Dynamic Non-Gaussian hybrid serial modeling for industrial process monitoring

Process monitoring has been widely used for fault detection and performance supervision in modern industrial processes. Nevertheless, hybrid characteristics including Gaussianity, non-Gaussianity and dynamic usually coexist in process variables, which brings new challenge to obtain satisfactory monitoring performance. Aiming at the hybrid characteristics problem, this paper proposes a dynamic non-Gaussian hybrid serial modeling method for industrial process monitoring. First, a multivariate non-Gaussianity evaluation method is utilized to divide industrial process variables into the Gaussian variable subspace and the non-Gaussian variable subspace. Afterwards considering the hybrid characteristics including Gaussianity, non-Gaussianity and dynamic at information level, a dynamic principal component analysis (DPCA)-dynamic independent component analysis (DICA)-based hybrid serial modeling method is presented for analyzing simultaneously the dynamic Gaussian and non-Gaussian information in each variable subspace. Subsequently, the final monitoring results are obtained using Bayesian inference and the DPCA-DICA-based hybrid serial similarity factor is proposed for fault identification. Unlike the existing methods, the proposed method analyzes simultaneously the Gaussianity, non-Gaussianity and dynamic at different levels of variable and information for improving performance. The case studies including a numerical system, the Tennessee Eastman process and a practical industrial process demonstrate its feasibility and effectiveness.