Data-driven causal inference based on a modified transfer entropy

• We modify the form of transfer entropy to estimate time delay better.
• We propose a strategy to eliminate redundant inferred relations.
• A case study on simulated process is performed to show the effectiveness.

Causality inference and root cause analysis are important for fault diagnosis in the chemical industry. Due to the increasing scale and complexity of chemical processes, data-driven methods become indispensable in causality inference. This paper proposes an approach based on the concept of transfer entropy which was presented by Schreiber in 2000 to generate a causal map. To get a better performance in estimating the time delay of causal relations, a modified form of the transfer entropy is presented in this paper. Case studies on two simulated chemical processes, including the benchmark Tennessee Eastman process are performed to illustrate the effectiveness of this approach.