A systematic comparison of PCA-based statistical process monitoring methods for high-dimensional, time-dependent processes

High-dimensional and time-dependent data pose significant challenges to Statistical Process Monitoring (SPM). Most of the high-dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as non-adaptive and adaptive. Non-adaptive methods include the static PCA approach and Dynamic PCA for data with autocorrelation. Methods, such as Dynamic PCA with Decorrelated Residuals, extend Dynamic PCA to further reduce the effects of autocorrelation and cross-correlation on the monitoring statistics. Recursive PCA and Moving Window PCA, developed for non-stationary data, are adaptive. These fundamental methods will be systematically compared on high-dimensional, time-dependent processes (including the Tennessee Eastman benchmark process) to provide practitioners with guidelines for appropriate monitoring strategies and a sense of how they can be expected to perform. The selection of parameter values for the different methods is also discussed. Finally, the relevant challenges of modeling time-dependent data are discussed, and areas of possible further research are highlighted. This article is protected by copyright. All rights reserved.