Correntropy-based partial directed coherence for testing multivariate Granger causality in nonlinear processes

Causal Network Structure Learning Based on Partial Least Squares and Causal Inference of Nonoptimal Performance in the Wastewater Treatment Process

Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to nonoptimal cause identification based on partial-least-squares-based Granger causality analysis (PLS-GC) and Bayesian networks (BNs). The proposed method has three main contributions. First, a multiblock operating performance assessment model is established to correspondingly extract economic-related information and dynamic information. Then, a Bayesian network structure is established by PLS-GC that excludes the strong coupling of variables and simplifies the network structure. Lastly, nonoptimal root cause and and nonoptimal transmission path are identified by Bayesian inference. The effectiveness of the proposed method was verified on Benchmark Simulation Model 1.

Kernel-nonlinear-PDC extends Partial Directed Coherence to detecting nonlinear causal coupling

Here we investigate a new concept, kernel-nonlinear-Partial Directed Coherence, whereby a kernel feature space representation of the data allows detecting nonlinear causal links that are otherwise undetectable through linear modeling. We show that adequate connectivity detection is achievable by applying asympotic decision criteria similar to the ones developed for linear models.