Adaptive variational mode decomposition method for signal processing based on mode characteristic

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

doi:10.1016/j.ymssp.2018.01.019

Cited by 241 publications

(95 citation statements)

References 19 publications

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“…Wang et al [36] presented the permutation entropy optimization method to determine the mode number K of VMD. Lian et al [37] proposed a neoteric method termed as adaptive variational mode decomposition, which can choose the mode number K of VMD based on the characteristic of the mode components. However, all parameter selection criteria presented in the above ref.…”

Section: Introductionmentioning

confidence: 99%

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Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

et al. 2020

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Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

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Section: Introductionmentioning

confidence: 99%

“…However, all parameter selection criteria presented in the above ref. [29][30][31][32][33][34][35][36][37] ignored the influence of the penalty factorα on the decomposition ability. Thus, on this basis, Shan et al [38] applied the minimization criterion of the root mean square error (RMSE) to adaptively search for two VMD parameters.…”

Section: Introductionmentioning

confidence: 99%

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

et al. 2020

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“…In order to overcome the critical drawback, some studies have been conducted. Lian et al [17] chose M according to a series of indicators including permutation entropy, extreme value in the frequency domain, kurtosis criterion, and energy loss coefficient. Liu et al [18] estimated M by using the minimum redundancy maximum relevance.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [18] estimated M by using the minimum redundancy maximum relevance. e above two methods in [17,18] both optimized the value of M but neglected the influence of α on decomposition results. Based on the grasshopper optimization algorithm, Zhang et al [19] proposed a method to obtain the optimum of M and α. Wang et al [20] used multi-objective particle swarm optimization (MOPSO) to obtain the optimum of M and α, in which the symbol dynamic entropy and the power spectral entropy were selected as the objective function of MOPSO.…”

Section: Introductionmentioning

confidence: 99%

Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis

Wang

Deng

Jianxi

et al. 2020

Mathematical Problems in Engineering

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In view of the incipient fault characteristics are difficult to be extracted from the raw bearing fault signals, an incipient bearing fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) is proposed. The beetle antennae search (BAS) algorithm is adopted to seek for the optimal combination of the VMD parameters. The reciprocals of the calculated kurtosis values of intrinsic mode functions (IMFs) decomposed via VMD are employed as a fitness function in the searching process. The optimal mode number and the quadratic penalty term of VMD are adaptively set after the search. Afterwards, a vibration signal is decomposed into a set of IMFs using the parameter-adaptive VMD, and the IMF with the maximal kurtosis value is selected as the sensitive one. The selected IMF is further analyzed by Hilbert envelope demodulation. The resulting envelope spectrum can show the significant fault impulse characteristics which are highly helpful to diagnose incipient bearing faults. The kurtosis and the proportion of fault energy are introduced as the input vector of the extreme learning machine (ELM). Comparisons have been conducted via ELM to evaluate the performance by using EMD and the fixed-parameter VMD. The experimental results demonstrate that the proposed method is more effective in extracting the incipient bearing fault characteristics.

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“…It shows better noise robustness, and can effectively separate two pure harmonic signals with similar frequencies. However, when the VMD method decomposes the signal, the decomposition effect is seriously affected by the number of decomposition components [56][57][58][59][60]. Some other methods have been proposed to realize signal analysis and fault diagnosis in recent years [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

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The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

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Adaptive variational mode decomposition method for signal processing based on mode characteristic

Cited by 241 publications

References 19 publications

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

Parameter-Adaptive VMD Method Based on BAS Optimization Algorithm for Incipient Bearing Fault Diagnosis

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

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