HOA-KELM: An Intelligent Diagnosis Method for Hot-Rolled Strip Manufacturing in the Industrial Internet of Things

In the production process of hot rolled strip steel plate, the Industrial Internet of Things (IIoT) improves the quality and efficiency of production and manufacturing products, but also because of the large amount of data generated by its equipment, resulting in a significant decline in plate crown fault diagnosis and decision-making ability. To improve the yield and quality of hot strip steel plate, in this paper, an intelligent algorithm based on the Hippopotamus optimization algorithm-Kernel Extreme Learning Machine (HOA-KELM) is proposed. Adaptive synthetic sampling technology (ADASYN) resampling technique is used to deal with multi-class unbalance of data. At the same time, InterpretML and SHapley additive stripping (SHAP) methods based on game theory were introduced to analyze the crown diagnosis model of hot strip, and the influencing factors of the strip in hot rolling were studied. To verify the applicability of the model, the model was tested on the hot rolling production data set and UCI data set. In the hot rolling data set, the model performance Kappa Coefficient, F1 Score, and Accuracy were 0.988, 0.993, and 0.992, respectively, indicating a good effect. It is compared with the common mathematical model. The findings indicate that this model significantly outperforms the conventional approach in solving the problem of strip convexity diagnosis in the hot rolling process, and can be proposed as an effective mathematical model for plate convexity diagnosis to realize the intelligent management of industrial production.