A multimodal analysis method based on border-peeling clustering for soft sensor

Soft sensors have been widely used in industrial process monitoring. The core of monitoring complicated industrial processes is to recognize multi-modes and strategically apply different sub-model. This paper proposes a new soft sensor-modeling method based on BPC (Border-peeling Clustering) and PLSR (Partial Least Square Regression). Moreover, BPC is robust towards noise and sample unbalanced problems. By iteratively peeling off layers of points, the cores of the latent clusters are revealed, which indicates the different operation modes. The three-phase flow process case proves the effectiveness and superiority of the proposed method. Experimental results of three-phase flow show that the mean square error of the proposed method is 34.1%, which is better than other methods.