scholarly journals Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring

2016 ◽  
Vol 11 (4) ◽  
pp. 443-452 ◽  
Author(s):  
Wenxian Yang ◽  
Zhike Peng ◽  
Kexiang Wei ◽  
Pu Shi ◽  
Wenye Tian
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Feature

Operation feature extraction of flood discharge structure based on improved variational mode decomposition and variance dedication rate

2019 ◽  
Vol 26 (3-4) ◽  
pp. 229-240
Author(s):  
Jianwei Zhang ◽  
Ge Hou ◽  
Han Wang ◽  
Yu Zhao ◽  
Jinlin Huang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Denoising Method ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Discharge Structure ◽  
Operation Characteristic ◽  
Rate Method

Operation feature extraction of flood discharge structures under ambient excitation has attracted increasing attention in recent years. However, the vibration signal of flood discharge structures is a nonstationary random signal with low signal-to-noise ratio, which is mixed with lots of low-frequency water flow noise and high-frequency white noise. It is difficult to excavate the hidden vibration characteristic information accurately. To solve the problem, we propose a novel denoising method called improved variational mode decomposition. As an improved method of variational mode decomposition, improved variational mode decomposition can effectively determine the decomposition mode number of variational mode decomposition by using the mutual information method. Furthermore, improved variational mode decomposition is combined with a variance dedication rate to extract the overall operation characteristic information of the structure. In order to evaluate the applicability and effectiveness of the proposed improved variational mode decomposition–variance dedication rate method, we compare the denoising results of simulation signals produced by an improved variational mode decomposition–variance dedication rate with those produced by digital filter, wavelet threshold, empirical mode decomposition, empirical wavelet transform, complete ensemble empirical mode decomposition with adaptive noise, and improved variational mode decomposition methods and find a better performance of the improved variational mode decomposition–variance dedication rate method. In addition, the proposed method is applied to the Three Gorges Dam, and the results show that the improved variational mode decomposition–variance dedication rate method can effectively remove heavy background noises and extract the operation characteristic information of the flood discharge structure, which contributes to health monitoring and damage identification of the flood discharge structure.

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

scholarly journals Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 520
Author(s):  
Tao Liang ◽  
Hao Lu ◽  
Hexu Sun
Keyword(s):  
Feature Extraction ◽  
Empirical Method ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Penalty Factor ◽  
Reconstructed Signal

The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number K and penalty factor α. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy (Ee) can reflect the sparsity of the signal, and Renyi entropy (Re) can reflect the energy aggregation degree of the time-frequency distribution of the signal. Therefore, Ee and Re are selected as fitness functions, and the optimal solution of VMD parameters is obtained by the MIGA algorithm. Second, the improved VMD algorithm is used to decompose the bearing fault signal, and then two intrinsic mode functions (IMF) with the most fault information are selected by improved kurtosis and Holder coefficient for reconstruction. Finally, the envelope spectrum of the reconstructed signal is analyzed. The analysis of comparative experiments shows that the feature extraction method can extract bearing fault features more accurately, and the fault diagnosis model based on this method has higher accuracy.

scholarly journals A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 507 ◽  
Author(s):  
Yuxing Li ◽  
Long Wang ◽  
Xueping Li ◽  
Xiaohui Yang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Extraction Technique ◽  
Permutation Entropy ◽  
Radio Noise ◽  
Mode Decomposition ◽  
Linear Spectrum ◽  
Frequency Feature ◽  
Feature Extraction Technique

Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

Time-Frequency Feature Extraction without Cross-Terms Based on Acoustic Signal in Rotor Malfunctions

2005 ◽  
Vol 293-294 ◽  
pp. 467-474 ◽  
Author(s):  
F.Q. Wu ◽  
Guang Meng
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Wigner Distribution ◽  
Component Analysis ◽  
Independent Component ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Cross Terms ◽  
The Cross

An effective approach is presented to eliminate the cross-terms in Wigner distribution by ICA (independent component analysis) and EMD (empirical mode decomposition), through which the cross-terms caused by the uncorrelated mixing signals can be removed successfully. This method is used for time-varying signal analysis and is powerful in signal feature extraction, especially for joint time frequency resolution, which is demonstrated by numerical examples. To further understand the method and its application, a detailed analysis about abrupt unbalance experimental example is shown to explain the cause of malfunction as well as its occurrence and phenomenon. In addition, the proposed approach based upon independent component analysis, empirical mode decomposition method and wigner distribution allows the separation and analysis of the sources with nonlinear and non-stationary properties. In this method, the main conceptual innovations are the associated introduction of ‘source separation’ and ‘intrinsic mode functions’ based on the local properties of the mixed signals, which makes the instantaneous frequency meaningful; the method serves to illustrate the roles played by the nonlinear and non-stationary effects in the energy-time-frequency distribution. At the same time, the method can also be expanded and applied in other fields.

Fault feature extraction of wind turbine gearbox under variable speed based on improved adaptive variational mode decomposition

2019 ◽  
Vol 234 (6) ◽  
pp. 848-861 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Shuai Wang ◽  
Yiqun Qian
Keyword(s):  
Feature Extraction ◽  
Wind Turbine ◽  
Variational Mode Decomposition ◽  
Variable Speed ◽  
Noise Interference ◽  
Order Spectrum ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Non Stationary Signal ◽  
Fault Feature Extraction

This paper proposes a study on gearbox fault feature extraction of wind turbine under variable speed condition using improved adaptive variational mode decomposition (VMD). Frequent changes in wind speed and critical noise interference make the vibration signal exhibit non-stationary characteristics. Although computational order tracking can transform non-stationary signals in the time domain into stationary angular signals, and then extract fault features from order spectrum obtained by FFT, the obtained order spectrum is liable to be polluted by noise or to appear order aliasing. To avoid order aliasing and eliminate noise interference, the original non-stationary signal is firstly processed using the improved adaptive VMD method called adaptive differential evolution – VMD (ADE-VMD). ADE-VMD can not only utilise the advantages of traditional VMD but also adaptively select narrow-band intrinsic mode function (NBIMF) to construct the reconstructed signal with less noise and without order aliasing. In the experiment, we compared the ADE-VMD method with other VMD methods such as GA-VMD, PSO-VMD and DE-VMD, and the results showed that ADE-VMD has excellent adaptive processing ability, and its convergence and optimisation speed are more remarkable. ADE-VMD can effectively filter the noise inference and avoid the order aliasing, so it is well suitable for fault feature extraction under variable speed.

scholarly journals Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 783 ◽  
Author(s):  
Martin Valtierra-Rodriguez ◽  
Juan Amezquita-Sanchez ◽  
Arturo Garcia-Perez ◽  
David Camarena-Martinez
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Induction Motors ◽  
A Priori ◽  
Digital Signal ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Mode Decomposition

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

Sign in / Sign up

Export Citation Format

Share Document