Analysis and design of modified window shapes for S-transform to improve time–frequency localization

Ma, Jia; Jiang, Jin

doi:10.1016/j.ymssp.2014.12.015

Cited by 23 publications

(9 citation statements)

References 28 publications

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“…Although the linear time-frequency representations (TFRs), including short-time Fourier transform (STFT) and wavelet transform, perform well in detecting high frequency impulses in the signal, their poor resolution at low frequencies can easily cause the loss of information on texture in the low frequency band. The S transform [19], S-method [20], and their modified versions [21], [22] have better time-frequency concentration and better cross-term suppression ability; this improves estimation accuracy at a continuous instantaneous frequency. However, for the instantaneous impulses of a bearing fault, it is not necessary to analyze the intermittent time-varying frequency law, as it has no clear physical meaning.…”

Section: Adaptive Optimal Kernel Time-frequency Representation (Amentioning

confidence: 99%

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Lin

Liu

et al. 2019

IEEE Access

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The texture feature tensor established from a subband time-frequency image (TFI) was extracted and used to identify the fault states of a rolling bearing. The TFI of adaptive optimal-kernel distribution was optimally partitioned into TFI blocks based on the minimum frequency band entropy. The texture features were extracted from the co-occurrence matrix of each TFI block. Based on the order of the segmented frequency bands, the texture feature tensor was constructed using the multidimensional feature vectors from all the blocks; this preserved the inherent characteristic of the TFI structure and avoided the information loss caused by vectorizing multidimensional features. The linear support higher order tensor machine based on the feature tensor was applied to identify the fault states of the rolling bearing. INDEX TERMS Texture feature tensor, frequency band entropy, linear support higher-order tensor machine, bearing fault intelligent diagnosis.

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Section: Adaptive Optimal Kernel Time-frequency Representation (Amentioning

confidence: 99%

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Lin

Liu

et al. 2019

IEEE Access

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“…Thus, we can say that the CST intertwines the STFT and CWT. Since its inception, the Stockwell transform has gained considerable attention and has been widely applied in geophysics, quantum physics, acoustics, feature detection, biomedical imaging, oceanology and signal processing, in general [7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A family of convolution-based generalized Stockwell transforms

Srivástava

Shah

Tantary

2020

J. Pseudo-Differ. Oper. Appl.

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The main purpose of this paper is to introduce a family of convolution-based generalized Stockwell transforms in the context of time-fractional-frequency analysis. The spirit of this article is completely different from two existing studies (see D. P. Xu and K. Guo [Appl. Geophys. 9 (2012) 73-79] and S. K. Singh [J. Pseudo-Differ. Oper. Appl. 4 (2013) 251-265]) in the sense that our approach completely relies on the convolution structure associated with the fractional Fourier transform. We first study all of the fundamental properties of the generalized Stockwell transform, including a relationship between the fractional Wigner distribution and the proposed transform. In the sequel, we introduce both the semi-discrete and discrete counterparts of the proposed transform. We culminate our investigation by establishing some Heisenberg-type inequalities for the generalized Stockwell transform in the fractional Fourier domain.

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“…[6][7][8][9][10] GST makes the width of window vary with the frequency, which is adoptable for analyzing modulation fault signals because it can adjust its time-frequency resolution effectively. Ma and Jiang 11 proposed a modified window design method for S transform to improve the time-frequency localization, which can generate windows with different width profiles for multicomponent signals through selecting proper tuning parameters of a sigmoid function. Cai and Li 12 introduced a time-frequency domain denoising method using the GST, in which a time-frequency filter factor is constructed to filter the vibration signal in the time-frequency domain, and can eliminate strong noise and can be used to extract the edge band structure that reflects the fault mode of gear.…”

Section: Introductionmentioning

confidence: 99%

Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform

Yan

Wang

Zhou

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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In order to extract fault impulse feature of large-scale rotating machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined multiresolution generalized S transform is proposed in this paper. In this method, multiresolution generalized S transform is employed to find the optimal atom for every iteration, which firstly takes in to account the generalized S transform with discretized adjustment factors, then builds an atom corresponding to the maximum energy. The multiresolution generalized S transform has better accuracy compared to generalized S transform and faster searching speed compared to the orthogonal matching pursuit method in selecting the optimal atom. Then, the orthogonal matching pursuit method is used to decompose the signal into several optimal atoms. The proposed method is applied to analyze the simulated signal and vibration signals collected from experimental failure rolling bearings. The results prove that the proposed method has better performances such as high precision and fast decomposition speed than the traditional orthogonal matching pursuit method method and local mean decomposition method.

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Analysis and design of modified window shapes for S-transform to improve time–frequency localization

Cited by 23 publications

References 28 publications

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

A family of convolution-based generalized Stockwell transforms

Sparse feature extraction for fault diagnosis of rotating machinery based on sparse decomposition combined multiresolution generalized S transform

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