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Dynamic process monitoring using dynamic latent‐variable and canonical correlation analysis model
Author(s) -
Lou Siwei,
Wu Ping,
Guo Lingling,
He Jiajun,
Zhang Xujie,
Gao Jinfeng
Publication year - 2021
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.23923
Subject(s) - linear subspace , canonical correlation , control theory (sociology) , process (computing) , latent variable , computer science , canonical form , mathematics , algorithm , mathematical optimization , artificial intelligence , geometry , control (management) , operating system , pure mathematics
Dynamic latent‐variable (DLV) modelling is a very effective method for dynamic process monitoring. However, the DLV method only focuses on auto‐correlation in process data but ignores the cross‐correlation between inputs and outputs. To overcome this shortcoming, a novel dynamic process monitoring method using dynamic‐latent variable and canonical correlation analysis (DLV‐CCA) is proposed. Considering the dynamics in the process data, the proposed DLV‐CCA method first utilizes the DLV method to decompose the input space into input dynamic and static subspaces. The output space is also decomposed into output dynamic and static subspaces by DLV. Then, canonical correlation analysis (CCA) is used to explore the cross‐correlation between the dynamic subspaces (including input dynamic and output dynamic subspaces) and the static subspaces (including input static and output static subspaces). According to the CCA results, residual signals are generated and corresponding Hotelling's T 2 statistics are established to detect variations in these residual signals. A numerical example and a closed‐loop continuous stirred‐tank reactor (CSTR) are employed to demonstrate the superior performance of the DLV‐CCA based process monitoring compared with other relevant methods.