Transactions of Materials and Heat Treatment

Title：Research on vibration prediction for tandem cold rolling mill based on EEMD-LSTM

Authors： Zhang Ruicheng Cao Zhixin

Unit： School of Electrical Engineering North China University of Science and Technology

KeyWords： tandem cold rolling vibration prediction of rolling mill EEMD decomposition LSTM network vibration acceleration

ClassificationCode：TG335

year,vol(issue):pagenumber：2022,47(9):174-181

Abstract：

For the characteristics of non-linear and non-stationary for vibration of tandem cold rolling mill, and it is closely related to the current and historical states, based on Ensemble Empirical Mode Decomposition (EEMD)-Long and Short Term Memory Recurrent Neural Network (LSTM), a rolling mill vibration prediction model was proposed. Then, the rolling mill vibration acceleration was decomposed into several IMF modal components and residual components with single frequency and relative stability by the EEMD method, and the complexity of vibration acceleration signal was reduced effectively. Furthermore, the prediction model of rolling mill vibration was established by using LSTM network with memory unit, and the prediction accuracy of rolling mill vibration was significantly improved by introducing historical vibration information. The simulation results show that the prediction accuracy of EEMD-LSTM model is 11% higher than that of LSTM model, and it has a good prediction effect on the rolling mill vibration. Meanwhile, the quantitative relationship between each process parameter and rolling mill vibration is analyzed, which provides a reference for quickly suppressing the rolling mill vibration and optimizing the rolling schedule.

Funds：

河北省自然科学基金资助项目（F2018209201）

张瑞成（1975-），男，博士，教授 E-mail：rchzhang@126.com

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