A Systematic Comparison of Statistical Process Monitoring Methods for High-dimensional, Time-dependent Processes KU Leuven
© 2016 American Institute of Chemical Engineers. High-dimensional and time-dependent data pose significant challenges to Statistical Process Monitoring. Most of the high-dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as nonadaptive and adaptive. Nonadaptive methods include the static PCA approach and Dynamic Principal Component Analysis (DPCA) for data ...