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Process monitoring method based on correlation variable classification and vine copula
Author(s) -
Cui Qun,
Li Shaojun
Publication year - 2020
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23702
Subject(s) - vine copula , vine , copula (linguistics) , linear subspace , subspace topology , correlation , mathematics , statistics , computer science , econometrics , artificial intelligence , ecology , geometry , biology
Chemical processes are becoming increasingly complicated, leading to an increase in process variables and more complex relationships among them. The vine copula has a significant advantage in portraying the dependence of high‐dimensional variables. However, as the dimensions increase, the vine copula model incurs a high computational load; such pressure greatly reduces model efficiency. Relationships among variables in the industrial process are complex. Different variables may be strongly or weakly associated or even independent. This paper proposes a process monitoring method based on correlation variable classification and vine copula. The weighted correlation measure is first used to divide variables into a correlated subspace and weakly correlated subspace. Then, two vine structures, C‐vine and D‐vine, are applied to the correlated and weakly correlated subspaces, respectively. This method takes advantage of C‐vine for correlated variables and the flexibility of D‐vine for weakly correlated variables. Finally, comprehensive statistics are established based on different subspaces. Monitoring results of the numerical system and the Tennessee Eastman process demonstrate the effectiveness and validity of the proposed method.