Granger Causality Detection Based on Neural Network

Abstract

Plant-wide oscillations are very common in industrial processes. When a control unit oscillates during the process, the oscillations will propagate through the connectivity between the units, which will cause poor product quality and higher energy consumption. It is important to diagnose the root cause of plant-wide oscillations. Generally, methods for estimating Granger causality use linear models such as autoregressive models. This paper proposes using Granger causality analysis based on the neural network for root cause diagnosis, which effectively solves the problem that Granger causality analysis based on linear models cannot handle non-linear data. The Granger causality detection model based on neural network is successfully applied to the plant-wide oscillation root location of industrial process, and the correct root cause is detected, which proves the feasibility and effectiveness of the method.