One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes

Abstract

In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residuals—the difference between the actual value and the fitted value—with the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-class classification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-class classification-based control charts and the traditional Hotelling's T ² chart.

Keywords:

• Autocorrelated multivariate process
• Data mining algorithm
• One-class classification algorithm
• Statistical process control

Mathematics Subject Classification:

• 62
• 62-07

Acknowledgment

We would like to thank the Editor and referees, whose comments helped greatly in improving the presentation of this article. S. B. Kim's work was supported by startup funds from Korea University.