Kernel Latent Vector Autoregressive Model for Nonlinear Dynamic Data Modeling and Monitoring

To handle complex non-linearity and latent dynamics in industrial processes, this paper proposes a kernel latent vector auto-regressive (K-LaVAR) algorithm for nonlinear dynamic process modeling and monitoring. By combining kernel mapping and the latent dynamic model, the K-LaVAR algorithm enables nonlinear dimension reduction and circumvents the excessively large dimension issue induced by the kernel mapping. In addition, dual monitoring indices are developed to discern normal variations from dynamic and static aspects with respective statistical control limits. The revamped Tennessee Eastman Process (TEP) simulation benchmark is adopted to demonstrate the advantages of the K LaVAR model in dynamic latent variables extraction, overall monitoring performance improvement, and ensuring prompt detection of process disturbances.