Order Intrinsic Mode Functions Research Articles

Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM

With the large-scale development of electric vehicles, the accuracy of electric vehicle charging load prediction is increasingly important for electric power system. Accurate EV charging load prediction is essential for the efficiency of electric system planning and economic operation of electric system. This paper proposes an electric vehicle charging load predicting method based on variational mode decomposition and Prophet-LSTM. Firstly, the variational mode decomposition algorithm is used to decompose the charging load into several intrinsic mode functions in order to explore the characteristics of EV charging load data. Secondly, in order to make full use of the advantages of various forecasting methods, the intrinsic mode functions are classified into low and high frequency sequences based on their over-zero rates. The high and low frequency sequences are reconstructed to obtain two frequency sequences. Then the LSTM neural network and Prophet model are used to predict the high and low frequency sequences, respectively. Finally, the prediction results obtained from the prediction of high frequency and low frequency sequences are combined to obtain the final prediction result. The assessment of the prediction results shows that the prediction accuracy of the prediction method proposed in this paper is improved compared to the traditional prediction methods, and the average absolute error is lower than that of ARIMA, LSTM and Prophet respectively by 7.57%, 8.73%, and 46.02%. The results show that the prediction method proposed in this paper has higher prediction accuracy than the traditional methods, and is effective in predicting EV charging load.

Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering

The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method.

Analysis of Pressure Fluctuation Characteristics of Central Swirl Combustors Based on Empirical Mode Decomposition.

In order to study the characteristics of pressure fluctuation during unstable combustion, experimental studies had been conducted on the mechanism model of the swirl combustor and the industrial swirl combustor. The signal of dynamic pressure, heat release rate, and the high-speed flame image in the two combustors were synchronously collected by using dynamic pressure sensors, a photoelectric sensor, and a high-speed camera under normal temperature and pressure. After empirical mode decomposition of the dynamic pressure signal, several intrinsic mode functions were obtained. It was found that the pressure pulsation energy is concentrated in the first three order intrinsic mode function. Through fast Fourier transform spectrum calculation, it was found that the first three order intrinsic mode function pulsation can characterize the changes of heat release rate and air flow pulsation under cold state and flame instability. It showed that the decomposition of the dynamic pressure in the combustor by this method can obtain the main physical processes in its connotation, and provide data processing methods for the induction mechanism of oscillating combustion and combustion diagnosis in an industrial combustor test.

Obtaining Information about Operation of Centrifugal Compressor from Pressure by Combining EEMD and IMFE.

Based on entropy characteristics, some complex nonlinear dynamics of the dynamic pressure at the outlet of a centrifugal compressor are analyzed, as the centrifugal compressor operates in a stable and unstable state. First, the 800-kW centrifugal compressor is tested to gather the time sequence of dynamic pressure at the outlet by controlling the opening of the anti-surge valve at the outlet, and both the stable and unstable states are tested. Then, multi-scale fuzzy entropy and an improved method are introduced to analyze the gathered time sequence of dynamic pressure. Furthermore, the decomposed signals of dynamic pressure are obtained using ensemble empirical mode decomposition (EEMD), and are decomposed into six intrinsic mode functions and one residual signal, and the intrinsic mode functions with large correlation coefficients in the frequency domain are used to calculate the improved multi-scale fuzzy entropy (IMFE). Finally, the statistical reliability of the method is studied by modifying the original data. After analysis of the relationships between the dynamic pressure and entropy characteristics, some important intrinsic dynamics are captured. The entropy becomes the largest in the stable state, but decreases rapidly with the deepening of the unstable state, and it becomes the smallest in the surge. Compared with multi-scale fuzzy entropy, the curve of the improved method is smoother and could show the change of entropy exactly under different scale factors. For the decomposed signals, the unstable state is captured clearly for higher order intrinsic mode functions and residual signals, while the unstable state is not apparent for lower order intrinsic mode functions. In conclusion, it can be observed that the proposed method can be used to accurately identify the unstable states of a centrifugal compressor in real-time fault diagnosis.

Deep Learning Approach to Cardiovascular Disease Classification Employing Modified ECG Signal from Empirical Mode Decomposition

Multiple cardiovascular disease classification from Electrocardiogram (ECG) signal is necessary for efficient and fast remedial treatment of the patient. This paper presents a method to classify multiple heart diseases using one dimensional deep convolutional neural network (CNN) where a modified ECG signal is given as an input signal to the network. Each ECG signal is first decomposed through Empirical Mode Decomposition (EMD) and higher order Intrinsic Mode Functions (IMFs) are combined to form a modified ECG signal. It is believed that the use of EMD would provide a broader range of information and can provide denoising performance. This processed signal is fed into the CNN architecture that classifies the record according to cardiovascular diseases using softmax regressor at the end of the network. It is observed that the CNN architecture learns the inherent features of the modified ECG signal better in comparison with the raw ECG signal. The method is applied on three publicly available ECG databases and it is found to be superior to other approaches in terms of classification accuracy. In MIT-BIH, St.-Petersberg, PTB databases the proposed method achieves maximum accuracy of 97.70%, 99.71%, and 98.24%, respectively.

A Negative Selection Algorithm-Based Identification Framework for Distribution Network Faults With High Resistance

Most high resistance faults in distribution network are caused by overhead lines contacting with high impedance objects. It is difficult to identify the high resistance faults with the steady-state characteristics in distribution network. In this paper, a Negative Selection Algorithm (NSA) based identification framework is proposed to detect the distribution network faults with high resistance. The Hilbert-Huang transform (HHT) analysis method is used to distinguish the faults from normal state. The sum of the first two order intrinsic mode function (IMF) components of zero sequence voltage within a cycle after fault is taken as the extracted characteristic of high resistance faults. An improved negative selection method is proposed to increase detection rate and realize the classification of abnormal states, so that normal training samples and a few fault samples can generate enough detector sets with higher coverage of non-self set area. Based on a 10 kV distribution network, the performance of the proposed identification framework is evaluated. The simulation results show that, compared with the wavelet analysis and the neural network algorithm, the proposed algorithm can effectively identify the high resistance faults in distribution network with small samples.

A Method to Improve the Imaging Quality in Dual-Wavelength Digital Holographic Microscopy.

A digital hologram-optimizing method was proposed to improve the imaging quality of dual-wavelength digital holographic microscopy (DDHM) by reducing the phase noise level. In our previous work, phase noise reduction was achieved by dual-wavelength digital image-plane holographic microscopy (DDIPHM). In the optimization method in this paper, the phase noise was further reduced by enhancing the real-image term and suppressing effects of the zero-order term in the frequency spectrum of a digital hologram. Practically, the carrier frequency of the real-image term has the correspondence with interference fringes in the hologram. Mathematically, the first order intrinsic mode function (IMF1) in bidimensional empirical mode decomposition (BEMD) has similar characteristics to the grayscale values of ideal interference fringes. Therefore, with the combination of DDIPHM and BEMD, by utilizing the characteristics of IMF1, the digital hologram was optimized with purified interference fringes, enhancing the real-image term simultaneously. Finally, the validity of the proposed method was verified by experimental results on a microstep.

Noise reduction method of ship radiated noise with ensemble empirical mode decomposition of adaptive noise

Ensemble empirical mode decomposition method (EEMD) is important improvement of the empirical mode decomposition (EMD), but it still leaves some annoying difficulties unresolved, such as large computational cost and reconstruction error. To solve above problems of the EEMD, a new method named EEMD of adaptive noise (EEMDAN) is proposed, which can obtain accurate time-frequency distribution of the original signal. In addition, aiming at the non-linear, non-stationary and non-Gaussian characteristics of underwater acoustic signal, a noise reduction method based on the EEMDAN is also proposed. Each order intrinsic mode function (IMF) can be obtained by the EEMDAN for noisy Lorenz signal and three different types of real ship radiated noises. The components of the signal and noise can be adaptively determined by IMF correlation coefficient based on energy density and average cycle. The results show that the method is effective and a very clean signal can be obtained.

BoostEMD: An Extension of EMD Method and Its Application for Denoising of EMG Signals

The paper presents a novel extension of the Huang’s Empirical Mode Decomposition (EMD) method, called BoostEMD, that allows calculating higher order Intrinsic Mode Functions (IMFs) that capture higher frequency empirical mode oscillations (empiquencies) in the EMG (electromyography) data. We describe the use of the second order IMFs for denoising physical action EMG signals. We demonstrate the efficiency of denoising by performing classification of EMG data before and after application of the denoising procedure as well as by evaluating properties of the extracted noise signal. DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13763

The Improved Algorithm of the EMD Decomposition Based on Cubic Spline Interpolation

It is critical to reduce the abnormal and complex jamming signals in the first order IMF component, because the decomposition result of the first order intrinsic mode function (IMF) in the empirical mode decomposition (EMD) method directly affects the subsequent decomposition effect, and also there are sampling restrictions for ultrasonic detectors. In this paper, we use a threestep optimization method to obtain the first order IMF. The first step is that the local extreme points of collected echo signals are fitted as the upper and lower envelopes by using the cubic spline interpolation method. The second step is to look for the curve of local mean of the echo signals on the basis of the upper and lower envelopes. The third step decomposes the curve of local mean by the EMD method. The simulation results show that our proposed method can effectively reduce jamming signals in the first order IMF and achieve the aim of improving the decomposition effect of the EMD method.

Empirical Mode Decomposition of simulated and real ultrasonic Doppler signals of periodic fetal activity

Simulated signals comprising components (trains of Gaussian packets) resulting from cardiac movements and from pseudorespiratory movements with added white noise were submitted to Empirical Mode Decomposition. The increase of sampling frequency fs (from 0.5kHz to 5kHz) for given signal to noise ratio SNR moves signal components toward higher order intrinsic mode functions (IMFs) and increases their number. The increase of the SNR (from −5dB to 10dB, fixed fs) moves the signal components to lower order IMFs. The separation of components is most efficient for SNR≥5dB and fs not exceeding 1kHz, for lower SNRs fs should be at least 2kHz. SNR=∞ results in erroneous decomposition and therefore limited noise level is beneficial. Recommended number of sifting iterations is 10. Fetal data obtained using 2MHz emission frequency and sampled at 2kHz were decomposed. The cardiac signal always appears in IMF3, frequently also in IMF1 and IMF2. The pseudobreathing signal, appearing mainly in IMF4-6, is easy to separate. Signals resulting from fetal displacements due to maternal respiration appear in IMF7 or IMF8. The EMD performs better than the classic linear filtering as a tool for separation of the pseudorespiration signals and provides inferior results in terms of separation of the cardiac signals.