The continuous casting process is a continuous, complex phase transition process. The noise components of the continuous casting process are complex, the model is difficult to establish, and it is difficult to separate the noise and clear signals effectively. Owing to these demerits, a hybrid algorithm combining Variational Mode Decomposition (VMD) and Wavelet Threshold denoising (WTD) is proposed, which involves multiscale resolution and adaptive features. First of all, the original signal is decomposed into several Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD), and the model parameter K of the VMD is obtained by analyzing the EMD results. Then, the original signal is decomposed by VMD based on the number of IMFs K, and the Mutual Information Entropy (MIE) between IMFs is calculated to identify the noise dominant component and the information dominant component. Next, the noise dominant component is denoised by WTD. Finally, the denoised noise dominant component and all information dominant components are reconstructed to obtain the denoised signal. In this paper, a comprehensive comparative analysis of EMD, Ensemble Empirical Mode Decomposition (EEMD), Complementary Empirical Mode Decomposition (CEEMD), EMD-WTD, Empirical Wavelet Transform (EWT), WTD, VMD, and VMD-WTD is carried out, and the denoising performance of the various methods is evaluated from four perspectives. The experimental results show that the hybrid algorithm proposed in this paper has a better denoising effect than traditional methods and can effectively separate noise and clear signals. The proposed denoising algorithm is shown to be able to effectively recognize different cast speeds.

The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the experimental results show that the proposed hybrid method based on EMD and SVR is a powerful tool for solving complex time series prediction. In view of the excellent generalization ability of the EMD, it is believed that the hybrid algorithm of EMD and SVR is the best model for mold level predict among the six methods, providing a new idea for guiding continuous casting process improvement.

In the continuous-casting process, mold-level control is one of the most important factors that ensures the quality of high-efficiency continuous casting slabs. In traditional mold-level prediction control, the mold-level prediction accuracy is low, and the calculation cost is high. In order to improve the prediction accuracy for mold-level prediction, an adaptive hybrid prediction algorithm is proposed. This new algorithm is the combination of empirical mode decomposition (EMD), variational mode decomposition (VMD), and support vector regression (SVR), and it effectively overcomes the impact of noise on the original signal. Firstly, the intrinsic mode functions (IMFs) of the mold-level signal are obtained by the adaptive EMD, and the key parameter of the VMD is obtained by the correlation analysis between the IMFs. VMD is performed based on the key parameter to obtain several IMFs, and the noise IMFs are denoised by wavelet threshold denoising (WTD). Then, SVR is used to predict each denoised component to obtain the predicted IMF. Finally, the predicted mold-level signal is reconstructed by the predicted IMFs. In addition, compared with WTD–SVR and EMD–SVR, VMD–SVR has a competitive advantage against the above three methods in terms of robustness. This new method provides a new idea for mold-level prediction.

Purpose This paper aims to revel the leakage characteristics of the bent-axis piston pump considering elastohydrodynamic deformation via a dynamic leakage model. Design/methodology/approach A dynamic leakage model of bent-axis piston pump based on elastohydrodynamic lubrication theory is proposed, which is used to present the leakage characteristics of bent-axis piston pump. The model is composed of three parts. First, the dynamic gap in the piston ring-cylinder bore interface (PRCB) is described via the elastohydrodynamic lubrication equations. Then, the PRCB leakage is presented based on the dynamic gap. Finally, combined with leakage equation of the valve plate-cylinder block interface (VPCB), the total leakage model is proposed. Through the numerical simulation and experiment, the leakage characteristics of bent-axis piston pump considering elasto-hydrodynamic deformation are studied. Findings The PRCB leakage is negatively correlated with VPCB leakage under the range of 800–1400 r/min and 1–25 MPa. When the discharge pressure is less than the critical pressure, the PRCB leakage is the main factor affecting the total leakage in bent-axis piston pump. On the contrary, the VPCB leakage is the main factor. The critical pressure increases with increasing speed Originality/value The effect of operating parameters has a significant effect on the elastic deformation of piston ring without considering wear of friction pairs in bent-axis piston pump. There is a critical phenomenon in the leakage, which is related to the operating parameters, and provides a novel idea for extracting wear information from leakage and evaluating the status of bent-piston pump.

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.