Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing

He, Dong; Cao, Hongrui; Wang, Shibin

doi:10.1016/j.ymssp.2018.08.004

Cited by 157 publications

(64 citation statements)

References 49 publications

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“…ν x = d 2 λx dt 2 (t) = 0.Finally, a new second-order horizontal synchrosqueezing transform can thus be obtained using Eq. (11) by replacing the group-delay estimatort(t, ω) by our enhanced estimator given by Eq (20)…”

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confidence: 99%

Second-order Time-Reassigned Synchrosqueezing Transform: Application to Draupner Wave Analysis

Fourer

Auger

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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This paper addresses the problem of efficiently jointly representing a nonstationary multicomponent signal in time and frequency. We introduce a novel enhancement of the time-reassigned synchrosqueezing method designed to compute sharpened and reversible representations of impulsive or strongly modulated signals. After establishing theoretical relations of the new proposed method with our previous results, we illustrate in numerical experiments the improvement brought by our proposal when applied on both synthetic and real-world signals. Our experiments deal with an analysis of the Draupner wave record for which we provide pioneered time-frequency analysis results.

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confidence: 99%

Second-order Time-Reassigned Synchrosqueezing Transform: Application to Draupner Wave Analysis

Fourer

Auger

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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“…This approach requires a deep a priori knowledge of the time evolution of the fault harmonics in the current signal, which requires highly skilled maintenance personnel for implementing it and hinders its application in automated diagnostic systems. Other approach apply to the spectrogram a post-processing based on reassignment [39] or synchrosqueezing [9,40] techniques, so improving its sharpness, but these techniques add a considerable computational burden to the process of building the current spectrogram, departing from the simplicity of the STFT. Another alternative for obtaining an improved spectrogram is the Matching Pursuit approach [41,42].…”

Section: Type Of Fault Fault Harmonics Frequencymentioning

confidence: 99%

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

et al. 2019

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Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain-such as a spectrogram-is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it-short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

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“…Recently, two methods were proposed to analyze the impulse signals: time-reassigned SST (TSST) [ 12 ] and transient-extracting transform (TET) [ 13 ]. TSST can achieve a concentrated time–frequency representation for strong FM, whose ridge curves are almost parallel to the frequency axis.…”

Section: Introductionmentioning

confidence: 99%

Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

Zhao

Gelman

Chu

et al. 2020

Sensors

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It is proposed a novel instantaneous frequency estimation technology, multi-generalized demodulation transform, for non-stationary signals, whose true time variations of instantaneous frequencies are unknown and difficult to extract from the time-frequency representation due to essentially noisy environment. Theoretical bases of the novel instantaneous frequency estimation technology are created. The main innovations are summarized as: (a) novel instantaneous frequency estimation technology, multi-generalized demodulation transform, is proposed, (b) novel instantaneous frequency estimation results, obtained by simulation, for four types of amplitude and frequency modulated non-stationary single and multicomponent signals under strong background noise (signal to noise ratio is −5 dB), and (c) novel experimental instantaneous frequency estimation results for defect frequency of rolling bearings for multiple defect frequency harmonics, using the proposed technology in non-stationary conditions and in conditions of different levels of noise interference, including a strong noise interference. Quantitative instantaneous frequency estimation errors are employed to evaluate performance of the proposed IF estimation technology. Simulation and experimental estimation results show high effectiveness of the proposed estimation technology.

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Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing

Cited by 157 publications

References 49 publications

Second-order Time-Reassigned Synchrosqueezing Transform: Application to Draupner Wave Analysis

Second-order Time-Reassigned Synchrosqueezing Transform: Application to Draupner Wave Analysis

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

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