Semi-Supervised Deep Dynamic Probabilistic Latent Variable Model for Multimode Process Soft Sensor Application

Nonlinear and multimode characteristics commonly appear in modern industrial process data with increasing complexity and dynamics, which have brought challenges to soft sensor modeling. To solve these issues, in this article, a dynamic mixture variational autoencoder regression model is first proposed to handle the multimode industrial process modeling with dynamic features. Furthermore, to deal with the partially labeled process data with rare quality values and large-scale unlabeled samples, a semi-supervised mixture variational autoencoder regression model is proposed, where a corresponding semi-supervised data sequence division scheme is introduced to make full use of the information in both labeled and unlabeled data. Finally, to verify the feasibility and effectiveness of the proposed methods, the models are applied to a numerical case and a methanation furnace case. The results show that the proposed methods have superior soft sensing performance, compared with the state-of-the-art methods.

Automated summary

In this article, a dynamic mixture variational autoencoder regression model is proposed to handle the multimode industrial process modeling with dynamic features. Furthermore, a semi-supervised mixture variational autoencoder regression model is introduced to deal with the partially labeled process data with rare quality values and large-scale unlabeled samples, where a corresponding semi-supervised data sequence division scheme is introduced. The proposed methods are applied to a numerical case and a methanation furnace case, and the results demonstrate their superior soft sensing performance compared to the state-of-the-art methods.