Improvement of the sensitivity of T2 quality control charts by variable grouping and dimension reducing

Abstract

With increasing number of variables the Hotelling's T statistic can detect only larger failures in the variables. A new method is introduced for reducing the dimension of the Hotelling's statistic in order to detect smaller failures. The basic idea is to group some variables into a combined variable and to calculate the T value from this variable and from the remaining variables. As the new calculated variable has not a Gaussian distribution a proper static transformation is applied. Both uncorrelated and correlated data are dealt with. In the latter case principal component analysis is used before calculating T. Several simulations show that the sensitivity of the new T control charts is improved. The theory is confirmed by an application of sensor fault monitoring of a gas analyzer.

DOI: 10.1109/CDC.2011.6161140

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Cite this paper

@inproceedings{Friebel2011ImprovementOT, title={Improvement of the sensitivity of T2 quality control charts by variable grouping and dimension reducing}, author={Thomas Friebel and Robert Haber}, booktitle={CDC-ECE}, year={2011} }