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Parameter selection guidelines for adaptive PCA‐based control charts
Author(s) -
Schmitt Eric,
Rato Tiago,
De Ketelaere Bart,
Reis Marco,
Hubert Mia
Publication year - 2016
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2783
Subject(s) - principal component analysis , weighting , computer science , statistical process control , selection (genetic algorithm) , data mining , process (computing) , control chart , principal (computer security) , model selection , control (management) , machine learning , artificial intelligence , medicine , radiology , operating system
Methods based on principal component analysis (PCA) are widely used for statistical process monitoring of high‐dimensional processes. Allowing the monitoring model to update as new observations are acquired extends this class of approaches to non‐stationary processes. The updating procedure is governed by a weighting parameter that defines the rate at which older observations are discarded, and therefore, it greatly affects model quality and monitoring performance. Additionally, monitoring non‐stationary processes can require adjustments to the parameters defining the control limits of adaptive PCA in order to achieve the intended false detection rate. These two aspects require careful consideration prior the implementation of adaptive PCA. Towards this end, approaches are given in this paper for both parameter selection challenges. Results are presented for a simulation and two real‐life industrial process examples. Copyright © 2016 John Wiley & Sons, Ltd.