Virtual Sample Generation Method Based on Feature Scaling and Co-training Label for Industrial Data Modeling

Real time detection of production quality and environmental protection indices is the basis for realizing operational optimization of industrial processes. Insufficient samples are one of the main problems of building soft sensor model for such difficulty-to-measure key indices. Aiming at this issue, a novel virtual sample generation (VSG) strategy based on feature scaling and co-training label is proposed. First, the variational autoencoder (VAE) is used to make feature reduction for visualize analysis. Second, the typical sample cluster and its boundary are selected for generating the virtual sample inputs. Third, the feature elevation based on VAE decoding are used to regain the original input dimension. Fourth, the co-training method is used to pseudo-label and select the optimal virtual sample outputs. Fifth, the hybrid samples are used to construct the soft measuring model. The proposed method is verified by the dioxin emission data of an actual municipal solid waste incineration process in Beijing.