The Evolution of US and UK Real GDP Components in the Time-Frequency Domain: A Continuous Wavelet Analysis

2021 ◽  
Author(s):  
Patrick M. Crowley ◽  
Andrew Hughes Hallett
Keyword(s):  
Wavelet Analysis ◽  
Frequency Domain ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Real Gdp

scholarly journals Fiscal policy tracking design in the time–frequency domain using wavelet analysis

2015 ◽  
Vol 51 ◽  
pp. 502-514 ◽  
Author(s):  
Patrick M. Crowley ◽  
David Hudgins
Keyword(s):  
Fiscal Policy ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Time Frequency

scholarly journals Time Frequency Analysis of Wavelet and Fourier Transform

2020 ◽  
Author(s):  
Karlton Wirsing
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Efficient Access ◽  
The Fourier Transform

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.

scholarly journals Vibration Signature Analysis and Parameter Extractions on Damages in Gears and Rolling Element Bearings

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Chia-Hsuan Shen ◽  
Jie Wen ◽  
Pirapat Arunyanart ◽  
Fred K. Choy
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Continuous Wavelet Transform ◽  
Gear Tooth ◽  
Continuous Wavelet ◽  
Rolling Element Bearings ◽  
Damage Scenarios ◽  
Time Frequency ◽  
Rolling Element ◽  
Machine Health Monitoring

This paper is to analyze and identify damage in gear teeth and rolling element bearings by establishing pattern feature parameters from vibration signatures. In the present work, different damage scenarios involving different combinations of gear tooth damage, bearing damage are considered. Each of the damage scenarios are studied and compared in the time domain, the frequency domain, and the joint time-frequency domain using the FM0 technique, the Fourier Transform, the Wigner-Ville Transform, and the Continuous Wavelet Transform, respectively. Results obtained from the three different signal domains are analyzed to develop indicative parameters and visual presentations that measure the integrity and wellness of the bearing and gear components. The joint time-frequency domain obtained from the continuous wavelet transform has shown to be a superior technique for providing clear visual examination solution for different types of component damages as well as for feature extractions used for computer-based machine health monitoring solution.

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