Mold-level prediction based on long short-term memory model and multi-mode decomposition with mutual information entropy

Wenbin Su,Zhufeng Lei

doi:10.1177/1687814019894433

Wenbin Su, Zhufeng Lei

Open Access

https://doi.org/10.1177/1687814019894433

Copy DOI

Abstract

The mold is referred to as the heart of the continuous casting machine. Mold-level control is one of the keys to ensuring the quality of a high-efficiency continuous casting slab. This article addresses the failure of the mold-level prediction model in the actual production process to overcome the impact of noise. To improve the accuracy of mold-level prediction, a novel method for mold-level prediction based on the multi-mode decomposition method and the long short-term memory model is proposed. First, empirical mode decomposition of the mold-level data is performed. The actual eigenmode component number K is obtained through the calculation of the mutual information entropy of the eigenmode components. Then, we perform a K-based variational mode decomposition on the mold-level data. The noise dominant component is denoised by the calculation of the mutual information entropy of the eigenmode components. Moreover, the long short-term memory model is used to predict the noise dominant component and the information dominant component after denoising. Finally, the predicted result is subjected to variational mode decomposition reconstruction to obtain the predicted mold-level data. The experimental results show that compared with the other methods tested, the model has better prediction efficiency, prediction accuracy, and generalization ability. It provides a new idea for mold-liquid-level prediction and continuous casting blank quality assurance.

Highlights

Mold-level control is the basis for stable production operations, avoiding steel leakage and steel spillage
By selecting K = 9 as the mode component number for variational mode decomposition (VMD) decomposition, we were able to clearly separate the original signals and avoid modal aliasing. It can be seen from the spectrum diagram that the IMF3–IMF7 frequency bandwidth is relatively long and the noise is serious, so wavelet threshold denoising (WTD) was performed on these intrinsic mode functions (IMFs)
VMD-Long short-term memory (LSTM)’s root mean square error is the smallest, which shows that the model has stronger robustness; its mean absolute error (MAE) is the largest, reflecting that VMD-LSTM prediction is the most accurate; mean absolute percentage error (MAPE) considers the error between the predicted value and the real value, and the ratio between the error and the real value, which shows that the VMDLSTM model has the highest prediction accuracy

Summary

Introduction

The mold is referred to as the heart of the continuous casting machine. Mold-level control is the basis for stable production operations, avoiding steel leakage and steel spillage. MMD: multi-mode decomposition; LSTM: long short-term memory; IMF: intrinsic mode function; EMD: empirical mode decomposition; MIE: mutual information entropy; VMD: variational mode decomposition; WTD: wavelet threshold denoising. By selecting K = 9 as the mode component number for VMD decomposition, we were able to clearly separate the original signals and avoid modal aliasing It can be seen from the spectrum diagram that the IMF3–IMF7 frequency bandwidth is relatively long and the noise is serious, so WTD was performed on these IMFs. the first five IMFs’ center frequencies were significantly reduced and the amplitudes were significantly reduced. EMD-SVR: empirical mode decomposition–support vector machine; EEMD-SVR: ensemble empirical mode decomposition–support vector machine; EWT-SVR: empirical wavelet transform–support vector machine; VMD-SVR: variational mode decomposition–support vector machine

Method

Findings

Conclusion