Mold-Level Prediction for Continuous Casting Using VMD–SVR

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.

Download Full-text

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

Processes ◽

10.3390/pr7030177 ◽

2019 ◽

Vol 7 (3) ◽

pp. 177 ◽

Cited By ~ 4

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.

Download Full-text

Classification of sleep apnea using EMD-based features and PSO-trained neural networks

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2021-0025 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Sajjad Afrakhteh ◽

Ahmad Ayatollahi ◽

Fatemeh Soltani

Keyword(s):

Neural Networks ◽

Sleep Apnea ◽

Back Propagation ◽

Pso Algorithm ◽

Bp Algorithm ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Linear Discriminant ◽

Mode Decomposition ◽

Obstructive Sleep

Abstract In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN’s performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN’s accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

Download Full-text

Research on gearbox impact feature extraction method based on improved ESMD

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2022.64.1.20 ◽

2022 ◽

Vol 64 (1) ◽

pp. 20-27

Author(s):

Fengfeng Bie ◽

Sheng Gu ◽

Yue Guo ◽

Gang Yang ◽

Jian Peng

Keyword(s):

Feature Vector ◽

Vibration Signal ◽

Permutation Entropy ◽

Significant Feature ◽

Support Vector ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Envelope Spectrum ◽

Impact Characteristics ◽

The Impact

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

Download Full-text

District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Energies ◽

10.3390/en12112122 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2122 ◽

Cited By ~ 6

Author(s):

Guixiang Xue ◽

Yu Pan ◽

Tao Lin ◽

Jiancai Song ◽

Chengying Qi ◽

...

Keyword(s):

Prediction Accuracy ◽

Feature Fusion ◽

District Heating ◽

Accurate Prediction ◽

Prediction Algorithm ◽

Load Prediction ◽

Short Term ◽

District Heating System ◽

Heating Load ◽

The Impact

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

Download Full-text

Internal leakage identification of hydraulic cylinder based on intrinsic mode functions with random forest

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406219846148 ◽

2019 ◽

Vol 233 (15) ◽

pp. 5532-5544 ◽

Cited By ~ 1

Author(s):

Lin Li ◽

Yixiang Huang ◽

Jianfeng Tao ◽

Chengliang Liu

Keyword(s):

Random Forest ◽

Hydraulic Cylinder ◽

Support Vector ◽

Hydraulic Systems ◽

Intrinsic Mode Functions ◽

Random Forest Algorithm ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method ◽

Hydraulic Cylinders ◽

Experimental Pressure

Monitoring for internal leakage of hydraulic cylinders is vital to maintain the efficiency and safety of hydraulic systems. An intelligent classifier is proposed to automatically evaluate internal leakage levels based on the newly extracted features and random forest algorithm. The inlet and outlet pressures as well as the pressure differences of two chambers are chosen as the monitoring parameters for leakage identification. The empirical mode decomposition method is used to decompose the raw pressure signals into a series of intrinsic mode functions to obtain the essence in experimental signals. Then, the features extracted from intrinsic mode functions in terms of statistical analysis are formed the input vector to train the leakage detector. The classifier based on random forest is established to categorize internal leakage into proper levels. The accuracy of the internal leakage evaluator is verified by the experimental pressure signals. Moreover, an internal leakage evaluator is established based on the support vector machine algorithm, in which the wavelet transform is applied for feature extraction. The accuracy and efficiency of different classifiers are compared based on leakage experiments. The results show that the classifier trained by the intrinsic mode function features in terms of random forest algorithm may more effectively and accurately identify internal leakage levels of hydraulic cylinders. The leakage evaluator provides probability for online monitoring of the internal leakage of hydraulic cylinders based on the inherent sensors.

Download Full-text

Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

Shock and Vibration ◽

10.1155/2016/3891429 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 14

Author(s):

Liye Zhao ◽

Wei Yu ◽

Ruqiang Yan

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Correlation Coefficients ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Support Vector ◽

Svm Classifier ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

Download Full-text

Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

International Journal of Epilepsy ◽

10.1055/s-0039-1693072 ◽

2018 ◽

Vol 05 (02) ◽

pp. 092-098

Author(s):

Pushpa Balakrishnan ◽

S. Hemalatha ◽

Dinesh Nayak Shroff Keshav

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Brain Activity ◽

Epileptic Seizures ◽

Machine Learning Algorithms ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Absolute Deviation ◽

Mode Decomposition ◽

Life Threatening

Abstract Background Epilepsy is a common neurological disorder characterized by seizures and can lead to life-threatening consequences. The electroencephalogram (EEG) is a diagnostic test used to analyze brain activity in various neurological conditions including epilepsy and interpreted by the clinician for appropriate diagnosis. However, the process of EEG analysis for diagnosis can be automated using machine learning algorithms (MLAs) to aid the clinician. The objective of the study was to test different algorithms that could be used for the detection of seizures. Materials and Methods Video EEG (vEEG) was collected from subjects diagnosed to have episodes of seizures. The epilepsy dataset thus obtained was subjected to empirical mode decomposition (EMD) and the signal was decomposed into intrinsic mode functions (IMFs). The first five levels of decomposition were considered for analysis as per the established protocol. Statistical features such as interquartile range (IQR), entropy, and mean absolute deviation (MAD) were extracted from these IMFs. Results In this study, different MLAs such as nearest neighbor (NN), naïve Bayes (NB), and support vector machines (SVMs) were used to distinguish between normal (interictal) and abnormal (ictal) states. The demonstrated accuracy rates were 97.32% for NN, 99.02% for NB, and 93.75% for SVM. Conclusion Based on this accuracy and sensitivity, it may be posited that the NB classifier provides significantly better results for the detection of abnormal signals indicating that MLA can detect the seizure with better accuracy.

Download Full-text

Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

Applied Sciences ◽

10.3390/app9010180 ◽

2019 ◽

Vol 9 (1) ◽

pp. 180 ◽

Cited By ~ 4

Author(s):

Weifang Zhang ◽

Meng Zhang ◽

Yan Zhao ◽

Bo Jin ◽

Wei Dai

Keyword(s):

Discrete Wavelet ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Denoising Method ◽

Mode Decomposition ◽

Noisy Signals ◽

Band Limited ◽

Adaptive Signal Decomposition ◽

Adaptive Signal ◽

The Impact

Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of K band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underbinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number K and data-fidelity constraint parameter α. After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.

Download Full-text

Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods

Water ◽

10.3390/w11061226 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1226 ◽

Cited By ~ 2

Author(s):

Mohammed Falah Allawi ◽

Faridah Binti Othman ◽

Haitham Abdulmohsin Afan ◽

Ali Najah Ahmed ◽

Md. Shabbir Hossain ◽

...

Keyword(s):

Artificial Intelligence ◽

Prediction Accuracy ◽

Evaporation Rate ◽

Climatic Conditions ◽

Support Vector ◽

Time Increment ◽

Accuracy Level ◽

Increment Rate ◽

Input Variables ◽

The Impact

The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered.

Download Full-text

Preterm-Term Birth Classification Using EMD-Based Time-Domain Features of Single-Channel Electrohysterogram Data

10.21203/rs.3.rs-570938/v1 ◽

2021 ◽

Author(s):

Suparerk Janjarasjitt

Keyword(s):

Preterm Birth ◽

Time Domain ◽

Single Channel ◽

Support Vector ◽

Computational Results ◽

Intrinsic Mode Functions ◽

Term Birth ◽

Emg Signal ◽

Mode Decomposition ◽

The Time Domain

Abstract The preterm birth anticipation is a crucial task that can reduce the rate of preterm birth and also the complications of preterm birth. Electrohysterogram (EHG) or uterine electromyogram (EMG) data have been evidenced that they can provide an information useful for preterm birth anticipation. Four distinct time-domain features, i.e., mean absolute value, average amplitude change, difference absolute standard deviation value, and log detector, commonly applied to EMG signal processing are applied and investigated in this study. A single-channel of EHG data is decomposed into its constituent components, i.e., intrinsic mode functions, using empirical mode decomposition (EMD) before their time-domain features are extracted. The time-domain features of intrinsic mode functions of EHG data associated with preterm and term births are applied for preterm-term birth classification using support vector machine (SVM) with a radial basis function. The preterm-term classifications are validated using 10-fold cross validation. From the computational results, it is shown that the excellent preterm-term birth classification can be achieved using a single-channel of EHG data. The computational results further suggest that the best overall performance on preterm-term birth classification is obtained when thirteen (out of sixteen) EMD-based time-domain features are applied. The best accuracy, sensitivity, specificity, and F1-score achieved are, respectively, 0.9382, 0.9130, 0.9634, and 0.9366.

Download Full-text