Research on iterated Hilbert transform and its application in mechanical fault diagnosis

Qin, Yi; Shu-ren, Qin; Mao, Yongfang

doi:10.1016/j.ymssp.2008.01.014

Cited by 60 publications

(22 citation statements)

References 13 publications

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“…This leads to a research avenue of nonstationary signals de‐noising. Within this subject, two approaches are developed for solving this problem: wavelet threshold de‐noising and empirical mode decomposition (EMD)‐based de‐noising have been proposed .…”

Section: Instructionmentioning

confidence: 99%

High‐order partial differential equation de‐noising method for vibration signal

Zhao

Yin

2014

Math Methods in App Sciences

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A novel approach for 1D vibration signal de‐noising filter using partial differential equation (PDE) is presented. In particular, the numerical solution of higher‐order PDE is generated, and we show that it enables the amplitude‐frequency characteristic in filter to be estimated more accurately, which results in better de‐noising performance in comparison with the low‐order PDE. The de‐noising tests on different degree of artificial noise are conducted. Experimental tests have been rigorously compared with different de‐noising methods to verify the efficacy of the proposed high‐order PDE filter method. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Instructionmentioning

confidence: 99%

High‐order partial differential equation de‐noising method for vibration signal

Zhao

Yin

2014

Math Methods in App Sciences

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“…Gianfelici et al introduced an iterated Hilbert transform (IHT) method to obtain slow varying amplitude and its corresponding oscillatory signal by filtering and implement the method iteratively to the residue [18]. Qin et al have successfully utilized the IHT method for mechanical fault diagnosis [19]. The idea to decompose a multicomponent signal into monotones is very useful and deserves further study.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Demodulation and Hilbert Transform Based Signal Decomposition Method

Ren

Wang

et al. 2017

Shock and Vibration

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This paper proposes a new signal decomposition method that aims to decompose a multicomponent signal into monocomponent signal. The main procedure is to extract the components with frequencies higher than a given bisecting frequency by three steps: (1) the generalized demodulation is used to project the components with lower frequencies onto negative frequency domain, (2) the Hilbert transform is performed to eliminate the negative frequency components, and (3) the inverse generalized demodulation is used to obtain the signal which contains components with higher frequencies only. By running the procedure recursively, all monocomponent signals can be extracted efficiently. A comprehensive derivation of the decomposition method is provided. The validity of the proposed method has been demonstrated by extensive numerical analysis. The proposed method is also applied to decompose the dynamic strain signal of a cable-stayed bridge and the echolocation signal of a bat.

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“…Since EMD can pick out the high frequency component at any time instant, when there is abnormity in the signal, it cannot sift it out [12]. Furthermore, when the frequencies of some components in the signal are close [13], EMD cannot separate them so as to form mode aliasing. This phenomenon indicates in some degree that EMD is not an orthogonal decomposition method.…”

Section: Introductionmentioning

confidence: 99%