Root Cause Diagnosis of Oscillation-Type Plant Faults Using Nonlinear Causality Analysis

Abstract

In industrial plants, productivity and product quality are often impacted by different types of faults. Specifically, oscillations commonly exist in many close-loop controlled processes. An oscillation generated in a single unit may propagate along process flows and feedback loops, affecting the performance of the entire plant. Therefore, it is critical to diagnose such oscillation-type plant faults and find out the root cause, so as to achieve fast recovery from abnormalities. In recent research, Granger causality (GC) test, which uses a statistical hypothesis test to judge whether a time series is useful in forecasting another, has been adopted to discover the root cause of plant-wide oscillations. However, the conventional GC is based on linear autoregressive (AR) models and cannot accurately handle the nonlinear causal relationship between time series. To solve this problem, a two-step diagnosis approach is proposed in this paper. In the first step, the faulty variables are isolated by the least absolute shrinkage and selection operator (LASSO) based reconstruction analysis method. Then, the nonlinear GC test based on Gaussian process regression (GPR) is conducted on the isolated process variables to discover the path of fault propagation and find out the root cause of the fault.