Kernel Latent State Space Modeling for Nonlinear Dynamic Process Monitoring

Monitoring nonlinear dynamic processes is challenging due to the inherent complexity and uncertainty in industrial systems. Fault detection and fault identification are critical components of process monitoring, yet existing methods often struggle to capture predictable dynamics with compact model parameters. This could compromise the fault detection performance and further impede the fault identification accuracy. This article proposes a unified modeling framework to strategically address these challenges through nonlinearity handling, most predictable dynamics extraction, and parsimonious system parameterization. Building on this enhanced capability, the proposed model integrates fault detection and identification with mutual information, thereby improving the identification accuracy. Validations on the revamped Tennessee Eastman Process and a real-world multiphase flow facility demonstrate superior fault detection metrics and fault identification capabilities compared to alternative approaches.