Locally Weighted Canonical Correlation Analysis for Nonlinear Process Monitoring

A locally weighted canonical correlation analysis (LWCCA) method is proposed to achieve efficient nonlinear process monitoring. The basic idea of the LWCCA is to approximate a nonlinear process through several local linear canonical correlation analysis (CCA) models, in which the determination of sample weights is a key step. Slowly decreasing weights will ignore the local behaviors, whereas rapidly decreasing weights will lead to significant false alarms. A randomized algorithm-based approach is proposed to determine the tunable parameter for calculating the weights. Thus, the LWCCA model explores as much local behavior as possible with the false alarm performance guaranteed. When a local CCA model that characterizes the process input and process output correlation is established, optimal fault detection residuals are generated, and monitoring statistics are established. Two experimental studies are conducted through which the efficiency of the LWCCA method is verified.