Abstract

We develop a novel fault diagnosis method that combines information entropy with transfer entropy for chemical processes. On the basis of the correlation information entropy, an online process monitoring model for the abnormal conditions of chemical processes is first established. Then, the combination of information entropy and transfer entropy is used for fault diagnosis. We apply the generalized correlation coefficient of mutual information in information entropy in extracting the condition feature of process variables. Subsequently, transfer entropy analysis is performed to obtain the fault chain. The database between the fault feature variables and the root cause is established. Finally, the Tennessee-Eastman (TE) chemical process is taken as a case study.

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