Recursive correlated representation learning for adaptive monitoring of slowly varying processes

Abstract

Correlated representation learning has found wide usage in process monitoring. However, slow and normal changes frequently occur in practical production processes, which may lead to model mismatch and degrade monitoring performance. Therefore, updating the monitoring model online and involving recently processed data information are important. This study proposes a recursive correlated representation learning (RCRL) incorporating an approach for online model updating for adaptive monitoring of slowly varying processes. First, an initial canonical correlation analysis-based monitoring model is established using historical process data. Second, an online model updating criterion is developed, and updating procedures are provided to reflect online data information and update monitoring model in a timely manner. Then, monitoring statistics are established and decision making logic is established to identify process status. The fitness of the monitoring scheme is increased because the online process information is considered to update the model. The proposed RCRL-based monitoring scheme is applied on a numerical example and a lab-scale distillation process. The effectiveness and superiority of the RCRL approach are verified.

Keywords: Data-driven monitoring; Process monitoring; Recursive correlated representation learning; Slowly varying processes.