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.

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.

scholarly journals A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Cancan Yi ◽  
Yong Lv ◽  
Zhang Dang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Pso Algorithm ◽  
Potential Method ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Mode Decomposition

Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.

Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine

2016 ◽  
Vol 39 (7) ◽  
pp. 1000-1006 ◽  
Author(s):  
Xueli An ◽  
Yongjun Tang
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Wind Turbine ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition

For the unsteady characteristics of a fault vibration signal of a wind turbine’s rolling bearing, a bearing fault diagnosis method based on variational mode decomposition of the energy distribution is proposed. Firstly, variational mode decomposition is used to decompose the original vibration signal into a finite number of stationary components. Then, some components which comprise the major fault information are selected for further analysis. When a rolling bearing fault occurs, the energy in different frequency bands of the vibration acceleration signals will change. Energy characteristic parameters can then be extracted from each component as the input parameters of the classifier, based on the K nearest neighbour algorithm. This can identify the type of fault in the rolling bearing. The vibration signals from a spherical roller bearing in its normal state, with an outer race fault, with an inner race fault and with a roller fault were analyzed. The results showed that the proposed method (variational mode decomposition is used as a pre-processor to extract the energy of each frequency band as the characteristic parameter) can identify the working state and fault type of rolling bearings in a wind turbine.

An optimal variational mode decomposition for rolling bearing fault feature extraction

2019 ◽  
Vol 30 (5) ◽  
pp. 055004 ◽  
Author(s):  
Dongdong Wei ◽  
Hongkai Jiang ◽  
Haidong Shao ◽  
Xingqiu Li ◽  
Ying Lin
Keyword(s):  
Feature Extraction ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction

scholarly journals Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Maohua Xiao ◽  
Kai Wen ◽  
Cunyi Zhang ◽  
Xiao Zhao ◽  
Weihua Wei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Characteristic Frequency ◽  
Extraction Method ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction ◽  
Fault Characteristic

Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.

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