Dynamic Principal Component Analysis (DPCA) Method for Serially Correlated Data

To effectively monitor production processes, short sampling intervals are required to capture subtle changes, making serial correlation a common characteristic of real-world process data. Exploring Dynamic Principal Component Analysis (DPCA) methods suitable for serially correlated data is essential, involving time-lagged variable expansions and covariance matrix adaptations for dynamic feature extraction.