VMD based adaptive multiscale fuzzy entropy and its application to rolling bearing fault diagnosis

Zheng, Jinde; Jiang, Zhanwei; Zi-wei, Pan; Zhang, Kang

doi:10.1109/icsenst.2016.7796267

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…Jinde et al has proposed VMD, adaptive multiscale fuzzy entropy (AMFE) and support vector machine (SVM) for rolling bearing fault diagnosis [25]. Zhao and Li has proposed VMD and Teager energy operator (TEO) for wind turbine bearing diagnosis [26].…”

Section: Bearing Applicationmentioning

confidence: 99%

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

et al. 2019

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Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis.

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Section: Bearing Applicationmentioning

confidence: 99%

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

et al. 2019

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“…What's more, Index Energy is constructed through the inner dynamic relation between the signal in real-time monitoring and subsector. Hence, in some meaning, Index Energy is a process amount [4] [5]. It provides condition for judging the information that contained in component dynamically.…”

Section: Selection Of Imf Based On the Index Energymentioning

confidence: 99%

Research on the Multi-scale Fuzzy Entropy based on Index Energy

Wei¹,

Ma²,

Cui³

2015

Advances in Intelligent Systems Research

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In order to improve the accuracy of feature extraction, a Multi-scale Fuzzy Entropy algorithm is proposed based on Entropy theory. The new algorithm combines the concept of Fuzzy Entropy and Multi-scale Entropy. Aimed at the shortage of Empirical mode decomposition (EMD), Index Energy is selected as evaluation index, and a new feature extraction method is designed to dispose vibration signal. Experiment shows that the new method can effectively distinguish different condition of rotor vibration signal and can significantly improve the effect of feature extraction.

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“…The key of vibration analysis is to extract the feature information from the vibration signal. 3 However, the vibration signal obtained by the data acquisition device contains various noises. Therefore, it is necessary to effectively filter the noise signal, increase the SNR, and extract useful information contained in the noise, so that we can obtain correct analysis results.…”

Section: Introductionmentioning

confidence: 99%

Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Fault diagnosis of rolling bearings can effectively prevent sudden accidents and is an important factor for the safe operation of mechanical systems. However, traditional time–frequency analysis techniques cannot effectively obtain the fault feature information. In this paper, a flat variational modal decomposition denoising method based on wavelet transform and variational modal decomposition is proposed to solve susceptibility of vibration signal to noise interference and easily obtain fault features. In this method, first, a series of mother wavelets with different periods are designed based on tone-burst signals, in the decomposition process of variational modal decomposition. This method is based on the designed mother wavelet along with wavelet correlation coefficient for the elimination of the components that are superfluous and frequent from each intrinsic mode function. Then, the regression coefficients of the denoise components and the original signal are calculated, and we select the corresponding components with higher regression coefficients to reconstruct the signal. The reconstructed signal is taken as the new original signal to be decomposed again by variational modal decomposition, and the relevant components are analyzed by enveloping the spectrum, so as to effectively remove noise interference and ensure accurate acquisition of fault feature frequency. We apply this method to the rolling bearing fault data and a comparative study is made with variational modal decomposition and empirical mode decomposition algorithms. The results show that the signal-to-noise ratio of the signal is improved by 77% and 44% after being processed by the flat variational modal decomposition method, compared to the empirical mode decomposition and the variational modal decomposition methods.

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VMD based adaptive multiscale fuzzy entropy and its application to rolling bearing fault diagnosis

Cited by 14 publications

References 7 publications

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

Research on the Multi-scale Fuzzy Entropy based on Index Energy

Application of a flat variational modal decomposition algorithm in fault diagnosis of rolling bearings

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