Real-time process monitoring using kernel distances. Issue 21 (1st November 2016)

• Record Type: Journal Article
• Authors: Wei, Qingming; Huang, Wenpo; Jiang, Wei; Zhao, Wenhui
• Is Part Of: International journal of production research. Volume 54:Issue 21(2016)
• Page Start: 6563
• Page End: 6578
• Publication Date: 2016-11-01
• Subjects: statistical process control -- linear discriminant analysis -- real-time contrast -- supervised learning; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2016.1173257
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : Real-time monitoring is an important task in process control. It often relies on estimation of process parameters in Phase I and Phase II and aims to identify significant differences between the estimates when triggering signals. Real-time contrast (RTC) control charts use classification methods to separate the Phase I and Phase II data and monitor the classification probabilities. However, since the classification probability statistics take discretely distributed values, the corresponding RTC charts become less efficient in the detection ability. In this paper, we propose to use distance-based RTC statistics for process monitoring, which are related to the distance from observations to the classification boundary. We illustrate our idea using the kernel linear discriminant analysis (KLDA) method and develop three distance-based KLDA statistics for RTC monitoring. The performance of the KLDA distance-based charting methods is compared with the classification probability-based control charts. Our results indicate that the distance-based RTC charts are more efficient than the class of probability-based control charts. A real example is used to illustrate the performance of the proposed method.