ENBIS: European Network for Business and Industrial Statistics

Interpreting Multivariate Control Chart Signals using Computational Intelligence Techniques

Abstract

Submitted by

Vassilis Plagianakos

Authors

V.P. Plagianakos Department of Informatics with Biomedical Applications, University of Central Greece, Papasiopoulou 2-4, Lamia, 35100, Greece. S. Bersimis Department of Informatics & Telematics, Harokopio University, 89, Harokopou Street, 176 71, Kallithea, Greece.

Abstract

Multivariate control charts are used for monitoring and controlling the process mean and the process variability of multivariate processes. These control charts are able to recognize an out-of-control process. The identification of an out-of-control variable or variables after a multivariate control chart signals has been an interesting topic for many researchers over the last few years. This work reviews promising techniques for interpreting an out-of-control signal, while introduces new algorithms and models based on recently proposed computational intelligence techniques. The proposed methods, which have a sound mathematical background, are thoroughly investigated and have proven to be efficient and effective.

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