Advances in Extraction of Signal From Ground Motion Time Histories Using Time-Frequency Analysis

2018 ◽  
pp. 1-30
Author(s):  
Vaneeta Devi ◽  
M. L. Sharma
Keyword(s):  
Ground Motion ◽  
Frequency Analysis ◽  
Earthquake Engineering ◽  
Spectral Estimation ◽  
Time History ◽  
Time Frequency Analysis ◽  
Shape Distribution ◽  
Time Frequency ◽  
Motion Time ◽  
Time Histories

Time-frequency representation and spectral features extraction from a digitally recorded ground motion time history of an earthquake is cornerstone in earthquake engineering signal processing and interpretation. Recently developed time-frequency analysis (TFA) techniques are one of the most suitable techniques for the spectral estimation of signals whose frequency content varies with time. The most often used TFA techniques are short-term Fourier transform, Gabor transform, wavelet transform, Wigner-Ville distribution, Choi-William distribution, and cone shape distribution. The spectrograms of TFA reveal better spectral estimation in time-frequency domain and hence recommended to estimate local frequencies, dominate frequency and their incident time. Moreover, the time of occurrence of frequency component corresponding to maximum energy burst as well as its variation with time can also be identified. Results obtained from TFA techniques shows better picture of the spectral content in the data than the other conventional techniques.

scholarly journals Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4102
Author(s):  
Tomas A. Garcia-Calva ◽  
Daniel Morinigo-Sotelo ◽  
Oscar Duque-Perez ◽  
Arturo Garcia-Perez ◽  
Rene de J. Romero-Troncoso
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
Induction Motors ◽  
Spectral Estimation ◽  
Diagnostic Techniques ◽  
Frequency Resolution ◽  
Minimum Norm ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Conditions

In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.

Wavelet Packet Characterization of Scenario Earthquake Ground Motions

2017 ◽  
Vol 11 (03) ◽  
pp. 1750006
Author(s):  
Ajin Baby ◽  
Manish Shrikhande
Keyword(s):  
Ground Motion ◽  
Prediction Models ◽  
Response Spectrum ◽  
Wavelet Packet ◽  
Time History ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Scenario Earthquake ◽  
Motion Time ◽  
Time Histories

With increased emphasis on performance-based seismic design, the need for appropriate ground motion time histories for use in nonlinear dynamic analyses is felt accutely. However, it is generally not possible to get a suitable recorded time history consistent with the estimated hazard at a specific site. The ground motion prediction models are therefore derived/developed from a statistical analysis of recorded ground motion for a variety of source and site conditions to address this need. Most often, the ground motion prediction models are developed to model the response spectrum amplitudes at a set of natural periods and the ground motion time history, if required, is then generated to be consistent with this predicted response spectrum. These simulated time histories often lack in modeling the wave arrivals and temporal variation in the distribution of energy with respect to frequency. In this paper, we present a wavelet-based ground motion prediction model for directly generating ground motion time history that is consistent with the postulated scenario earthquake at a site.

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
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