Active nonstationary variables selection based just-in-time co-integration analysis and slow feature analysis monitoring approach for dynamic processes

Abstract

For industrial processes, operating conditions tend to be time varying, leading to the time-varying nonstationary characteristics. In this paper, an active nonstationary variables selection-based just-in-time co-integration analysis and slow feature analysis monitoring approach is proposed to explore the real-time variations in dynamic processes. To this end, by analyzing the time-varying stationarity of online data, active nonstationary variables are selected. Meanwhile, a just-in-time strategy is used to update the offline model. On this basis, co-integration analysis and slow feature analysis are developed for extracting long-run equilibrium relationships and slowly varying features. A comprehensive statistic is generated by Bayesian inference to monitor the operation status. With the active nonstationary information extraction, the proposed method emphasizes the online nonstationary characteristics, which allows the monitoring model to effectively capture the dynamic variations. Two case studies on benchmark processes show the advantages and feasibility of the proposed method.