Instantaneous frequency identification of time-varying structures by continuous wavelet transform

2013 ◽  
Vol 52 ◽  
pp. 17-25 ◽  
Author(s):  
Chao Wang ◽  
Wei-Xin Ren ◽  
Zuo-Cai Wang ◽  
Hong-Ping Zhu
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Instantaneous Frequency ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Frequency Identification

scholarly journals Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170254 ◽  
Author(s):  
Jean Baptiste Tary ◽  
Roberto Henry Herrera ◽  
Mirko van der Baan
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Signal ◽  
American Football ◽  
Frequency Resolution ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Information ◽  
Synchrosqueezing Transform

The continuous wavelet transform (CWT) has played a key role in the analysis of time-frequency information in many different fields of science and engineering. It builds on the classical short-time Fourier transform but allows for variable time-frequency resolution. Yet, interpretation of the resulting spectral decomposition is often hindered by smearing and leakage of individual frequency components. Computation of instantaneous frequencies, combined by frequency reassignment, may then be applied by highly localized techniques, such as the synchrosqueezing transform and ConceFT, in order to reduce these effects. In this paper, we present the synchrosqueezing transform together with the CWT and illustrate their relative performances using four signals from different fields, namely the LIGO signal showing gravitational waves, a ‘FanQuake’ signal displaying observed vibrations during an American football game, a seismic recording of the M w 8.2 Chiapas earthquake, Mexico, of 8 September 2017, followed by the Irma hurricane, and a volcano-seismic signal recorded at the Popocatépetl volcano showing a tremor followed by harmonic resonances. These examples illustrate how high-localization techniques improve analysis of the time-frequency information of time-varying signals. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

scholarly journals Tacholess Rotational Speed Estimation via The Integration of Continuous Wavelet Transform and Time-Dependent Autoregression

2020 ◽  
Author(s):  
Ran Zhang ◽  
Xingxing Liu ◽  
Yongjun Zheng ◽  
Haotun Lv ◽  
Baosheng Li ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Estimation Method ◽  
Rotational Frequency ◽  
Time Dependent ◽  
Speed Estimation ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Vibration Sensors ◽  
Preprocessing Technique

Abstract Speed estimation is crucial to monitor the conditions of rotational machinery. Most speed measurements are carried out by installing encoders or tachometers inside the machines. In many cases, such method could be cumbersome or even inaccessible. This paper proposes a vibration-based speed estimation method. The vibration sensors are often cheaper and easier to install than angle encoders. In the proposed method, the continuous wavelet transform (CWT) is used as a preprocessing technique to extract the signal of importance. Then, the time-varying autoregressive (TAR) model is applied to analyze the rotational frequency. Additionally, the paper presents a fast algorithm for implementation. The proposed method is validated by both synthetic and empirical data.

Continuous wavelet transform for time-varying motion extraction

2010 ◽  
Vol 4 (4) ◽  
pp. 271 ◽  
Author(s):  
A. Briassouli ◽  
D. Matsiki ◽  
I. Kompatsiaris
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Motion Extraction

scholarly journals Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

1999 ◽  
Vol 42 (3) ◽  
Author(s):  
T. Bartosch ◽  
D. Seidl
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Time Varying ◽  
Continuous Wavelet ◽  
Short Time Fourier Transform ◽  
Array Data ◽  
Definition Of ◽  
Short Time

Among a variety of spectrogram methods Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were selected to analyse transients in non-stationary tremor signals. Depending on the properties of the tremor signal a more suitable representation of the signal is gained by CWT. Three selected broadband tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli.

Instantaneous frequency estimation and representation of the audio signal through Complex Wavelet Additive Synthesis

2014 ◽  
Vol 12 (03) ◽  
pp. 1450030 ◽  
Author(s):  
Jesús Ponce de León ◽  
José Ramón Beltrán ◽  
Fernando Beltrán
Keyword(s):  
Wavelet Transform ◽  
Comparative Study ◽  
Continuous Wavelet Transform ◽  
Instantaneous Frequency ◽  
Audio Signal ◽  
Frequency Resolution ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Complex Wavelet ◽  
Additive Synthesis

In this work, an improvement of the Complex Wavelet Additive Synthesis (CWAS) algorithm is presented. This algorithm is based on a discrete version of the Complex Continuous Wavelet Transform (CCWT) which analyzes the input signal in a frame-to-frame approach and under variable frequency resolution per octave. After summarizing several Time-Frequency Distributions (TFD), concretely the standard Short Time Fourier Transform (STFT), the Pseudo Wigner–Ville Distribution (PWVD), reassignment and complex wavelets, a comparative study of the accuracy in the instantaneous frequency (IF) estimation is shown. The comparative study includes three different signal processing tools (based on the summarized TFD): the Time-Frequency Toolbox (TFTB) of François Auger, the High Resolution Spectrographic Routines (HRSR) of Sean Fulop and the proposed CWAS algorithm. A set of eight synthetic signals have been analyzed using six different methods: the regular STFT spectrogram, the PWVD, their corresponding reassigned versions, the Nelson crossed spectrum method and finally the Complex Continuous Wavelet Transform (CCWT). Finally, two- and three-dimensional Time-Frequency representations of the IF provided by the CWAS algorithm are presented.

Sign in / Sign up

Export Citation Format

Share Document