Indirect core-driven cross-channel collaborative fusion soft sensor modeling

Abstract Soft sensor modeling plays a crucial role in industrial processes by enabling real-time prediction of product quality through the use of process variables that are difficult to measure directly. However, traditional soft sensor models often struggle to effectively capture the complex temporal dynamics and deep interdependencies among variables in high-dimensional process data.This paper proposes a novel Indirect Core Allocation Model (ICAM) to address this issue. ICAM efficiently fuses and processes multivariable process data through core aggregation and allocation. The model leverages an indirect attention mechanism to integrate information from various channels with linear complexity, and dynamically selects key process variables through random pooling. This process generates a global indirect core representation, which is then efficiently allocated to the embedding sequences of original samples, facilitating the sharing and collaborative optimization of process variable information. Specifically, ICAM balances the exploration and exploitation of information by dynamically selecting key variables during the training phase. During the testing phase, a probability-based weighted averaging method is employed to ensure the stability and consistency of the prediction results. To verify the effectiveness of ICAM, we conducted modeling experiments on penicillin fermentation, hot rolling, and injection molding industrial process datasets with high data dimensionality. The experimental results demonstrate that ICAM outperforms other advanced models in terms of prediction accuracy and shows superior performance in industrial process quality prediction. Our data and code will be published at https://github.com/pisa-lut/ICAM .