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Bio-inspired Information and Communication Technologies. 12th EAI International Conference, BICT 2020, Shanghai, China, July 7-8, 2020, Proceedings

Research Article

Causal Network Analysis and Fault Root Point Detection Based on Symbolic Transfer Entropy

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  • @INPROCEEDINGS{10.1007/978-3-030-57115-3_9,
        author={Jian-Guo Wang and Xiang-Yun Ye and Yuan Yao},
        title={Causal Network Analysis and Fault Root Point Detection Based on Symbolic Transfer Entropy},
        proceedings={Bio-inspired Information and Communication Technologies. 12th EAI International Conference, BICT 2020, Shanghai, China, July 7-8, 2020, Proceedings},
        proceedings_a={BICT},
        year={2020},
        month={8},
        keywords={Symbolic transfer entropy Causal network Root cause of failure},
        doi={10.1007/978-3-030-57115-3_9}
    }
    
  • Jian-Guo Wang
    Xiang-Yun Ye
    Yuan Yao
    Year: 2020
    Causal Network Analysis and Fault Root Point Detection Based on Symbolic Transfer Entropy
    BICT
    Springer
    DOI: 10.1007/978-3-030-57115-3_9
Jian-Guo Wang1,*, Xiang-Yun Ye1, Yuan Yao2
  • 1: School of Mechatronical Engineering and Automation, Shanghai Key Lab of Power Station Automation Technology, Shanghai University
  • 2: Department of Chemical Engineering, National Tsing-Hua University
*Contact email: jgwang@shu.edu.cn

Abstract

Transfer entropy (TE) is a model-free method based on data-driven information theory. It can obtain causal relationships between variables. It has been used for modeling, monitoring and fault diagnosis of complex industrial processes. It can detect the causal relationship between variables without the need to assume any underlying model, but its calculation process is complicated and the calculation time is long. In order to overcome this limitation, symbol transfer entropy is proposed. The symbol transfer entropy is robust and fast to calculate. It can also quantify the dominant direction of information flow between time series with identical and non-identical coupling systems, thereby improving the accuracy of causal paths. Sex. Through the symbolic transfer of entropy, a causal network diagram can be obtained, and the root cause of the fault can be found. The effectiveness and accuracy of the method are verified by simulation and actual industrial cases (Tennessee-Eastman process)

Keywords
Symbolic transfer entropy Causal network Root cause of failure
Published
2020-08-11
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-57115-3_9
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