Industrial IoT-Enabled Prediction Interval Estimation of Mechanical Performances for Hot-Rolling Steel
Gongzhuang Peng, Yinliang Cheng, Hongwei Wang, Weiming Shen
Abstract
The prediction of mechanical properties holds the key to ensuring product quality and developing new materials in the steel industry. In this study, an industrial Internet of Things platform is developed to obtain the property-related parameters, and a data-driven approach for estimating the prediction intervals (PIs) of these properties is proposed to address the drawbacks of point prediction methods. By combining an optimized extreme learning machine (ELM) and the delta method, the proposed approach specifically applies the regularization mechanism to improve the generalization and stability of the model; it also uses the artificial bee colony algorithm to optimize the initial weights and bias of the input layer in regularized ELM (RELM). An actual steel coil dataset consisting of 27 input dimensions and 2120 samples was used to validate the proposed method, in comparison with the backpropagation neural network, standard ELM, RELM optimized using the genetic algorithm, and lower upper bound estimation method. Experiment results confirm that the proposed method can obtain PIs with a higher coverage probability, narrower interval width, and smaller calculation error.