A novel kernel dynamic inner slow feature analysis method for dynamic nonlinear process concurrent monitoring of operating point deviations and process dynamics anomalies

Abstract

A novel nonlinear dynamic inner slow feature analysis method is proposed for dynamic nonlinear process concurrent monitoring of operating point deviations and process dynamics anomalies. In this method, the nonlinear correlation and serial autocorrelation are considered meanwhile to extract the serial auto-correlated latent slow features with explicit dynamic representation. In order to approve slow features (SFs) with explicit dynamic representation from nonlinear dynamic process data, a new multi-goal optimization question is formulized with constraints of the extract latent variable catch some variation information and mutually orthogonal. After the nonlinear dynamic inner slow feature analysis model is trained from data, a corresponding detection strategy is also developed to perform process condition concurrent monitoring. Finally, the superiority and effectiveness of the proposed monitoring method are demonstrated by a numerical simulation case and an actual cold rolling mill case.