期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 39, 期 2, 页码 2166-2171出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2011.07.045
关键词
Support vector domain description; Kernel k-means; Multimodal process; Fault detection; Statistical process control
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. (C) 2011 Elsevier Ltd. All rights reserved.
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