A Simplified Recursive Dynamic Pca Based Monitoring Scheme for Imperial Smelting Process

Imperial Smelting Process (ISP) is one of the main methods for Zinc and Lead smelting. Its operating conditions change very frequently due to the changes of work points, which always lead to false alarms. We focus on this issue and present a recursive Dynamic PCA (RPCA) based monitoring scheme for ISP to adapt process changes. We present a simplified RPCA algorithm based on first-order perturbation analysis (FOP), which is a rank-one update of eigenvalues and their corresponding eigenvectors of an observation covariance matrix. The computation cost is greatly decreased. We also present two new statistics for process monitoring in ISP to avoid numerical computation difficulty induced by the traditional statistics. Finally, we apply the proposed method to real data from ISP. The results show that the proposed scheme can be able to eliminate false alarms and detect faults efficiently.