Development of Soft-Sensor Considering Process Maintenance Using Transfer Learning and Adaptive Modeling

Abstract

In manufacturing processes, various types of soft-sensors have been widely used for predicting important variables, including quality variables, that is difficult to be measured with hardware sensors. Various methods combining transfer learning and adaptive modeling have been proposed for quick soft-sensor adaptation after process maintenance. In conventional methods, it is necessary to switch soft-sensors using transfer learning to soft-sensors without using transfer learning to prevent negative transfer, which may cause performance deterioration of soft-sensors. However, it is difficult to appropriately determine the timing of soft-sensor switching. In this study, we propose a new transfer learning-based adaptive soft-sensor that can avoid the problem of soft-sensor switching. In the proposed method, soft-sensors are constructed using samples measured within the last fixing period as the target domain, and updating them sequentially when transfer learning is adopted. The usefulness of the proposed method was illustrated through its application to real data on the distillation process of a fluorinated telomer intermediate. It was confirmed that the proposed method improved the RMSE by an average of 17% compared with the conventional method.