A new transfer entropy approach based on information granulation and clustering for root cause analysis

Abstract

In complex industrial facilities, a fault may generate in a unit and easily propagate to other units, resulting in degraded performance of process operations and deteriorating the situation. Therefore, it is of great importance to trace the propagation of faults and locate the root causes. The non-parametric causality inference technique known as Transfer Entropy (TE) is successful in identifying cause–effect relations for both nonlinear and linear processes. Even though, TE has two drawbacks limiting its practical use in real industries: (1) It requires the studied process be stationary whereas the presence of faults may lead to nonstationary changes; (2) The computational complexity of TE is high while the real application could be sensitive to the computational cost. Motivated by these issues, this paper proposes a new transfer entropy approach based on the information granulation and clustering to identify the root causes of faults. The contributions of the proposed approach are twofold: (1) the Information Granulation based Transfer Entropy (IGTE) and Information Granulation based Direct Transfer Entropy (IGDTE) are proposed to infer causal and direct causal relations; (2) an Ordering Points To Identify the Clustering Structure (OPTICS) clustering based Probability Density Function (PDF) estimator is designed to estimate joint/conditional probabilities based on the information granule. Two case studies are used to illustrate the effectiveness of the proposed approach.