Rolling Ball-Bearing Fault Classification Using Variational Mode Decomposition and Footprint of Hilbert Transform

2020 ◽  
pp. 581-589
Author(s):  
Rahul Dubey
Keyword(s):  
Hilbert Transform ◽  
Ball Bearing ◽  
Fault Classification ◽  
Variational Mode Decomposition ◽  
Rolling Ball ◽  
Bearing Fault ◽  
Mode Decomposition

An optimized variational mode extraction method for rolling bearing fault diagnosis

2021 ◽  
pp. 147592172110066
Author(s):  
Bin Pang ◽  
Mojtaba Nazari ◽  
Zhenduo Sun ◽  
Jiaying Li ◽  
Guiji Tang
Keyword(s):  
Extraction Method ◽  
Fitness Function ◽  
Rolling Bearing ◽  
Initial Guess ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Extraction

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

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.

Roller Bearing Fault Diagnosis Based on IMF Kurtosis and SVM

2013 ◽  
Vol 694-697 ◽  
pp. 1160-1166
Author(s):  
Ke Heng Zhu ◽  
Xi Geng Song ◽  
Dong Xin Xue
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
The Imf

This paper presents a fault diagnosis method of roller bearings based on intrinsic mode function (IMF) kurtosis and support vector machine (SVM). In order to improve the performance of kurtosis under strong levels of background noise, the empirical mode decomposition (EMD) method is used to decompose the bearing vibration signals into a number of IMFs. The IMF kurtosis is then calculated because of its sensitivity of impulses caused by faults. Subsequently, the IMF kurtosis values are treated as fault feature vectors and input into SVM for fault classification. The experimental results show the effectiveness of the proposed approach in roller bearing 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.

Application of optimized variational mode decomposition based on kurtosis and resonance frequency in bearing fault feature extraction

2019 ◽  
Vol 42 (3) ◽  
pp. 518-527 ◽  
Author(s):  
Hua Li ◽  
Tao Liu ◽  
Xing Wu ◽  
Qing Chen
Keyword(s):  
Resonance Frequency ◽  
Rolling Bearing ◽  
Adaptive Signal Processing ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Fault Type ◽  
Mode Decomposition ◽  
Adaptive Signal ◽  
Fault Feature Extraction ◽  
Fault Information

Variational mode decomposition (VMD) is an adaptive signal processing method proposed recently. It has gradually been widely used due to its good performance. According to the problem that the parameters of VMD need to be determined in advance, a simple and feasible method of determining the influence parameters based on the principle of kurtosis maximum is put forward. A novel intrinsic mode function (IMF) selection method based on resonance frequency is proposed in order to select the IMF that contains the abundant fault feature information. Firstly, the parameters of VMD are optimized by the principle of kurtosis maximum, the optimal penalty parameter and mode number of VMD are set, and the original fault signal is processed by the optimized VMD to obtain the established IMF components. Then, the sensitive IMF(s) with the fault information is selected by resonance frequency. Finally, the selected IMF(s) is analyzed by the envelope demodulation analysis to extract the fault characteristic frequency to judge the fault type of the rolling bearing. It is shown that the method can extract the weak characteristics of the early fault signal of the rolling bearing, and it can realize the judgment of the bearing fault accurately through the analysis of simulated signal and the actual data of bearing.

Sign in / Sign up

Export Citation Format

Share Document