Nonlinear Process Monitoring Based on Improved Kernel ICA

An industrial process often presents a large number of measured variables, which are usually driven by fewer nonlinear essential variables. An improved kernel independent component analysis based on particle swarm optimization (PSO-KICA) is presented to extract these essential variables from the process recorded variables in the KPCA feature space. Process faults can be detected more efficiently by monitoring the independent components. To monitor the non-Gaussian distributed independent components obtained by PSO-KICA, the empirical control limit is employed. The proposed approach is illustrated by the application to the nonisothermal CSTR process