A hierarchical multimode dynamic process monitoring scheme and its application to the Tennessee Eastman process⁎

Abstract

Multimode characteristics commonly exist in modern industrial processes. Previous multi-model approaches treat steady states and transitions separately. However, identifying each mode is often tedious, generally achieved through clustering, requiring operators to tune hyperparameters extensively. As practitioners prefer a concise and easily implemented approach for multimode dynamic process monitoring, we initially propose a hierarchical scheme to simplify the modeling process while enhancing monitoring performance. Our method iteratively constructs dynamic models in a hierarchical, monitoring-oriented manner without mode partition. It offers three advantages. Firstly, modeling is directly conducted following a hierarchical structure driven by monitoring indexes, which is more concise and ensures monitoring performance. Secondly, by eliminating mode partition, only three hyperparameters, such as model order and the termination condition, need to be decided by humans. This significantly reduces human labour and facilitates the applicability of the proposed method across various processes. Lastly, by focusing on dynamic characteristics rather than steady-state and transitional modes, our method reduces the number of required models for a given process, resulting in a simpler multi-model structure that still ensures monitoring performance.