期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 142, 期 -, 页码 255-264出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2015.02.011
关键词
Causal network; Marginal correlations; Multivariate systems; Partial correlations; Statistical process control
类别
资金
- Portuguese Foundation for Science and Technology [SFRH/BD/65794/2009]
- Portuguese FCT [PTDC/EQU-ESI/108374/2008]
- European Union's FEDER through Eixo I do Programa Operacional Factores de Competitividade (POFC) of QREN [FCOMP-01-0124-FEDER-010397]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/65794/2009, PTDC/EQU-ESI/108374/2008] Funding Source: FCT
This article addresses the problem Of monitoring on-line the correlation structure of process variables. Current solutions consist in adopting a moving window approach or an EWMA updating scheme for continuous estimation of the variance-covariance matrix, based on which several monitoring statistics can be derived. The last alternative (EWMA recursion) has attracted more interest, and monitoring statistics have been proposed for the trace of the updated covariance or regarding squared deviations from target. Even though better than the usual benchmark, PCA-MSPC, these methodologies are based only on the (marginal) covariance and therefore unable to discern local changes in the process structure. Partial correlations are a useful tool for enhancing the detection of such structural changes, since they convey more detailed information about the process structure. The results obtained confirm the improved monitoring performance gained with the use of partial correlations. (C) 2015 Elsevier B.V. All rights reserved.
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