Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

Authors:
Issam Ben Khediri;Mohamed Limam;Claus Weihs
Affiliations:
Department of Statistics, Dortmund University of Technology, Germany;Laboratory of Operational Research, Decision and Control, Institut Supérieur de Gestion, University of Tunis, Tunisia;Department of Statistics, Dortmund University of Technology, Germany
Venue:
Computers and Industrial Engineering
Year:
2011

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Abstract

On-line control of nonlinear nonstationary processes using multivariate statistical methods has recently prompt a lot of interest due to its industrial practical importance. Indeed basic process control methods do not allow monitoring of such processes. For this purpose this study proposes a variable window real-time monitoring system based on a fast block adaptive Kernel Principal Component Analysis scheme. While previous adaptive KPCA models allow only handling of one observation at a time, in this study we propose a way to fast update or downdate the KPCA model when a block of data is provided and not only one observation. Using a variable window size procedure to determine the model size and adaptive chart parameters, this model is applied to monitor two simulated benchmark processes. A comparison of performances of the adopted control strategy with various Principal Component Analysis (PCA) control models shows that the derived strategy is robust and yields better detection abilities of disturbances.