Soft Sensor for Multiphase and Multimode Processes Based on Gaussian Mixture Regression

Abstract

For complex industrial plants with multiphase and multimode characteristic, traditional multivariate statistical soft sensor methods are not applicable as Gaussian distribution assumption of data is not met. Thus Gaussian mixture model (GMM) is used to approximate data distribution. In the previous GMM-based soft sensor modeling researches, GMM is only used to identify operating mode, then other regression algorithms like PLS are used for quality prediction in different localized modes. In this article, an existing method—Gaussian mixture regression (GMR) is introduced for soft sensor modeling, which has been already successfully applied in robot programming by demonstration (PbD). Different from past GMM-based soft sensors, GMR is directly used for regression. In GMR, data mode identification and regression are incorporated into one model, thus there is no need to switch prediction model when data mode has changed. Feasibility and efficiency of GMR based soft sensor are validated in the fermentation process and TE process.