An online transfer kernel recursive algorithm for soft sensor modeling with variable working conditions

Abstract

Soft sensor technology has found widespread application in the real-time detection of challenging variables like product quality and key process parameters. However, changes in working conditions can lead to alterations in data distribution, resulting in model inaccuracies. Furthermore, data from new working conditions arrive sequentially and online. Due to technical difficulties, time-consuming measurements, and high costs, there is often a lack of sufficient new data for modeling in the initial phase. Moreover, industrial processes often exhibit dynamic, nonlinear, variable coupling and other factors, posing significant challenges for model establishment. To address these challenges, this study proposed an online transfer kernel recursive algorithm for soft sensor modeling under changing working conditions. The algorithm consists of an offline phase and an online phase. In the offline phase, the algorithm considers the dynamic features of the process and variable coupling, resulting in the establishment of a dynamic inner model. The original data is then projected onto a latent variable space. In the online phase, the proposed method incorporates the concept of online transfer learning and takes into account the nonlinearity of the process. This allows for the establishment of a kernel online recursive method in the latent variable space, integrating parameter transfer and sample transfer. Experimental results conducted on multiple industrial datasets validate the effectiveness of the proposed approach.