Time Frequency Analysis of Electrooculograph (EOG) Signal of Eye Movement Potentials Based on Wavelet Energy Distribution

2011 ◽  
Author(s):  
W.M. Bukhari W. Daud ◽  
Rubita Sudirman
Keyword(s):  
Energy Distribution ◽  
Eye Movement ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Wavelet Energy

An Investigation of Tooth Wear Diagnosis of Gear Based on Process Systems Modeling

2012 ◽  
Vol 549 ◽  
pp. 834-838
Author(s):  
Feng Li Wang ◽  
Hui Xing
Keyword(s):  
Energy Distribution ◽  
Frequency Spectrum ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Tooth Wear ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Gear Fault ◽  
Local Wave ◽  
Instantaneous Energy

Targeting the advantages of local wave analysis(LWA) and the characteristics of gear fault vibration signals, LWA is introduced into gear fault diagnosis. The concept of the instantaneous energy in time- frequency analysis, based on local wave time-frequency spectrum, was used to measure the energy distribution of the signal in time-frequency domain. Furthermore, when tooth wear occurs in gear, the energy of the gear vibration signal would change correspondingly, whilst local wave time-frequency spectrum can exactly provide the instantaneous energy distribution of the signal with the change of the time and frequency. Thus, the fault information of the gear vibration signal can be extracted effectively from the local wave time-frequency spectrum. The analysis results from the experimental signals show that local wave time-frequency analysis could extract the characteristics information of the gear fault vibration signal effectively.

Modal Analysis of a Bridge During High-Speed Train Passages by Enhanced Variational Mode Decomposition

2020 ◽  
Vol 20 (13) ◽  
pp. 2041002
Author(s):  
Xiao-Mei Yang ◽  
Chun-Xu Qu ◽  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Hua Liu
Keyword(s):  
Energy Distribution ◽  
Modal Analysis ◽  
Frequency Analysis ◽  
High Speed ◽  
Large Scale ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Variations

Modal analysis of bridge under high-speed trains is essential to the design and health monitoring of bridge, but it is difficult to be implemented since the vehicle–bridge interaction (VBI) effect is involved. In this paper, the time–frequency analysis technique is performed on the non-stationary train-induced bridge responses to estimate the frequency variations. To suppress the interference terms in time–frequency analysis but preserve the time-variant characteristics of responses, the enhanced variational mode decomposition (VMD) is proposed, which is used to decompose the train-induced dynamic response into many of envelope-normalized intrinsic mode functions (IMFs). Then the short-time Fourier transform is applied to observe the time–frequency energy distribution of each IMF. The train-induced bridge signals measured from a large-scale high-speed railway bridge are analyzed in this paper. The IMFs associated with the pseudo-frequencies caused by train or the resonant frequencies of bridge are distinguished. And, frequency variations are captured from the time–frequency energy distributions of envelope-normalized IMFs. The results show the proposed method can extract the frequency variations of low-energy IMFs effectively, which are hard to be observed from the time–frequency energy distribution of train-induced bridge response. The instantaneous frequency characteristics extracted from the train-induced bridge response could be the important support for investigating the VBI effect of train–bridge system.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency

Anti-Radiation-Missile detection based on parameterized time-frequency analysis

2010 ◽  
Vol 30 (11) ◽  
pp. 3108-3110
Author(s):  
Xiao-ming LIU ◽  
Jian-dong WANG ◽  
Xu-dong WANG
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency
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