Soft Sensor Modeling Based on Vector- Quantized Weighted-Wasserstein VAE for Polyester Polymerization Process

The uneven distribution of process industrial data poses a significant challenge for soft sensor modeling. Hence, it is necessary to employ generative models to generate some new data used for augmenting the distribution fitting ability of the model by the full utilization of sparse regions. Existing generative models are mostly applied in the image and text generation fields, which are more suitable for discrete data where each variable only consists of integers. To enhance the applicability of generative models in industrial data modeling, a novel vector-quantized weighted-Wasserstein variational autoencoder based soft sensor is developed in this article to solve uneven distribution data. The proposed model further enhances the advantages of present generative models in generating qualified data by redesigning the model structure for clustering and training the data, thereby greatly alleviating poor generative performance caused by sparse regions. Specifically, first, the vector quantization strategy is designed to address the problem of sparse regions. After that, the sparse regions are transferred into the boundary of each group to generate more well-proportioned points. Then, the weighted and continuous variation distance mixture strategy is introduced to better evaluate the divergence between the real and learned distribution without mutation in the training part. Finally, a soft sensor model with the above data augmentation strategies is developed. By comparing it with three state-of-the-art augmentation strategies on a real polyester polymerization dataset, the superiority of the proposed soft sensor model with the novel data augmentation strategies is unequivocally verified.