Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

Abstract

To cope with the soft sensor modeling of unobserved multimode nonlinear processes, this paper proposes a modified kernel partial least squares (KPLS) by integrating latent factor clustering (LFC), called LFC-KPLS. In the proposed method, the process data are first divided into several batches orderly, and then projected onto the latent space by using the nonlinear functional expansion technology. In the latent space, partial least squares method is applied to compute the regression coefficients between the input variables and output variable of each batch. These regression coefficients, called the latent factors, can describe the functional relationships in the unobserved multimode data. Therefore, the latent factors are used for mode clustering so that the process data with similar functional relations can be clustered in one mode together. For each mode, the nonlinear soft sensor is established based on KPLS. To assign the mode of the online query sample, a mode identification strategy based on Bayesian inference is designed for the soft sensor online prediction. Finally, two cases studies are adopted to validate the proposed method.

Autogenerated Multilocal PLS Models without Pre-classification for Quality Monitoring of Nonlinear Processes with Unevenly Distributed Data

“…19 Wang et al ( 2019) used a self-organized framework to construct multiple KPLS models and used conditional probability density analysis to identify the sample mode. 20 Deng et al (2020) proposed a modified KPLS method that uses latent factors and identified sample modes using Bayesian inference. 21 Wu et al (2021) proposed a locality preserving randomized canonical correlation analysis method for real-time nonlinear process monitoring.…”

“…20 Deng et al (2020) proposed a modified KPLS method that uses latent factors and identified sample modes using Bayesian inference. 21 Wu et al (2021) proposed a locality preserving randomized canonical correlation analysis method for real-time nonlinear process monitoring. 22 Zhang et al (2021) investigated a mixture of probabilistic PCA with clustering for process monitoring.…”