scholarly journals Mold Level Predict of Continuous Casting Using Hybrid EMD-SVR-GA Algorithm

Processes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 177 ◽  
Author(s):  
Zhufeng Lei ◽  
Wenbin Su
Keyword(s):  
Continuous Casting ◽  
Production Control ◽  
Moving Average ◽  
Time Series Prediction ◽  
Casting Process ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Autoregressive Moving Average Model ◽  
Mode Decomposition ◽  
Moving Average Model

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.

Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods

2018 ◽  
Vol 18 (2) ◽  
pp. 347-375 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Autoregressive Moving Average Model ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Moving Average Model ◽  
Adaptive Noise

Ambient excitations applied to structures may lead to non-stationary vibration responses. In such circumstances, it may be difficult or improper to extract meaningful and significant damage features through methods that mainly rely on the stationarity of data. This article proposes a new hybrid algorithm for feature extraction as a combination of a new adaptive signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise and autoregressive moving average model. The major contribution of this algorithm is to address the important issue of feature extraction under ambient vibration and non-stationary signals. The improved complete ensemble empirical mode decomposition with adaptive noise method is an improvement on the well-known ensemble empirical mode decomposition technique by removing redundant intrinsic mode functions. In addition, a novel automatic approach is presented to select the most relevant intrinsic mode functions to damage based on the intrinsic mode function energy level. Fitting an autoregressive moving average model to each selected intrinsic mode function, the model residuals are extracted as the damage-sensitive features. The main limitation is that such features are high-dimensional multivariate time series data, which may make a difficult and time-consuming decision-making process for damage localization. Multivariate distance correlation methods are introduced to cope with this drawback and locate structural damage using the multivariate residual sets of the normal and damaged conditions. The accuracy and robustness of the proposed methods are validated by a numerical shear-building model and an experimental benchmark structure. The effects of sampling frequency and time duration are evaluated as well. Results demonstrate the effectiveness and capability of the proposed methods to extract sufficient and reliable features, identify damage location, and quantify damage severity under ambient excitations and non-stationary signals.

scholarly journals Mold-Level Prediction for Continuous Casting Using VMD–SVR

Metals ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 458 ◽  
Author(s):  
Wenbin Su ◽  
Zhufeng Lei ◽  
Ladao Yang ◽  
Qiao Hu
Keyword(s):  
Continuous Casting ◽  
Prediction Accuracy ◽  
High Efficiency ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Level Control ◽  
Mode Decomposition ◽  
The Impact ◽  
Key Parameter

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.

scholarly journals Medium- and long-term runoff forecasting based on a random forest regression model

2020 ◽  
Vol 20 (8) ◽  
pp. 3658-3664
Author(s):  
Chen Shijun ◽  
Wei Qin ◽  
Zhu Yanmei ◽  
Ma Guangwen ◽  
Han Xiaoyan ◽  
...  
Keyword(s):  
Random Forest ◽  
Power Plants ◽  
Moving Average ◽  
Support Vector ◽  
Random Forest Regression ◽  
Autoregressive Moving Average Model ◽  
Runoff Forecasting ◽  
Svm Model ◽  
Moving Average Model

Abstract Medium- and long-term runoff forecasting is closely related to the generation capacity forecasting of cascade hydropower stations, which is of great significance to power plants when arranging production plans and assisting market decisions. In order to improve the accuracy of runoff forecasting, an attempt was made to use random forest regression (RFR) to model the medium- and long-term runoff forecasting and to further make a verification based on the actual monthly runoff data of Mupo and Chuntangba stations. By comparison with the forecast results attained through a support vector machine (SVM) and an integrated autoregressive moving average model (IARMA), the results showed that the RFR model had the lowest mean square error (MSE) among the three methods. In addition, the coefficients of determination R2 of the RFR for the two stations increased by 0.0261 and 0.0295 compared with the SVM model, and the R2 rose by 0.1134 and 0.1332 compared with the IARMA model. The comparison of the three methods showed that the RFR had higher forecasting accuracy as well as stronger reliability and practicability than the IARMA model and the SVM model, so the RFR provided a new idea and method for the study of runoff forecasting.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Sign in / Sign up

Export Citation Format

Share Document