Kernel k-means clustering based local support vector domain description fault detection of multimodal processes

Authors:
Issam Ben Khediri;Claus Weihs;Mohamed Limam
Affiliations:
Department of Statistics, Dortmund University of Technology, Germany;Department of Statistics, Dortmund University of Technology, Germany;Laboratory of Operational Research, Decision and Control, Institut Supérieur de Gestion, University of Tunis, Tunisia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 6
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Abstract

The multimodal and nonlinear structure of a system makes process modeling and control quite complex. To monitor processes that have these characteristics, this paper presents a procedure based on kernel techniques for unsupervised learning that are able to separate different nonlinear process modes and to effectively detect faults. These techniques are named Kernel k-means (KK-means) clustering and support vector domain description (SVDD). In order to assess this monitoring strategy two different simulation studies as well as a real case study of an Etch Metal process are performed. Results show that the proposed control chart provides efficient fault detection performance with reduced false alarm rates.