Learning Deep Correlated Representations for Nonlinear Process Monitoring

Deep neural network (DNN) extracts hierarchical representations from process data and is promising for nonlinear process monitoring. Obtaining meaningful representations and generating efficient fault detection residual are the main challenges in DNN-based monitoring. This study proposes a regularized deep correlated representation (RDCR) method that incorporates deep belief networks (DBNs) and canonical correlation analysis (CCA) for nonlinear process monitoring. Hierarchical representations are initially extracted using DBN to process input and output variables. Second, hierarchical representations from process input and output are modeled through CCA to characterize the relationship between them. Efficient fault detection residuals are then generated, and monitoring statistics are established. CCA-based monitoring relies on the most correlated representations; thus, a multiobjective evolutionary optimization-based regularization is performed to select the most correlated representations and eliminate the influence of unrelated representations. The advantages of the RDCR monitoring are verified through experimental studies on a numerical example and the Tennessee Eastman process.