An adaptive time-frequency filtering method for nonstationary signals based on the generalized S-transform

2010 ◽  
Vol 6 (2) ◽  
pp. 133-136
Author(s):  
Dian-wei Wang ◽  
Yan-jun Li ◽  
Ke Zhang ◽  
Huan-min Xu
Keyword(s):  
Frequency Filtering ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Filtering Method ◽  
S Transform ◽  
Generalized S Transform ◽  
Adaptive Time

A Time-Frequency Filtering Method Based on Generalized S Transform and its Application in Machinery Fault Diagnosis

2012 ◽  
Vol 157-158 ◽  
pp. 531-537 ◽  
Author(s):  
Xiu Wen Li ◽  
Jian Hong Yang ◽  
Min Li ◽  
Jin Wu Xu
Keyword(s):  
Frequency Resolution ◽  
Good Effect ◽  
Frequency Filtering ◽  
Cross Term ◽  
Signal Energy ◽  
Time Frequency ◽  
Filtering Method ◽  
S Transform ◽  
Generalized S Transform ◽  
Machinery Fault Diagnosis

Aimed at the problem of low resolution and cross term interference of the traditional time-frequency analysis methods, a new time-frequency filtering method based on generalized S transform is proposed. The method is extended under the premise of the linearity, lossless invertibility, high time-frequency resolution of S transform. On the basis, a coefficient which is direct to the signal energy distribution is introduced. In this way, the resolution of the S transform can be adjust adaptively. Eventually, this method is applied to the time-frequency filtering. The results of simulation and faulty bearing show that the proposed methodology can achieve good effect of noise reduction, and be more suitable for the non-stationary characteristics of vibration signals.

scholarly journals S-TRANSFORM RIDGE FILTERING

2019 ◽  
Vol 2 (3) ◽  
pp. 168-173
Author(s):  
Aleksander Serdyukov ◽  
Anton Azarov ◽  
Alexandr Yablokov
Keyword(s):  
Seismic Data ◽  
Point Of View ◽  
Time Averaging ◽  
Signal Recovery ◽  
Frequency Filtering ◽  
Problem Of Time ◽  
Time Frequency ◽  
Filtering Method ◽  
S Transform ◽  
Temporal Localization

The problem of time-frequency filtering of seismic data on the basis of S-conversion is considered. S-transform provides a frequency-dependent resolution, while maintaining a direct connection with the Fourier spectrum. S-conversion is widely used in seismic processing. The standard filtering method based on S-conversion is based on its reversibility. From the point of view of temporal localization, this method is not optimal, since the calculation of the inverse S-transform includes time averaging. We propose an alternative filtering method based on signal recovery from S-transform peaks.

scholarly journals The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 769 ◽  
Author(s):  
Hui Chen ◽  
Lingqi Lu ◽  
Dan Xu ◽  
Jiaxing Kang ◽  
Xuping Chen
Keyword(s):  
High Precision ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Precision Time

scholarly journals Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S-Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Bin Liu ◽  
Youqian Feng ◽  
Zhonghai Yin ◽  
Xiangyu Fan
Keyword(s):  
Empirical Mode Decomposition ◽  
Prior Information ◽  
Ensemble Empirical Mode Decomposition ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Mode Decomposition ◽  
S Transform ◽  
Emitter Recognition ◽  
Generalized S Transform

Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.

Gear fault diagnosis based on time–frequency domain de-noising using the generalized S transform

2017 ◽  
Vol 24 (15) ◽  
pp. 3338-3347 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Time Frequency ◽  
Transmission Modes ◽  
S Transform ◽  
Mining Machinery ◽  
Gear Fault ◽  
Generalized S Transform ◽  
Gear Fault Diagnosis

Gears are the most important transmission modes used in mining machinery, and gear faults can cause serious damage and even accidents. In the work process, vibration signals are influenced not only by friction, nonlinear stiffness, and nonstationary loads, but also by strong noise. It is difficult to separate the useful information from the noise, which brings some trouble to the fault diagnosis of mining machinery gears. The generalized S transform has the advantages of the short time Fourier transform and wavelet transform and is reversible. The time–frequency energy distribution of the gear vibration signal can be accurately presented by the generalized S transform, and a time–frequency filter factor can be constructed to filter the vibration signal in the time–frequency domain. These characteristics play an important role when the generalized S transform is used to remove the noise in the time–frequency domain. In this paper, a new gear fault diagnosis based on the time–frequency domain de-noising is proposed that uses the generalized S transform. The application principle, method steps, and evaluation index of the method are presented, and a wavelet soft-threshold filtering method is implemented for comparison with the proposed approach. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a gear with a tooth crack. Our analyses also indicate that the proposed method can be used for fault diagnosis of mining machinery gears.

Sign in / Sign up

Export Citation Format

Share Document