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

Time-frequency analysis is of necessity for wrist pulse signal due to its complexity, among which, empirical mode decomposition (EMD) algorithm and its improved noise-assisted versions (such as ensemble EMD, noise-assisted multivariate EMD (NA-MEMD) and very recently median EMD) are deemed to be the most representative ones. In this study, we provide an in-depth evaluation of these well-established noise-assisted EMD algorithms in computational pulse analysis for the first time. In particular, we compare the performance of the different algorithms systematically and quantitatively based on objective quantitative criteria: number and central frequency of intrinsic mode function (IMF) components, total orthogonality index and mode mixing. Rather than using synthetic signals with visual inspection in most existing literature, the wrist pulse signals used in the evaluation are real recorded samples acquired from both healthy and patient subjects. Through extensive experiments, we found that: 1) Advanced EMD algorithm that has the best performance in other areas may not be the most suitable method for pulse signal analysis, which indicates its high dependence on the type of signal to be analyzed; 2) Adding noise can significantly improve algorithm performance, but tends to produce physiologically not relevant components, which however are usually neglected throughout the intelligent pulse diagnosis literature. Therefore, excluding redundant components and then extracting features is expected to improve performance further. Together, currently NA-MEMD achieves a better performance consistently, potential to become a powerful tool for computational pulse analysis, which in itself have not been applied in wrist pulse analysis before. We believe our works can bring up a new perspective to application of EMD-like algorithms in computational pulse analysis/diagnosis with effective information and guidance. Additionally, considering the similarity between physiological signals, especially such as photoplethysmogram/electrocardiogram, our research can be extended to wearable health monitoring technologies, including smart watches and fitness trackers, and their potential future applications, such as in heart rate estimation and evaluate various cardiovascular-related diseases. The present study underscored the necessity of evaluation noise-assisted EMDs or other adaptive decomposition algorithms based on real recorded signals with more objective measures. Especially, caution the possible redundant components that are introduced.

Financial time series forecasting has become a challenge because it is noisy, non-stationary and chaotic. To overcome this limitation, this paper uses empirical mode decomposition (EMD) to aid the financial time series forecasting and proposes an approach via combining ARIMA and SVR (Support Vector Regression) to forecast. The approach contains four steps: (1) using ARIMA to analyze the linear part of the original time series; (2) EMD is used to decompose the dynamics of the non-linear part into several intrinsic mode function (IMF) components and one residual component; (3) developing a SVR model using the above IMFs and residual components as inputs to model the nonlinear part; (4) combining the forecasting results of linear model and nonlinear model. To verify the effectiveness of the proposed approach, four stock indices are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results.

Using filter bank property to simplify the calculations of Empirical Mode Decomposition

An accurate electricity load prediction is important to optimizing building electricity load performance. However, building electricity load prediction is complex due to many influencing factors. This study develops a hybrid algorithm that combines clustering approach, empirical mode decomposition, and support vector regression to develop a prediction model for building electricity load. k-shape clustering is used to extract similar building electricity load pattern, and empirical mode decomposition is employed to decompose electricity load data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of electricity load. Numerical testing demonstrated that the proposed method can accurately predict the electricity load in the building.

A braking intention identification method based on empirical mode decomposition (EMD) algorithm and entropy theory for electric vehicles is proposed. EMD algorithm is given to decompose nonstationary brake pedal signal to stationary intrinsic mode function (IMF), which is the base of data mining. After that, entropy theory is used to extract brake pedal signal features. A braking intention identification model is built based on fuzzy c-means clustering algorithm. The hardware and software for braking intention identification system based on this method is set up to do offline and real-time experiments. The results show that the identification method proposed in this paper has good real-time quality and can distinguish moderate braking intention and gentle braking intention better.

Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes. The method is fully adaptive and generates a complete set of orthogonal basis functions, called Intrinsic Mode Functions (IMFs), in a purely data-driven manner. Amplitude and frequency of IMFs may vary over time which renders them different from conventional basis systems and ideally suited to study non-linear and non-stationary time series. However, biomedical time series are often recorded over long time periods. This generates the need for efficient EMD algorithms which can analyze the data in real time. No such algorithms yet exist which are robust, efficient and easy to implement. The contribution shortly reviews the technique of EMD and related algorithms and develops an on-line variant, called slidingEMD, which is shown to perform well on large scale biomedical time series recorded during neuromonitoring.

In the present study, an adaptive Fourier Decomposition Method (FDM) is applied to identify damage at connections. For that purpose, an experimental single-story plane frame model is considered. The beam and columns of the frame are connected by angles and bolts, and the damage is introduced by loosening bolts at different locations. The frame is excited at the right top corner using a hammer, and strain time histories are collected from different points. The FDM and Empirical Mode Decomposition (EMD) algorithm are applied to characterize the damage sensitive features. The results obtained from the FDM are compared with the EMD algorithm, which shows that the performance of FDM is better, which motivates to extend its application further to estimate the amount of bolt loosening in terms of extracted damage sensitive features.

A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

This paper proposed a method for roller bearing fault diagnosis using Empirical Mode Decomposition (EMD) algorithm and decision tree. First, to obtain the Intrinsic Mode Functions (IMFs) of bearing vibration signal processed by EMD and processing the IMFs with autocorrelation for noise elimination, then extract the principal frequency as features in frequency domain. Second, build up decision tree with C4.5 algorithm and forecast samples. Feedback the node attributes information to EMD for reducing the calculation of needless attribute and optimizing signal processing algorithm. The experimental bearing vibration signal in accordance with the following conditions: normal, inner race fault, outer race fault and balls fault. The experiment result illustrated that the correct ratio of diagnosis is high and efficient. The method proposed is high accurate and useful for safe production.

With the problem of magnetic anomaly signal in low SNR, we using empirical mode decomposition (EMD) algorithm and Savitzky-Golay filter to solve weak magnetic anomaly signal in noisy environment detection problem. With the EMD, the weak magnetic anomaly signal can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs). The IMFs include signals different frequency component from high to low. Recover the signal with IMFs which correlation coefficient is low with noise signal and smooth the signal with Savitzky-Golay filter. The proposed method is verified by simulation on computer. The present experimental results show that this method is effective to detect the weak magnetic anomaly and recover the original signal.

Speech enhancement is performed in a wide and varied range of instruments and systems. In this paper, a novel approach to Speech Enhancement using Adaptive Empirical Mode Decomposition (SEAEMD) is presented. Spectral analysis of non-stationary signals can be performed by employing techniques such as the STFT and the Wavelet transform (WT), which use predefined basis functions. Empirical Mode Decomposition (EMD) performs very well in such environments. EMD decomposes a signal into a finite number of data-adaptive basis functions, called Intrinsic Mode Functions (IMFs). The new SEAEMD system incorporates this multi-resolution approach with adaptive noise cancellation (ANC) for effective speech enhancement on an IMF level, in stationary and non-stationary noise environments. A comparative performance study is included that compares the competitive method of conventional ANC to the robust SEAEMD system. The results demonstrate that the new system achieves significantly improved speech quality with a lower level of residual noise.

Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

This paper presents a novel method of detecting and classifying the power system faults of voltage sags based on Empirical Mode Decomposition (EMD). A technique employed for analyzing power system fault data in terms of voltage sags is required. Also, provides information about the underlying event i.e. the fault type. EMD is to method which decomposes a non stationary signal into mono component and symmetric component signals called Intrinsic Mode Functions (IMFs). Further the Hilbert Transform (HT) of IMF provides magnitude and phase angle information. The characteristic features of the first three IMFs of each phase are used as inputs to the classifier Probabilistic Neural Network (PNN) for identification of fault type. Four types of shunt faults are taken for classification. A comparison is also made with wavelet Transform (WT). Simulation results show that the classification accuracy is better for EMD, which proves that the method is efficient in classifying the faults.

The Fourier transform (FT) has been the most popular method for analyzing large rotating machine shaft vibration problems, but it assumes that these vibration signals are linear and stationary. However, in reality this is not always true. Nonlinear and non-stationary shaft vibration signals are often encountered during the start-up and shut-down processes of the machines. Additionally, mechanical faults, for example rotor-to-stator rubbing, fluid excitation, part-loosening, and shaft cracking, are nonlinear. Owing to these reasons, an accurate analysis of shaft vibration cannot always be achieved by using the FT. An alternative tool, the wavelet transform (WT), is now being used to improve the situation. But the efficiency is a problem especially when applying the WT to the accurate analysis of a large-scale, lengthy data. In view of the powerful capability of empirical mode decomposition (EMD) to process nonlinear/non-stationary signals, its algorithm efficiency and its satisfactory performance in minimizing energy leakage, the EMD is used in this paper to analyze the problem, the signals investigated are adaptively decomposed into a finite number of intrinsic mode functions (IMFs). The principal IMFs, identified using an energy-distribution threshold, dominate the signals’ oscillation. So, ‘purified’ shaft vibration signals can be reconstructed from these principal IMFs. To remove interference present in principal IMFs, an adaptive band-pass filter is designed, whose central frequency is automatically set to the frequency dominating the IMF being investigated. To facilitate the observation of transient shaft vibration, a transient shaft orbit (TSO) is constructed by introducing timescale into the orbit drawing process. Nine mathematical criteria are also proposed to evaluate the shaft vibrations exhibited in the IMFs and TSOs. The novelty of this approach is that the EMD provides an adaptive, effective, and efficient way to obtain ‘purified’ shaft vibration signals, which describe the transient shaft vibration more vividly and precisely, reducing misinterpretation of the machine running condition. The approach is validated by two practical examples: • Part-loosening incident on a large centrifugal compressor. • Fluid excitation incident on a centrifugal compressor. Calculations of the mathematical criteria for seven different running conditions of these large rotating machines are also presented. It is demonstrated that the proposed technique provides a feasible and reliable way to interpret shaft vibration, by which means the machine condition can be diagnosed correctly.

In this paper, we propose a novel ECG signal enhancement method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Higher Order Statistics (HOS). In our scheme, the noisy ECG signal is first decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) by using Empirical Mode Decomposition (EMD) or its variants. Therefore, the obtained modes are separated into two groups of noisy signal modes and one group of useful signal modes, by using a novel criterion derived from the HOS namely the fourth order cumulant or kurtosis. After that, a modified shrinkage scheme based on Interval Thresholding technique is adaptively applied to each selected IMF from the noise-dominant groups in order to reduce the noise and to preserve the QRS complex. The overall filtered ECG signal is then reconstructed by combining the thresholded IMFs and the retained unprocessed lower frequency relevant IMFs. Various tests and simulations are investigated to evaluate the performance of our proposed approach in combination with the EMD, Ensemble EMD (EEMD) and CEEMDAN algorithms. The simulation results carried on MIT-BIH Arrhythmia database, show that CEEMDAN method gives better performance than the two other methods, and outperforms some state-of-the-art methods in terms of Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE).