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

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.