corrective alarm responses. Existing data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event data. To improve the accuracy, this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events. A Granger causality inference method considering process topology is exploited to find out the causal relations among alarms. The topological nodes are used as the inputs of the model, and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes process. The root cause is then obtained from the directed acyclic graph (DAG) among alarm variables. The effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process (TEP) model.