Study on a Novel Fault Diagnosis Method Based on VMD and BLM

Zheng, Jianjie; Yuan, Yu; Zou, Li; Deng, Wu; Guo, Chen; Zhao, Huimin

doi:10.3390/sym11060747

Cited by 29 publications

(19 citation statements)

References 78 publications

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“…Finally, the SSM prediction method of degradation index is established to predict the probability density distribution of degradation index and obtain the reliability. In addition, some researchers proposed a lot of algorithms, which can be combined with different prediction models [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Zhang

Guo³

et al. 2021

IEEE Access

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Section: Introductionmentioning

confidence: 99%

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Zhang

Guo³

et al. 2021

IEEE Access

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“…Variational modal decomposition (VMD) method has a sufficient mathematical foundation, it can adaptively decompose the original signal to analyze the fault signal [ 23 , 24 , 25 , 26 ]. In addition, some new optimization methods are proposed to combine with signal processing methods in recent years [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation

Zhang

Wang

2020

Entropy

Self Cite

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It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance demodulation technology, namely GNVRFD, is proposed to realize the fault diagnosis of locomotive rolling bearings. In the proposed GNVRFD method, the genetic algorithm and nonlinear programming are combined to design a novel parameter optimization algorithm to adaptively optimize the two parameters of the VMD. Then the optimized VMD is employed to decompose the collected vibration signal into a series of intrinsic mode functions (IMFs), and the kurtosis value of each IMF is calculated, respectively. According to the principle of maximum value, two most sensitive IMF components are selected to reconstruct the vibration signal. The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively obtained. Finally, the actual data of rolling bearings is selected to testify the effectiveness of the proposed GNVRFD method. The experiment results demonstrate that the proposed GNVRFD method can more accurately and effectively diagnose the fault of locomotive rolling bearings by comparing with other fault diagnosis methods.

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“…In addition to the frequently-used time domain [2] and frequency domain analysis [3], current signal denoising methods have also developed some very advanced time-frequency analysis methods, such as empirical mode decomposition (EMD) [4], blind source separation (BSS) [5], energy entropy [6], variational mode decomposition (VMD) [7], wavelet transformation (WT) [8], approximate entropy (ApEn) [9], etc. Nonlinear and nonstationary signals can be decomposed into multiple intrinsic mode of the sampled signal, and PCA cannot well retain the real information of the original signal.…”

Section: Introductionmentioning

confidence: 99%

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

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Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

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Study on a Novel Fault Diagnosis Method Based on VMD and BLM

Cited by 29 publications

References 78 publications

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

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