Dynamic Process Monitoring Using Total Multirate Linear Gaussian State Space Model

Abstract

Conventional data-driven dynamic process monitoring methods usually rely on data collected at a single sampling rate. The effectiveness of these approaches typically diminishes when analyzing data from multiple sampling rates. To address this gap, this article introduces a new total multirate linear Gaussian state space model. This model is designed for modeling and monitoring in dynamic processes that involve data from various sampling rates. It works by establishing global dynamic latent variables that span across process variables and extracting local static latent variables for each sampling rate. For effective fault detection at different sampling rates, the model incorporates three kinds of statistics. The effectiveness of the proposed method in process monitoring is validated using the multiphase flow facility benchmark and a real papermaking wastewater treatment process.