کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
388116 | 660916 | 2012 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Kernel k-means clustering based local support vector domain description fault detection of multimodal processes Kernel k-means clustering based local support vector domain description fault detection of multimodal processes](/preview/png/388116.png)
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.
► This study proposes a monitoring procedure based on separate local SVDD models to control multimodal processes.
► We used Kernel k-means clustering to separate the different process modes.
► A procedure based on principal components analysis is introduced in order to determine the optimal number of clusters.
► Results show that this procedure allows better detection rate as comparison to global SVDD model.
Journal: Expert Systems with Applications - Volume 39, Issue 2, 1 February 2012, Pages 2166–2171