Semi-Supervised Relevance Variable Selection and Hierarchical Feature Regularization Variational Autoencoder for Nonlinear Quality-Related Process Monitoring

With the complexity and intelligence of the process, process monitoring plays a vital role in ensuring production safety and product quality, in which quality-related fault detection techniques have been extensively studied. The traditional monitoring strategies have the problem that the production process cannot be accurately monitored when the quality indicators are insufficient. It is difficult to extract accurate quality-related features with the guidance of limited quality labels. In addition, the model trained with limited quality indicators will get caught in overfitting problems. Motivated by the limitations, a novel semi-supervised relevance variable selection and hierarchical feature regularization variational autoencoder (SS-RVS-HFRVAE) is proposed to monitoring the process with limited quality indicators. First, a hierarchical feature regularization (HFR) variational autoencoder (VAE) is proposed to overcome the overfitting problem brought by limited quality labels. Secondly, a semi-supervised relevance variable selection (SS-RVS) strategy is proposed to extract the most quality-related features under semi-supervised process dataset. Finally, the experiments on numerical case and Tennessee Eastman (TE) process describe the effectiveness of the proposed method in semi-supervised quality-related process monitoring.