Bayesian inference based reorganized multiple characteristics subspaces fusion strategy for dynamic process monitoring

Abstract

Abstract The measured data of the large-scale industrial process usually has shown the nonstationary, non-Gaussian, dynamic characteristics, however, most traditional methods did not consider the multiple characteristics coexistence and viewed all the variables as a whole. To make up the deficiencies of the conventional methods, this paper proposes a novel reorganized multiple characteristics subspaces integrated with Bayesian inference (RMS-BI) monitoring strategy for large-scale dynamic process. Firstly, the overall process variables are divided into three subspaces by Jarque–Bera (J–B) test and Augmented Dickey–Fuller (ADF) test, which are the nonstationary subspace, stationary Gaussian subspace, and stationary non-Gaussian subspace. Then, the cointegration analysis (CA), dynamic principal component analysis (DPCA) and dynamic independent component analysis (DICA) models are singled out to monitor the abnormities in the three subspaces, respectively. After that, the monitoring results of the multiple subspaces are integrated by Bayesian inference (BI) to obtain global monitoring statistics. Finally, case studies on the Tennessee Eastman process and the real-world diesel working process are used to demonstrate the availability of the RMS-BI method.