Identifying the time of a step change with multivariate single control charts

Abstract

Change point estimation procedures simplify the efforts to search for and identify special causes in multivariate statistical process monitoring. After a signal is generated by the simultaneously used control charts or a single control chart, add-on change point procedure estimates the time of the change. In this study, multivariate joint change point estimation performance for simultaneous monitoring of both location and dispersion is compared under the assumption that various single charts are used to monitor the process. The change detection performance for several structural changes for the mean vector and covariance matrix is also discussed. It is concluded that choice of the control chart to obtain a signal may affect the change point detection performance.

Self-starting single control charts for multivariate processes: a comparison of methods

“…They also can be informative for determining the type of a process disturbance. Further investigation on the performance assessment of the change point estimator for multivariate performances is available in Doǧu & Kocakoc (2011 and Doǧu (2015).…”

Univariate and Multivariate Linear Profiles Using Max-Type Extended Exponentially Weighted Moving Average Schemes

“…Recently, authors in [14]- [21] have developed control procedures for monitoring both process parameters using a single control chart. In most of the cases, these classical control charts are designed to monitor one or more quality characteristics that are not functionally related; [22]- [24]. However, in many real-life applications, there is a functional relationship between one dependent variable and one, two or more explanatory variables.…”