Stacked supervised Poisson autoencoders-based soft-sensor for defects prediction in steelmaking process

Abstract

Soft-sensors are effective tools for predicting quality variables in many industries. In this work, a novel data-driven soft-sensor called stacked supervised Poisson autoencoders (SSPAE) is proposed to predict the number of defects in the steelmaking process. SSPAE is a deep learning-based soft-sensing model designed by integrating Poisson regression network layers into the deep autoencoders framework. In SSPAE, quality-related deep features can be progressively learned from data through the deep network architectures. During the feature extraction process, SSPAE takes the quality information into account, so that the extracted deep features are conducive to improving the accuracy of the prediction model. Additionally, due to the introduction of the Poisson regression network, SSPAE is more suitable for predicting the count-type quality variables. The proposed method is evaluated through a numerical example and real-world industrial data. The results demonstrated that SSPAE is superior to PLS, SVR, PR, SAE-FCL, and SAE-PR in prediction accuracy.