| Popis: |
Transfer entropy (TE) is a data-driven, model-free method that can obtain causal relationships between variables and is used in the modeling, monitoring, and fault diagnosis of complex industrial processes. Transfer entropy can detect the causal relationship between variables without the assumption of any basic model, but it is complicated and takes a long time to calculate. In order to solve this limitation, this paper proposes a method of causal network analysis and root cause detection based on parameter variable sequence transfer entropy, which is more robust than traditional transfer entropy, faster in calculation speed, and strong in anti-interference ability, thereby improving the causal path accuracy. The causal network diagram can be obtained by calculating the transfer entropy between sequences, and the root cause of the fault can be found. The effectiveness and accuracy of the method are verified by simulations and actual industrial cases (Tennessee-Eastman process). |
