Journal ArticleDOI
Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system
TLDR
VMD is a newly developed technique for adaptive signal decomposition, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions and shows that the multiple features can be better extracted with the VMD, simultaneously.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2015-08-01. It has received 418 citations till now. The article focuses on the topics: Wavelet & Wavelet transform.read more
Citations
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A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery
TL;DR: In this paper, the authors proposed a parameter-adaptive variational mode decomposition (VMD) method based on grasshopper optimization algorithm (GOA) to analyze vibration signals from rotating machinery.
Journal ArticleDOI
Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump
TL;DR: In this article, a variational mode decomposition (VM decomposition) was applied to detect different location fault features for rolling bearings fault diagnosis via modeling simulation vibration signal and practical vibration signal.
Journal ArticleDOI
Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive
TL;DR: In this paper, an independence-oriented VMD method via correlation analysis is proposed to adaptively extract weak and compound fault feature of wheel set bearing of high speed locomotive, and then the similar modes are combined according to the similarity of their envelopes to solve the over decomposition problem.
Journal ArticleDOI
A review of stochastic resonance in rotating machine fault detection
Siliang Lu,Qingbo He,Jun Wang +2 more
TL;DR: This study is committed to providing a comprehensive review of SR from history to state-of-the-art methods and finally to research prospects, along with the applications in rotating machine fault detection.
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Variational mode decomposition denoising combined the detrended fluctuation analysis
TL;DR: A novel signal denoising method that combines variational mode decomposition (VMD) and detrended fluctuation analysis (DFA), named DFA-VMD, is proposed in this paper and shows the superior performance of this proposed filtering over EMD-based denoisings and discrete wavelet threshold filtering.
References
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Journal ArticleDOI
Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu,Norden E. Huang +1 more
TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
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Variational Mode Decomposition
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Journal ArticleDOI
Empirical mode decomposition as a filter bank
TL;DR: It turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions, and the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.
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Multiplier and gradient methods
TL;DR: The main purpose of this paper is to suggest a method for finding the minimum of a functionf(x) subject to the constraintg(x)=0, which consists of replacingf byF=f+λg+1/2cg2, and computing the appropriate value of the Lagrange multiplier.
On empirical mode decomposition and its algorithms
TL;DR: Empirical Mode Decomposition is presented, and issues related to its effective implementation are discussed, and an interpretation of the method in terms of adaptive constant-Q filter banks is supported.