Multivariate Time Delay Estimation Based on Dynamic Characteristic Analytics

Abstract

Time delay widely exists among industrial process variables, which may lead to invalid description of systems and reduce the model accuracy for soft sensor, process monitoring etc. Therefore, it is crucial to estimate time delay for improving model accuracy. In traditional research, time delay estimation (TDE) algorithm for two variables is fully investigated, but little research pays attention to multivariate. The method of pairwise comparison may not only cause high calculation complexity but also cut off the correlation between multivariate. In this work, a new TDE algorithm is proposed to estimate time delay between multivariate, which extracts dynamic latent variable to represent the process using improved dynamic inner PCA algorithm (DiPCA). Dynamic latent variable extracts the autocorrelation and cross-correlation between process variables. Take it as a standard and analyze the time delay, so that the influence of multivariate variables could be comprehensively considered. Since there may be non-linear variables, the significant factor (SF) is used for variable selection, and the multivariate variables are divided into multiple linear subgroups, which makes the analysis result more reasonable. The effectiveness is illustrated with two numerical examples and the Tennessee Eastman process.