Triple-Gated Bidirectional Variational Pyramid Network for Multirate Industrial Soft Sensing

In various industrial processes, soft sensors have become important tools for predicting key quality variables. However, traditional soft sensor models only use data from the same sampling moments as the key quality variables, thus wasting information from other sampling moments. In light of this, a novel soft sensor model named triple-gated bidirectional variational pyramid network (G3-BiVPN) is proposed. Within G3-BiVPN, the multirate dataset is first segmented into multiple datasets each with a single sampling rate. For each dataset, a corresponding bidirectional variational autoencoder (BiVAE) is utilized for feature extraction. BiVAEs are used as backbone models to form a bidirectional pyramid structure. A triple gating mechanism consisting of attention gate (AG), temporal gate (TG), and spatial gate (SG) is integrated into BiVAEs to regulate information flow. Information can be flowed bidirectionally through different levels, with each level establishing a regression relationship with the key quality variables and selecting the optimal level as the final output. The core advantage of G3-BiVPN lies in its utilization of the multirate nature of the data. Finally, the efficiency of G3-BiVPN has been validated through two sets of real-world industrial process data with multiple sampling rates.