An Improved JITL Method for Soft Sensing of Multimodal Industrial Processes for Search Efficiency

Abstract

In the industrial process, due to product change, working condition switch, or controller adjustment, process data often presents multimodal characteristics. Data-driven approaches are often based on single-modal assumptions, which may fail to describe process characteristics. The traditional just-in-time learning (JITL) method can continuously update the model to describe the multimodal data, but it takes much time and cannot meet the real-time requirements. In this paper, an improved JITL method is proposed to find similar samples quickly. The new samples are divided into the main category first, and then find the similar samples to improve the search efficiency. The effectiveness of the method is proved by a case of an industrial soft sensor case combined with partial least squares (PLS). Compared with the basic JITL, the root mean square error (RMSE) of the proposed method is reduced by 0.09, and the running speed is increased by 8.8 times.