Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms

In process monitoring, principal component analysis (PCA) is a very popular method and has found wide applications. Conventionally, a fixed PCA model is used for monitoring. This paper presents the use of both recursive PCA (RPCA) and moving window PCA (MWPCA) to online update the PCA model and its corresponding control limits for monitoring statistics. An efficient algorithm is derived based on rank-one matrix update of the covariance matrix, which is tailored for RPCA and MWPCA computations. By the proposed method, the performance of process monitoring can be improved in two aspects. First, more consistent PCA model and control limits for monitoring statistics are resulted because of the increasing number of normal observations for modeling. Second, for parameter-varying processes, when natural drifting behavior or changing of operation region is acceptable, more reasonable PCA model and control limits for monitoring statistics are obtained in an adaptive manner. Simulation results have shown the effectiveness of the proposed approaches compared to the conventional PCA and RPCA methods. (C) 2010 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.