Data-driven causal inference based on a modified transfer entropy |
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| Affiliation: | 1. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;2. Dow Chemical Company, Texas Operations, 2301 Brazosport Blvd., Freeport, TX 77541, USA;3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;4. Department of Automation, Tsinghua University and Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China;1. Department of Automation, Shanghai Jiao Tong University, China;2. Manchester Business School, The University of Manchester, Manchester M15 6PB, UK;3. Institute of System Science and Control Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou, China;1. Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;2. Beijing Key Laboratory of Industrial Big Data System and Application, Tsinghua University, Beijing 100084, China;1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, Shandong Province, China;2. Shandong Electric Power Research Institute for State Grid Corporation of China, Jinan, Shandong Province, China |
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| Abstract: | Causality inference and root cause analysis are important for fault diagnosis in the chemical industry. Due to the increasing scale and complexity of chemical processes, data-driven methods become indispensable in causality inference. This paper proposes an approach based on the concept of transfer entropy which was presented by Schreiber in 2000 to generate a causal map. To get a better performance in estimating the time delay of causal relations, a modified form of the transfer entropy is presented in this paper. Case studies on two simulated chemical processes, including the benchmark Tennessee Eastman process are performed to illustrate the effectiveness of this approach. |
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| Keywords: | Causal inference Transfer entropy Process safety |
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