Nonlinear dynamic quality-related process monitoring based on dynamic total kernel PLS

Total projection to latent structures (T-PLS) has been used for quality-related process monitoring. Compared to PLS, the T-PLS is more effectively in detecting the quality-related abnormal situations for linear and static processes. To describe the nonlinear and dynamic process characteristics, a new monitoring approach, dynamic total kernel projection to latent structures (DT-KPLS), is proposed in this paper for the nonlinear dynamic quality-related process monitoring. DT-KPLS consists of two parts: (i) T-KPLS decomposes the process data X into four subspaces in a high-dimensional feature space; (ii) the time-lagged extension of data matrix is performed before applying T-KPLS to capture process dynamic. Finally, the effectiveness of the proposed method is demonstrated by a cyanide leaching process.