Parameter selection guidelines for adaptive PCA‐based control charts

Methods based on principal component analysis (PCA) are widely used for statistical process monitoring of high‐dimensional processes. Allowing the monitoring model to update as new observations are acquired extends this class of approaches to non‐stationary processes. The updating procedure is governed by a weighting parameter that defines the rate at which older observations are discarded, and therefore, it greatly affects model quality and monitoring performance. Additionally, monitoring non‐stationary processes can require adjustments to the parameters defining the control limits of adaptive PCA in order to achieve the intended false detection rate. These two aspects require careful consideration prior the implementation of adaptive PCA. Towards this end, approaches are given in this paper for both parameter selection challenges. Results are presented for a simulation and two real‐life industrial process examples.