Multimode Process Monitoring Based on Switching Autoregressive Dynamic Latent Variable Model

TLDR: A set of switching ARDLV models are proposed in the probabilistic framework, which extends the original single model to its multimode form and a hierarchical fault detection method is developed for process monitoring in the multimode processes.

Abstract: In most industrials, the dynamic characteristics are very common and should be paid enough attention for process control and monitoring purposes. As a high-order Bayesian network model, autoregressive dynamic latent variable (ARDLV) is able to effectively extract both autocorrelations and cross-correlations in data for a dynamic process. However, the operating conditions will be frequently changed in a real production line, which indicates that the measurements cannot be described using a single steady-state model. In this paper, a set of switching ARDLV models are proposed in the probabilistic framework, which extends the original single model to its multimode form. Based on it, a hierarchical fault detection method is developed for process monitoring in the multimode processes. Finally, the proposed method is demonstrated by a numerical example and a real predecarburization unit in an ammonia synthesis process.