Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 142, Issue -, Pages 265-275Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2015.02.007
Keywords
Marginal correlations; Partial correlations; Multivariate systems; Fault diagnosis; Control chart design
Categories
Funding
- 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] Funding Source: FCT
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Control charts based on partial correlations have proved to be an effective approach to detect fine deviations on the underlying structure of process data. The prompt detection of such faults is dependent on the proper selection of the control chart design parameters, namely their control limits, subgroup size (off-line case) and forgetting factor (on-line case). In this article, specific guidelines are provided to attain the desired detection power while maintaining the intended false alarm rate. A formal relationship that relates the on-line monitoring approach with the simpler off-line implementation is also derived. This relationship can then be used to design on-line control charts based on insights and results obtained with the more interpretable off-line version. A new fault diagnosis procedure is also introduced in order to take advantage of the partial correlations ability to remove the effects of faulty variables in the data, and thus obtain higher identification accuracy and decrease the total time invested in diagnosis activities and troubleshooting. (C) 2015 Elsevier B.V. All rights reserved.
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