Nonlinear Dynamic Process Monitoring Using Canonical Variate Kernel Analysis

Most industrial systems today are nonlinear and dynamic. Traditional fault detection techniques show their limits because they can hardly extract both nonlinear and dynamic features simultaneously. Canonical variate analysis (CVA) shows its excellent monitoring performance in fault detection for dynamic processes but is not applicable to nonlinear processes. Inspired by the CVA method, a novel nonlinear dynamic process monitoring method, namely, the “canonical variate kernel analysis” (CVKA), is proposed in this work. The way to extract nonlinear features is different from a traditional kernel canonical variate analysis (KCVA). In a sequential structure, the new approach firstly extracts the linear dynamic features from the data through the CVA method, followed by a kernel principal component analysis to extract nonlinear features from the CVA residual space. The new CVKA method is then applied to a TE process case study, proving the excellent performance of CVKA compared to other common approaches in dynamic nonlinear process monitoring for TE-like processes.