Research Article
Causal Network Analysis and Fault Root Point Detection Based on Symbolic Transfer Entropy
@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
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)
