2013
DOI: 10.1016/j.ymssp.2013.08.010
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Chirplet Wigner–Ville distribution for time–frequency representation and its application

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Cited by 25 publications
(12 citation statements)
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“…(6) indicates that, to compute STðt; f Þ at analysis frequency f , one needs to shift XðαÞ by Àf , compute Wðα; f Þ, evaluate the product Xðα þf ÞWðα; f Þ, and subsequently perform an inverse Fourier transform of Xðα þf ÞWðα; f Þ. The frequency representation, XðαÞ, needs to be evaluated only once and then shifted for different analysis frequencies.…”
Section: S-transform and Modificationsmentioning
confidence: 99%
See 1 more Smart Citation
“…(6) indicates that, to compute STðt; f Þ at analysis frequency f , one needs to shift XðαÞ by Àf , compute Wðα; f Þ, evaluate the product Xðα þf ÞWðα; f Þ, and subsequently perform an inverse Fourier transform of Xðα þf ÞWðα; f Þ. The frequency representation, XðαÞ, needs to be evaluated only once and then shifted for different analysis frequencies.…”
Section: S-transform and Modificationsmentioning
confidence: 99%
“…The time-frequency distribution (TFD) obtained by a TFR is an important tool for analysis of non-stationary signal that can be found in various situations in practice [3][4][5][6][7][8][9][10][11][12][13]. Some renowned TFR methods include short-time Fourier transform (STFT) [4], Wigner-Ville distribution [5,6], Hilbert-Huang transform [7,8], wavelet transforms [9,10], and the Generalized Synchrosqueezing Transform [11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, vibration signal analysis is vital for rolling bearings fault diagnosis due to its connection to fault feature extraction accuracy [3]. Aiming to extract the fault features, many feature extraction methods, including Wigner-Ville distribution (WVD) [4], wavelet packet decomposition (WPT) [5,6], and empirical mode decomposition (EMD) [7][8][9], have been proposed and have been demonstrated to be powerful. Additionally, many fault diagnosis methods also have been proposed, such as fast spectral kurtosis based on genetic algorithms [10], multiscale entropy and adaptive neurofuzzy inference system [11], and time varying singular value decomposition [12].…”
Section: Introductionmentioning
confidence: 99%
“…Many studies have been done to reduce these cross-terms [15]. Chen et al [16] presented a method called chirplet Wigner-Ville distribution that was capable of representing nonlinear and nonstationary signals effectively. The matching pursuit (MP) algorithm [17] was employed in the chirplet signal decomposition, and the vibration signal is decomposed adaptively into a series of linear chirplets.…”
Section: Introductionmentioning
confidence: 99%