High-frequency compensation for seismic data based on adaptive generalized S transform

Li, Huifeng; Wang, Jin; Wei, Zhengrong; Fang, Yang

doi:10.1007/s11770-020-0860-4

Cited by 2 publications

(2 citation statements)

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“…Based on equation (19), we can draw two conclusions: (1) The IF f 0 of the signal x (t) and the harmonics that make up the TFR of the AGST are the same. (2) The magnitude of the TF coefficients |GST (t, f)| at each time reaches its maximum value A • ĝ (0) when f = f 0 .…”

Section: Introducing Synchronous Extractionmentioning

confidence: 99%

“…Xue et al [18] utilized a first-order function of frequency with two parameters to modified the window function S transform. Li et al [19] modified the TF spectrum of the adaptive GST of seismic signals by utilizing the optimal Gauss window function to construct an amplitude spectrum compensation function. Nowadays, the GST is widely used in dealing with nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

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A novel time-frequency analysis method for fault diagnosis based on generalized S-transform and synchroextracting transform

Wang,

Fang,

Wang

et al. 2023

Meas. Sci. Technol.

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Rotating machinery usually operates under variable-speed conditions, and how to effectively handle nonstationary signal in fault diagnosis is a critical task. The time-frequency analysis (TFA) method is widely used in the processing of nonstationary signal. To improve the time-frequency resolution and clearly identify instantaneous frequency (IF) characteristics, the adaptive generalized S-synchroextracting transform (AGSSET), which is a novel TFA method proposed in this paper. Firstly, a new transform named adaptive generalized S-transform (AGST) is put forward by optimizing the window function of generalized S transform. In this paper, an adaptive window function optimization method based on the frequency spectrum of the vibration signal is introduced, and the energy concentration measure is used to determine the window function’s parameters in AGST. Simultaneously, the synchrony extraction idea is incorporated into the AGST, then the AGSSET is derived. To address more complex IF characteristics, the synchronous extraction operator (SEO) is reconstructed. In the simulation experiment, the GMLC signal model is selected to represent nonstationary signal and to verify the effectiveness of the proposed method. In addition, bearing fault data is also used for fault diagnosis experiments. The results of both numerical simulation and experimental analysis indicate that AGSSET performs well in identifying the time-varying IF characteristic in nonstationary signals. It can also efficiently detect faults with high accuracy and strong stability.

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Section: Introducing Synchronous Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%