Synchrosqueezing Matching Pursuit Time–Frequency Analysis

Xu, Lu; Yin, Xingyao; Zong, Zhaoyun; Li, Kun

doi:10.1109/lgrs.2020.2978877

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…(3) The gray histogram of the sub-image and the histogram information of the whole image are fused. Adaptation is the fusing factor, and its expression is as follows: (14) (4) The fusion results are fused with Histgram (L) again, and the fusion process is as follows: (15) (5) Cut the histogram according to CALHE, and then equalize the cut histogram to get a mapping table for each block.…”

Section: Local Histogram Clipping Equalization Image Enhancement Algo...mentioning

confidence: 99%

“…Zhu et al [14] proposed synchronous extraction of chirplet transform to improve the accuracy of intermediate frequency estimation. The purpose of the Synchrosqueezing matching pursuit algorithm is to enhance the energy aggregation in the TF plane [15]. This method compresses the TF distribution to the vicinity of the center time of the selected wavelet through a two-dimensional Gaussian function, which can improve the time-frequency aggregation.…”

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confidence: 99%

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Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

et al. 2022

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Aiming at the problem that the time-frequency image of bearing fault characteristics is relatively weak and difficult to identify. This paper presents a time-frequency analysis method of local maximum synchrosqueezing transform based on image enhancement. Firstly, the instantaneous frequency of the collected vibration signal is obtained through local maximum synchrosqueezing transformation. Secondly, a local histogram cropping equalization image enhancement algorithm is proposed, which is used to obtain time-frequency images with clearer textures. Then, in order to extract fault features from the enhanced instantaneous frequency (IF) image, A new neural network is proposed. The network consists of Multi-size convolution kernel module, Dual-channel pooling layer and Cross Stage Partial Network (MDCNet). Finally, the fault signal was collected on the bearing fault test bench for prediction, and the accuracy rate reached 99.7%. And compared with AlexNet, VGG-16, Resnet and other methods. The results show that the method can meet the needs of actual engineering.INDEX TERMS Fault diagnosis, image enhancement, instantaneous frequency.

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Section: Local Histogram Clipping Equalization Image Enhancement Algo...mentioning

confidence: 99%

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Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

et al. 2022

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“…Traditional time-frequency analysis methods include the short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert-Huang transform (HHT), Wigner-Ville dis-tribution (WVD), and reassignment method (RM). STFT and CWT are constrained by the Heisenberg uncer-tainty principle, and the resulting time-frequency distributions are frequently indistinct and unable to offer precise time-frequency representations for time-varying signals [2,3]. Due to the lack of mathematical theoretical support in Empirical Mode Decomposition (EMD), HHT suffers from endpoint effects and modal aliasing [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Time-Frequency Analysis and Modal Decomposition Method for Power Harmonics Based on Multisynchrosqueezing Transform

Tao,

Chu

2024

Preprint

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Time-frequency analysis (TFA) is an essential tool in power harmonic analysis. However, the traditional TFA method tends to generate a fuzzy time-frequency distribution. In order to enhance the energy aggregation of harmonic time-frequency distribution, the multisynchrosqueezing transform (MSST) is introduced to analyze the time-frequency and mode decomposition of power harmonics. Firstly, the result of the synchrosqueezing transform of a harmonic signal is calculated iteratively. Subsequently, the time-frequency distribution energy of the harmonic signal is compressed further to obtain a more accurate time-frequency graph of the harmonic signal. Secondly, MSST's powerful reconstruction ability is utilized to decompose the harmonic signal mode, resulting in a series of intrinsic mode type functions (IMT) of varying frequencies to extract each harmonic component. An improved multisynchrosqueezing transform (IMSST) was proposed to solve the problems of non-rearranged points and endpoint effects in MSST. Simulation experiments and measured data demonstrate that the proposed method can achieve more accurate time-frequency analysis and mode decomposition of harmonic signals. It also reveals the time-frequency characteristics and variations of harmonic waves in power grids, which is crucial for harmonic control in power grids.

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Detection of Bearing Fault in Induction Motor Based on Improved Adaptive Chirp Mode Decomposition

Li,

Qiu,

Liu

et al. 2024

Lecture Notes in Electrical Engineering

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Synchrosqueezing Matching Pursuit Time–Frequency Analysis

Cited by 11 publications

References 20 publications

Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

Application of Local Histogram Clipping Equalization Image Enhancement in Bearing Fault Diagnosis

A Time-Frequency Analysis and Modal Decomposition Method for Power Harmonics Based on Multisynchrosqueezing Transform

Detection of Bearing Fault in Induction Motor Based on Improved Adaptive Chirp Mode Decomposition

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