Simplified Granger causality map for data-driven root cause diagnosis of process disturbances

被引:51
作者
Liu, Yi [1 ]
Chen, Han-Sheng [2 ]
Wu, Haibin [3 ]
Dai, Yun [1 ]
Yao, Yuan [2 ]
Yan, Zhengbing [4 ]
机构
[1] Zhejiang Univ Technol, Inst Proc Equipment & Control Engn, Hangzhou 310023, Peoples R China
[2] Natl Tsing Hua Univ, Dept Chem Engn, Hsinchu 30013, Taiwan
[3] Natl Taiwan Univ, Grad Inst Elect Engn, Taipei 10617, Taiwan
[4] Wenzhou Univ, Coll Math Phys & Elect Informat Engn, Wenzhou 325035, Peoples R China
基金
中国国家自然科学基金;
关键词
Process monitoring; Root cause diagnosis; Granger causality; Maximum spanning tree; Causality analysis; MULTIVARIATE FAULT ISOLATION; VARIABLE SELECTION; DISCRIMINANT-ANALYSIS; BATCH PROCESSES;
D O I
10.1016/j.jprocont.2020.09.006
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Root cause diagnosis is an important step in process monitoring, which aims to identify the sources of process disturbances. The primary challenge is that process disturbances propagate between different operating units because of the flow of material and information. Data-driven causality analysis techniques, such as Granger causality (GC) test, have been widely adopted to construct process causal maps for root cause diagnosis. However, the generated causal map is over-complicated and difficult to interpret because of the existence of process loops and the violation of statistical assumptions. In this work, a two-step procedure is proposed to solve this problem. First, a causal map is built by adopting the conditional GC analysis, which is viewed as a graph in the next step. In this graph, each vertex corresponds to a process variable under investigation, while the weight of the edge connecting two vertices is the F-value calculated by conditional GC. This graph is then simplified by computing its maximum spanning tree. Thus, the results of the causality analysis are transformed into a directed acyclic graph, which eliminates all loops, highlights the root cause variable, and facilitates the diagnosis. The feasibility of this method is illustrated with the application to the Tennessee Eastman benchmark process. In the investigated case studies, the proposed method outperforms the conditional GC test and provides an easy way to identify the root cause of process disturbances. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码:45 / 54
页数:10
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