Mechanism and data fusion driven multi-indicator soft sensor framework for industrial processes

Soft sensors of quality indicators for industrial production processes compensate for the shortcomings of traditional measurement methods, which are essential for improving product quality. However, the complex mechanisms and time-varying delays in multi-unit, long-flow industrial processes pose significant challenges for multi-indicator soft sensing. Existing research on the fusion of mechanical and data-driven models is not sufficiently advanced. To address these challenges, a mechanism and data fusion driven multi-indicator soft sensor framework for industrial processes is proposed, using the hot strip rolling process (HSRP) as a case study. First, the mechanism of HSRP is analyzed and the unknown parameters in the mechanism model are identified. Second, the data derived from the mechanistic model are fused with the process data. Then Kolmogorov-Arnold Networks with an embedded time-series input layer (TS-KAN) are developed to address the challenge of time-varying delays caused by long production processes and production fluctuations. Finally, the proposed framework is validated using actual HSRP production data, achieving simultaneous accurate prediction of strip flatness and crown.