Enhanced dynamic latent variable analysis for dynamic process monitoring

Background: Process dynamic, also known as temporal correlation, is widespread in industrial processes and can greatly affect process monitoring results. In dynamic process monitoring, dynamic latent variable (DLV) mainly considers the autocorrelation and cross-correlation of variables, while slow feature analysis (SFA) only considers the varying speed of variables. Complex dynamic information needs to be fully considered. Methods: This paper proposes an enhanced dynamic latent variable (EDLV) analysis. First, EDLV focuses on both the varying speed and correlation of variables when extracting dynamic latent variables. Therefore, The proposed method achieves the distinction between the normal change of operating conditions and the occurrence of faults. Second, the process data is broken into dynamic and static subspaces for monitoring respectively, which benefits the accurate detection of different faults. Significant Findings: Tennessee Eastman (TE) process and three-phase flow facility are used to verify the effectiveness of the proposed method. It is proved that EDLV can divide dynamic process more reasonably and obtain better detection results.