Probabilistic Fusion Model for Industrial Soft Sensing Based on Quality-Relevant Feature Clustering

Abstract

For most modern industrial processes with strong nonlinear and multimodal characteristics, the traditional linear PLS-based soft sensor may not work well. Meanwhile, the traditional global modeling approach has a high demand for data representation capability in the face of complex data distribution, which poses a challenge to soft sensing. In addition, the unbalanced nature of data distribution exacerbates the model's neglect of local information to some extent, which enhances the overall prediction difficulty of the model. To this end, based on the PLS, a novel quality-relevant feature clustering (QRFC) model is proposed for the first time in this paper from the view of local modeling of probabilistic fusion. In the QRFC, the PLS can give reasonable and explanatory guidance on the initial feature space for the modeling. Besides, the different data distributions are modeled using a unified perspective through a balanced grouping of the data. Further, a regulation variable is introduced to learn the feature space and complete the output-relevant clustering by iterative approach, to implicitly construct the correlation with the quality variable, forming a local approximation of the overall prediction capability. In general, the benefits of this framework, including better consistency of correlation and stronger abilities to deal with process nonlinearities and multimodality, lead to superior performance. To evaluate the feasibility and efficiency of the developed soft sensor, a real industrial case is taken as a demonstration. The experimental results demonstrate that the proposed method outperforms several other soft sensing approaches.