Variational Information Inference: An Interpretable Disentangled Transfer Learning Quality Prediction for Multirate Industrial Processes

Different sampling rates are common for different variables in industrial processes because of the different electrical properties and requirements of sensors. Especially the sampling rate of quality variables is significantly lower than that of process variables. However, most soft sensors assume that industrial data is uniformly sampled, which differs significantly from actual industrial systems and may affect decision-making in the production process. An interpretable disentangled transfer learning (IDTL) quality prediction is proposed suitable for multirate industrial processes. First, a setness constructor is designed to diversify the original multirate data into multiple multirate sets to preserve information without data loss. Then, a disentangled transfer learning (TL) approach is proposed to infer domain-invariant and domain-specific representations from multiple multirate sets, thereby revealing the intrinsic properties of multirate industrial processes and improving the soft sensor performance. From the perspective of information theory, the theoretical representations for disentanglement and their connection to TL are established, laying a solid theoretical foundation for subsequent TL under complex working conditions. Our theoretical analysis shows that interpretable disentangled TL (IDTL) achieves optimal disentangled representations in equilibrium. Case studies of the debutanizer column dataset and the actual polyester esterification dataset validate the effectiveness of the proposed IDTL. Code is available at https://github.com/heheding/IDTL.