Russian Federation
Russian Federation
Russian Federation
The article analyzes the prospects for the development of systems for predicting paper breaks on paper machines using machine learning methods. The problem of the imbalance of classes and ensuring the interpretability of models in the conditions of the Russian CBC is solved. A hybrid architecture LSTM + XGBoost using data augmentation methods ADASYN, SMOTE and SHAP analysis is proposed.
predictive control, paper breakage predicting, paper machine, hybrid models, class imbalance, LSTM, XGBoost, machine learning
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