Multisynchrosqueezing Transform

Yu, Gang; Wang, Zhonghua; Zhao, Peng

doi:10.1109/tie.2018.2868296

Cited by 343 publications

(104 citation statements)

References 28 publications

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“…en, for the proposed method, according to equation (24), we first calculate the frequency energy function. e detected peaks of the sum of E i (ω) are shown in Figure 16(a), where " * " denotes the detected peaks.…”

Section: Methodsmentioning

confidence: 99%

An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019

Shock and Vibration

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In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

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Section: Methodsmentioning

confidence: 99%

An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019

Shock and Vibration

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“…A new IF estimate is constructed to reassign the blurry STFT energy by each iteration procedure. By multiple iterations, the IF estimate of the MSST method is closer and closer to the signal true IF [10] so that time-frequency images with high resolution of strongly time-varying signals can be obtained.…”

Section: Time-frequency Analysis Based On Msstmentioning

confidence: 99%

“…Cai and Xiao [9] introduced generalized S transform into the fault diagnosis of rolling bearing, which can effectively enhance the resolution of the vibration signal in the time-frequency domain. Recently, Yu et al [10] proposed a novel time-frequency method named MSST, which employs an iterative reassignment procedure to improve the energy concentration of the time-frequency representation. MSST has the ability of generating better energy concentration and suppressing the cross-terms over the time-frequency plane to effectively deal with the strongly time-varying signal.…”

Section: Introductionmentioning

confidence: 99%

Fine‐Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

Sun

Gao

Lin

et al. 2019

Shock and Vibration

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To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the highresolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. en, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the finegrained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. ese results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

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“…However, the method combined chirplet transform and synchroextracting is difficult to overcome the shortcoming of SET, i.e., achieving perfect reconstruction signal. Yu [23] proposed multisynchrosqueezing transform, employing an iterative reassignment procedure to concentrate the blurry TF energy in a stepwise manner, meanwhile retaining the ability of signal reconstruction. Zhu et al [24] proposed multisynchrosqueezing chirplet transform (MSSCT), which is considered to be a combination of adaptive chirplet transform and multisynchrosqueezing transform.…”

Section: Introductionmentioning

confidence: 99%

Parameterized Local Maximum Synchrosqueezing Transform and its Application in Engineering Vibration Signal Processing

et al. 2021

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The time-frequency (TF) analysis (TFA) method is an effective tool for analyzing the time-variant features of non-stationary signals. Synchrosqueezing transform (SST) is a promising TFA method that has recently shown its usefulness in a wide range of engineering signal processing applications. On the other hand, the SST method suffers from some drawbacks, one of which is that when processing the frequency-modulated (FM) signal, the TF representation will smear heavily, which hinders its application in engineering vibration signals. In this paper, we propose a new TFA method named parameterized local maximum synchrosqueezing transform (PLMSST) to study engineering vibration signals with FM characteristics. First, the limitation of SST in signal processing is discussed. Next, we demodulate the signal by parameterizing the short-time Fourier transform (STFT) to correct the deviation of instantaneous frequency (IF) estimation. Further, we detect the local maximum of the spectrogram in the frequency direction to get the accurate IF estimate, and then obtain the energy-concentrated TF representation. Finally, we introduce the reconstruction function of this method. The performance of the proposed method is validated by both the numerical and experimental signals including vibration signals of the rolling bearing and the bridge. The results show that the proposed method is more effective in processing engineering vibration signals than other TFA methods.

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Multisynchrosqueezing Transform

Cited by 343 publications

References 28 publications

An Underdetermined Blind Source Separation Method with Application to Modal Identification

An Underdetermined Blind Source Separation Method with Application to Modal Identification

Fine‐Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

Parameterized Local Maximum Synchrosqueezing Transform and its Application in Engineering Vibration Signal Processing

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