Soft sensor modeling for small data scenarios based on data enhancement and selective ensemble

Abstract

Data-driven soft sensors have been widely used to facilitate real-time estimations of difficult-to-measure variables. However, sufficient high-quality data are often difficult to obtain due to high cost or low sampling rate. Thus, a soft sensor method based on data enhancement and selective ensemble (DESE) is proposed. First, a generative model is proposed for generating virtual labeled samples by combining supervised variational autoencoder (SVAE) and Wasserstein GAN with gradient penalty (WGAN-gp), which is referred to as SV-WGANgp. Then, SV-WGANgp is trained on various resampled training subsets to generate different sets of virtual samples. Next, diverse enhanced base models are constructed with the extended training sets. Subsequently, a multi-objective optimization (MOO) is utilized to achieve ensemble pruning. Finally, the final prediction is obtained by fusing the selected models. The application results on an industrial chlortetracycline fermentation process and a simulated penicillin fermentation process verify the effectiveness and superiority of the proposed methods.