An Adaptive Dimension Reduction Scheme for Monitoring Feedback-controlled Processes

Detecting dynamic mean shifts is particularly important in monitoring feedback-controlled processes in which time-varying shifts are usually observed When multivariate control charts are being utilized, one way to improve performance is to reduce dimensions. However, it is difficult to identify and remove non-informative variables statically in a process with dynamic shifts, as the contribution of each variable changes continuously over time. In this paper, we propose an adaptive dimension reduction scheme that aims to reduce dimensions of multivariate control charts through online variable evaluation and selection. The resulting chart is expected to keep only informative variables and hence maximize the sensitivity of control charts. Specifically, two sets of projection matrices are presented and dimension reduction is achieved via projecting process vectors into a low-dimensional space. Although developed based on feedback-controlled processes, the proposed scheme can be easily extended to monitor general multivariate applications. Copyright (C) 2008 John Wiley & Sons, Ltd.