scholarly journals Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 935 ◽  
Author(s):  
Derong Luo ◽  
Ting Wu ◽  
Ming Li ◽  
Benshun Yi ◽  
Haibo Zuo
Keyword(s):  
Fourier Transform ◽  
Direct Current ◽  
Hilbert Transform ◽  
Amplitude Modulation ◽  
Modulation Frequency ◽  
Variational Mode Decomposition ◽  
Instantaneous Amplitude ◽  
Modal Number ◽  
Dc Power ◽  
Mode Decomposition

Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.

Envelope demodulation based on variational mode decomposition for gear fault diagnosis

2016 ◽  
Vol 231 (4) ◽  
pp. 864-870 ◽  
Author(s):  
Xueli An ◽  
Hongtao Zeng ◽  
Chaoshun Li
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Decomposition Method ◽  
Modulation Frequency ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Component Amplitude

A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.

scholarly journals Cycle Analysis Method of Tree Ring and Solar Activity Based on Variational Mode Decomposition and Hilbert Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Guohui Li ◽  
Mengtao Zheng ◽  
Hong Yang
Keyword(s):  
Solar Activity ◽  
Tree Ring ◽  
Sunspot Number ◽  
Hilbert Transform ◽  
Ring Width ◽  
Sunspot Activity ◽  
Variational Mode Decomposition ◽  
Tree Ring Width ◽  
Analysis Method ◽  
Mode Decomposition

According to the correlation of tree ring and solar activity, the cycle analysis method based on variational mode decomposition (VMD) and Hilbert transform is proposed. Firstly, the tree ring width of cypress during 1700 to 1955 beside the Huangdi Tomb and the long-term sunspot number during 1700 to 1955, respectively, are decomposed by VMD into a series of intrinsic mode functions (IMFs). Secondly, Hilbert transformation on the decomposed IMF component is performed. Then, the marginal spectra are given and analyzed. Finally, their quasiperiodic properties are obtained as follows: the tree ring width has the quasiperiodicity of 2 to 7a, 10.8a, and 25a; the sunspot number has the quasiperiodicity of 8.3a, 9.9a, 11.1a, 52.2a, and 101.2a. The result obtained by analyzing that quasiperiodicity shows that the main periods of tree ring width and the sunspot number in the same period are basically consistent, and tree ring width has other cycles. This shows that sunspot activity is an important factor affecting tree ring growth, and tree ring width is influenced by other external environments.

The Analysis of Gearbox Fault Diagnosis Research Based on the EMD and Hilbert Envelope Demodulation

2014 ◽  
Vol 926-930 ◽  
pp. 1800-1805 ◽  
Author(s):  
Guo Dong Han ◽  
Shu Ting Wan ◽  
Zhan Jie Lv ◽  
Rong Hai Liu ◽  
Jin Wang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Power Spectrum ◽  
Fast Fourier Transform ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
The Hilbert Transform ◽  
Fault Characteristic

This paper puts forward a kind of gearbox fault diagnosis methods which based on empirical mode decomposition (EMD), Hilbert transform, Fast Fourier Transform (FFT) and spectrum refined techniques. This method is applicable to the analysis of the nonlinear unsteady signal. First of all used wavelet denoising to the acquisition of gearbox vibrate signal, again carries on the empirical mode decomposition (EMD), than get a certain number of intrinsic mode function (imf); Choose the specific imf, based on kurtosis value, after the Hilbert transform and Fast Fourier Transform is done, the corresponding power spectrum can be obtained; To refine the power spectrum and extract the gearbox fault characteristic frequency; Then in pattern recognition and diagnosis of the gearbox fault, and compared with the normal signal characteristics. The analysis results show that the proposed method can effectively detect the gearbox fault characteristics.

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