Simultaneous Static and Dynamic Analysis for Fine-Scale Identification of Process Operation Statuses

Abstract

Closed-loop control is commonly used in industrial processes to track setpoints or regulate process disturbances. Process dynamics resulting from closed-loop control are reflected in data mainly in two aspects, namely serial correlation and variation of response speed. Concurrent analysis of both aspects from data has not been fully investigated in the literature. In this work, a combined strategy of canonical variate analysis and slow feature analysis is proposed to monitor process dynamics resulting from closed-loop control by exploring both serial correlations and variation speed of process data. First, the canonical subspaces reflecting serial correlation are modeled by maximizing correlation between the past and future values of the process data. Then, both the serially correlated canonical subspace and its residual subspace are further explored to extract the slow features, which are representations of process variation speed. The proposed method provides a meaningful physical interpretation and in-depth process analysis with considerations of process dynamics under closed-loop control. Besides, it provides a concurrent monitoring of both process faults and operating condition deviations, resulting in fine-scale identification of different operation statuses. To demonstrate the feasibility and effectiveness, the proposed strategy is tested in a simulated typical chemical process under closed-loop control, namely the three-phase flow process.