Process monitoring based on improved recursive PCA methods by adaptive extracting principal components

Two adaptive principal component analysis methods are improved based on adaptive extracting principal components (PCs) in process monitoring: recursive PCA (RPCA) and moving window PCA (MWPCA). An adaptive extracting PC algorithm is proposed using the threshold method based on the score rule in sport games to determine the number of PCs in real time. It can effectively overcome the shortcomings of the conventional cumulative percent variance method in obtaining the number of PCs. Moreover, two improved RPCA and MWPCA methods are proposed using the new threshold method to monitor an industrial process online. Similary to the forgetting factor in RPCA, an optimal variable moving window size is selected, adding forgetting factors into the data samples and covariance matrices, respectively. The results show the validity of improvements compared with the original RPCA and MWPCA in Tennessee Eastman process monitoring.