Nonlinear dynamic process monitoring integrating slow feature analysis with Kullback-Leibler Divergence

Abstract In chemical production processes, the nonlinear dynamic characteristics of data frequently result in suboptimal performance of process monitoring methods. To address this issue, a novel fault detection method integrating Slow Feature Analysis with Kullback-Leibler Divergence (SFA-KLD) is proposed. First, the dataset is decomposed using slow feature analysis to extract the corresponding slow features and fast features. Second, by integrating sliding window technique, the mean and variance of the fast features in each sample window are computed sequentially. Finally, the Kullback-Leibler Divergence is employed to develop novel statistical measures for fault detection. The proposed method is validated through two numerical examples and simulation experiments conducted on the Tennessee Eastman (TE) process. Experimental results demonstrate that, compared to traditional methods, SFA-KLD achieves better fault detection performance in nonlinear dynamic process monitoring.