Structured fault information-aided canonical variate analysis model for dynamic process monitoring

Process monitoring is one of the most crucial fundamental components in industrial processes. Traditional multivariate statistical analysis modeling only relies on data collected in normal condition, reflecting the lack of sensitivity to faults. This paper proposed a fault information-aided canonical variate analysis (FICVA) and a structured FICVA (SFICVA) monitoring strategy to improve fault detection rate. In FICVA, canonical variate analysis (CVA) is firstly applied to extract state and residual subspace based on normal data. Then, according to the data collected from one fault, the above subspaces are further decomposed into fault relevant and irrelevant state subspaces, and fault relevant and irrelevant residual subspaces for guaranteeing fault information can be concentrated. The correlation between normal data in various subspaces and the ability to concentrate abnormal information in fault data from fault relevant subspaces are theoretically analyzed. Then, to combine diverse fault information from different scenarios, SFICVA monitoring strategy is performed through Bayesian inference to ensemble a series of FICVA sub-models constructed by different faults to obtain more effective performance. Through a numerical example, experimental research on Tennessee Eastman process (TEP) and actual blast furnace ironmaking process (BFIP), rationality of FICVA method and effectiveness of SFICVA monitoring strategy are fully verified.(c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a fault information-aided canonical variate analysis (FICVA) and a structured FICVA (SFICVA) monitoring strategy to improve fault detection rate. In FICVA, canonical variate analysis (CVA) is used to extract state and residual subspace based on normal data. Then, the subspaces are further decomposed into fault relevant and irrelevant subspaces to concentrate fault information.