Root Cause Diagnosis of Process Faults Using Conditional Granger Causality Analysis and Maximum Spanning Tree

In industrial processes, various types of faults often propagate from one unit to another along information and material flows. In severe cases, fault propagation can eventually affect the entire plant, leading to the reduction in product quality and productivity, and even causing damages. In order to avoid these issues, effective root cause diagnosis is desired because the correct identification of the sources of process abnormalities is critically important for restoring the system to its normal condition in a timely manner. In recent years, the data-driven causality analysis method, such as Granger causality (GC) test, has been adopted to identify the causes of process faults. However, the conventional pairwise GC only considers the causal relationship between a pair of time series. In multivariate cases, repeated pairwise analyses are often conducted, which yet often give over-complex and misleading results. To solve this problem, in this research, the multivariate GC technique, which measures the conditional dependence between time series, is utilized to construct the causal map between process variables. In addition, the obtained causal map is further simplified by finding its maximum spanning tree, facilitating the identification of the root cause. The feasibility of the proposed method is illustrated by case studies. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.