A novel soft sensor method based on stacked fusion autoencoder with feature enhancement for industrial application

Abstract

Soft sensor is pivotal in contemporary industrial processes. However, extracting effective feature representations from intricate process data remains a challenging task. To this end, a feature enhancement stacked fusion autoencoder (FE-SFAE) is proposed to leverage spatiotemporal characteristics and linear residual fusion for modeling. Firstly, the feature enhancement (FE) module that incorporates multidirectional delayed transform (MDT) and bilateral smoothing constraint canonical polyadic (BS-CP) decomposition is developed to alleviate the issues of time-lag and spatial dependence. Based on the FE module, complementary data can be generated for the subsequent stage. Secondly, to address the problem of variables being overly non-linearized, we propose a linear residual fusion gate mechanism. On this basis, we design the model named FE-SFAE that not only captures spatiotemporal features in complementary data but also balances the degree of network nonlinearization. Finally, the superiority of FE-SFAE is demonstrated through an industrial case involving the esterification process of polyester polymerization.