Gaussian–Poisson Mixture Regression model for defects prediction in steelmaking

Abstract

In the steelmaking process, real-time prediction of the occurrence of defects is crucial. To this end, a novel Gaussian–Poisson Mixture Regression (GPMR) model is proposed in this work. GPMR utilizes Poisson and Gaussian distributions to describe input process measurements and the count-type output quality variable, respectively. At the same time, inspired by the idea of finite mixture models, the two distributions are further extended into a mixture form to improve the predictive performance of the model, which also fully considers the characteristics of multi-mode and process variation in the steelmaking process. In addition, a parameter learning strategy based on variational inference is designed for the training of GPMR. The application results of a numerical example and an actual steelmaking process demonstrate the effectiveness and superiority of the proposed GPMR method compared to other traditional methods.