Supervised Nonlinear Dynamic System for Soft Sensor Application Aided by Variational Auto-Encoder
Bingbing Shen, Zhiqiang Ge
Abstract
Dynamic data modeling has been attracting much attention from researchers and has been introduced into the probabilistic latent variable model in the process industry. It is a huge challenge to extend these dynamic probabilistic latent variable models to nonlinear forms. In this article, a supervised nonlinear dynamic system (NDS) based on variational auto-encoder (VAE) is introduced for processes with dynamic behaviors and nonlinear characteristics. Based on the framework of VAE, which has a probabilistic data representation and a high fitting ability, the supervised NDS can extract effective nonlinear features for latent variable regression. The feasibility of the proposed supervised NDS is tested on two numerical examples and an industrial case. Detailed comparisons verify the effectiveness and superiority of the proposed model.