An ensemble framework based on multivariate statistical analysis for process monitoring

Industrial process data shows the coexistence of multiple characteristics, such as linear, nonlinear, Gaussian, non-Gaussian, and dynamic. Various multivariate statistical analysis methods were applied for different process characteristics. However, using only one method may not have the ability to capture complex characteristics and the relationship between the variables. Therefore, this study designs an ensemble monitoring framework that can automatically determine the local models and the optimal monitoring variables. Firstly, multiple models that can extract different data characteristics are selected as candidate models. Secondly, combining the fault information and intelligent optimization algorithm, the monitoring performance differences are compared when different local models are selected for ensemble, so as to realize the elimination of monitoring redundant models. Finally, the process monitoring is implemented by integrating the determined local models. Under this framework, multiple models that describe the complex characteristics of process data from different aspects can be automatically determined to establish an ensemble monitoring model based on the various characteristics of the process data. Tennessee Eastman process and wastewater treatment process are used to verify the monitoring performance of the proposed framework.