Process monitoring via dependence description based on variable selection and vine copula

Abstract

Abstract Process monitoring is crucial to ensure the safety of industrial processes. Generally, the monitoring process involves all measured variables; however, large industrial processes contain many redundant variables. For a method based on describing the intrinsic correlation relationships among variables, the vine copula-based dependence description (VCDD) method shows significant advantages for describing nonlinear and non-Gaussian processes. However, redundant and irrelevant variables adversely affect the correlation between variables containing the most important information, reducing model performance. The lack of research in this area may substantially weaken the advantages of VCDD for fault monitoring. Therefore, this article introduces a variable selection vine copula dependence description monitoring model. It utilizes known faults as validation data to select the relevant variables for constructing the VCDD model, specifically designed for monitoring known faults. Furthermore, to prevent information loss, the remaining unselected variables are also employed to create a separate VCDD model, dedicated to monitoring unknown faults. The performance of the proposed method is verified by a numerical example, the Tennessee-Eastman process and the Penicillin fermentation process.