Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference

Abstract

Dynamic principal component analysis (DPCA) and dynamic independent component analysis (DICA), as the frequently-used dimensional reduction methods, have been widely applied to monitor dynamic process. Considering the respective advantages of DPCA and DICA in different data distribution characteristics, this paper proposes a novel process monitoring algorithm named DPCA, DICA and Bayesian Inference (DPCA–DICA–BI). The main idea of DPCA–DICA–BI is to put the process variables with same distribution characteristic (Gaussian or non-Gaussian) into a block on the basis of the variable normality by Jarque–Bera test and then to respectively apply DPCA and DICA in Gaussian and non-Gaussian blocks. Finally, to combine the monitoring performance of both blocks, Bayesian inference is employed to make an integrated decision. The DPCA–DICA–BI as well as PCA, ICA, DPCA, and DICA has been used to a numerical example and Tennessee Eastman process. The simulation results show the superiority of DPCA–DICA–BI.