Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach

TLDR

Partial least squares techniques, particularly nonlinear iterative PLS, are widely employed for modeling process data in data‑based monitoring. The study proposes a dynamic PLS algorithm to model dynamic processes by capturing the dynamic correlation between measurement and quality data blocks. The method uses a dynamic total PLS model to decompose the measurement block into four subspaces for monitoring. The dynamic T‑PLS model efficiently detects quality‑related abnormalities, as demonstrated by multiple examples.

Abstract

In data-based monitoring field, the nonlinear iterative partial least squares procedure has been a useful tool for process data modeling, which is also the foundation of projection to latent structures (PLS) models. To describe the dynamic processes properly, a dynamic PLS algorithm is proposed in this paper for dynamic process modeling, which captures the dynamic correlation between the measurement block and quality data block. For the purpose of process monitoring, a dynamic total PLS (T-PLS) model is presented to decompose the measurement block into four subspaces. The new model is the dynamic extension of the T-PLS model, which is efficient for detecting quality-related abnormal situation. Several examples are given to show the effectiveness of dynamic T-PLS models and the corresponding fault detection methods.