Novel Semi-Supervised Seasonal-Trend VTN for Multimode Process IoT Soft Sensing

The rapid development of soft sensors has significantly enhanced industrial operations by promoting sustainability, safety, and efficiency. However, modern process industries involve highly dynamic systems with multi-mode and nonlinear data, posing challenges for conventional soft sensor models that assume uniform data distributions. Additionally, limited target mode data hinders effective training. To address these issues, we propose the Seasonal-Trend Variational Transformer Network (ST-VTN), a deep probabilistic model based on seasonal-trend decomposition. ST-VTN employs a Transformer-based encoder to extract seasonal and trend patterns from multi-mode data, enhancing latent Gaussian feature representation. A mode discriminator further improves mode-specific feature learning by distinguishing trend characteristics across modes. Built on a variational Bayesian framework, ST-VTN models the regression between latent features and quality variables. A two-stage pre-training and fine-tuning strategy enables effective use of source mode data, even with scarce target mode samples. Experiments on gas turbine and sulfur recovery datasets confirm ST-VTN’s superior performance for multi-mode regression tasks.