Optimal false alarm controlled support vector data description for multivariate process monitoring. (May 2018)

• Record Type: Journal Article
• Authors: Kim, Younghoon; Kim, Seoung Bum
• Is Part Of: Journal of process control. Volume 65(2018)
• Page Start: 1
• Page End: 14
• Publication Date: 2018-05
• Subjects: Control chart -- Machine learning -- One-class classification -- Process control -- Support vector data description; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.10.012
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: An optimal false alarm controlled one-class classification algorithm (OSVDD) is proposed. OSVDD is formulated by mixed integer programming. OSVDD controls Type I error rate exactly with cardinality constraint. Control chart based on the OSVDD is presented. The proposed OSVDD-based control chart outperforms the SVDD-based control chart. Abstract: One-class classification plays a key role in the detection of outliers and abnormalities. Recently, several attempts have been made to extend the application of one-class classification techniques to statistical process control problems, where many of these one-class classification-based approaches have used a support vector data description algorithm. The monitoring statistics for a support vector data description-based control chart are sufficiently defined. However, the control limits are not obvious because the procedure used to derive the control limit does not include a method for controlling the false alarm rate (i.e., Type I error rate), which clearly limits its use in process monitoring. In this study, we propose a new multivariate control chart based on a technique for optimal false alarm-controlled support vector data description, which minimizes the radius of a spherically shaped boundary so that it includes the normal data that are equal to an assigned constant value. By modifying this constant value, users can precisely control the proportion of abnormal data determined by the spherically shaped boundary, which equals the expected Type I error rate. We demonstrated the usefulness of the proposed charts in experiments with simulated data and real process data based on a thin film transistor–liquid crystal display.