Time-Frequency Analysis of Pressure Fluctuations on a Hydrofoil Undergoing a Transient Pitching Motion Using Hilbert-Huang and Teager-Huang Transforms

2007 ◽  
Author(s):  
S. Benramdane ◽  
J. C. Cexus ◽  
A. O. Boudraa ◽  
J.-A. Astolfi
Keyword(s):  
Frequency Analysis ◽  
Pressure Coefficient ◽  
Pressure Fluctuations ◽  
Low Frequency ◽  
Suction Side ◽  
Intrinsic Mode Functions ◽  
Pitching Motion ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
The Hilbert Transform

In this paper, time-frequency analysis of wall pressure signals of a hydrofoil’s suction side undergoing a forced transient pitching motion with incoming flow is conducted. A novel method recently introduced by Huang et al., the Empirical Mode Decomposition (EMD), is first used to decompose resulting non-stationary signals into frequency sub-band components called Intrinsic Mode Functions (IMFs). EMD-filtered pressure coefficient signals are then reconstructed from few selected IMFs from low frequency modes and time-frequency analysis performed on high frequency modes. For this latter purpose, two analysis methods are used. The first one consists in demodulating IMFs into their Instantaneous Amplitude (IA) and Instantaneous Frequency (IF) using the Hilbert transform and the second one is based on the Teager energy tracking operator (TEO). The transition occurrence is analyzed using IA and IF of extracted IMFs from chordwise pressure transducer’s signals. This transition occurrence is then described in time-frequency domain.

Time-Frequency Analysis for Power System Oscillations

2013 ◽  
Vol 336-338 ◽  
pp. 928-931
Author(s):  
Chia Liang Lu ◽  
Pei Hwa Huang
Keyword(s):  
Power Systems ◽  
Power System ◽  
Frequency Analysis ◽  
Low Frequency ◽  
Normal Operation ◽  
Intrinsic Mode Functions ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Low Frequency Oscillations

Low frequency oscillations due to the lack of damping may occur in power systems under normal operation and will cause system instability. These oscillations are essentially nonlinear power responses which are difficult to extract the inherent characteristics by the time domain method. This paper aims to analyze nonlinear power responses by using the Hilbert-Huang transform (HHT) which is a time-frequency signal processing method which comprises steps of the empirical mode decomposition and the Hilbert transform. Dynamic power system responses, including generator output power and line power are to be processed by the HHT and a set of intrinsic mode functions and the associated Hilbert spectrum are obtained. The generator with most effects on the system will be accordingly found out through the time-frequency analysis and the power system stabilizer will be placed at the generator. Numerical results from a sample power system are demonstrated to show the validity of the time-frequency approach in the study of power system low frequency oscillations.

ICE Fault Diagnosis Method Based on Mutual Information and WVD Time-Frequency Analysis

2014 ◽  
Vol 525 ◽  
pp. 741-745 ◽  
Author(s):  
Xin Jun Wang ◽  
Yan Ping Cai ◽  
Xu Ze Lin
Keyword(s):  
Mutual Information ◽  
Frequency Analysis ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Modal Component ◽  
Diagnosis Method ◽  
Feature Information ◽  
Signal Time

A new time-frequency analysis method based on mutual information and EMD-WVD time-frequency analysis is proposed. The method uses EMD to decompose signals into independent intrinsic mode functions (IMF), uses the mutual information of EMD intrinsic mode components and original signal as basis to determine EMD false modal component, and uses WVD of EMD true Intrinsic Mode to reconstruct the WVD of original signal for feature information extraction. ICE fault signal time-frequency analysis results show that the proposed method can effectively improve the accuracy of WVD time-frequency analysis, and correctly analyze characteristics of the signal.

Applications of variational mode decomposition in seismic time-frequency analysis

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V365-V378 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Yangkang Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Synthetic Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Decomposition Approach ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition

We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.

scholarly journals Application of S-Transform in ISAR Imaging

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 676 ◽  
Author(s):  
Bo Zang ◽  
Mingzhe Zhu ◽  
Xianda Zhou ◽  
Lu Zhong
Keyword(s):  
Frequency Analysis ◽  
Low Frequency ◽  
Frequency Region ◽  
Echo Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Isar Imaging ◽  
Body Components ◽  
Over Time

In inverse synthetic aperture radar (ISAR) imaging, time-frequency analysis is the basic method for processing echo signals, which are reflected by the results of time-frequency analysis as each component changes over time. In the time-frequency map, a target’s rigid body components will appear as a series of single-frequency signals in the low-frequency region, and the micro-Doppler components generated by the target’s moving parts will be distributed in the high-frequency region with obvious frequency modulation. Among various time-frequency analysis methods, S-transform is especially suitable for analyzing these radar echo signals with micro-Doppler (m-D) components because of its multiresolution characteristics. In this paper, S-transform and the corresponding synchrosqueezing method are used to analyze the ISAR echo signal and perform imaging. Synchrosqueezing is a post-processing method for the time-frequency analysis result, which could retain most merits of S-transform while significantly improving the readability of the S-transformation result. The results of various simulations and actual data will show that S-transform is highly matched with the echo signal for ISAR imaging: the better frequency-domain resolution at low frequencies can concentrate the energy of the rigid body components in the low-frequency region, and better time resolution at high frequencies can better describe the transformation of the m-D component over time. The combination with synchrosqueezing also significantly improves the effect of time-frequency analysis and final imaging, and alleviates the shortcomings of the original S-transform. These results will be able to play a role in subsequent work like feature extraction and parameter estimation.

An Approach of Signal Analysis Based on High-Order Spline Interpolation

2012 ◽  
Vol 507 ◽  
pp. 226-230
Author(s):  
Jian Jun Li ◽  
Xi Bing Li ◽  
Hong You ◽  
Cheng Liu
Keyword(s):  
Frequency Analysis ◽  
Spline Interpolation ◽  
High Order ◽  
Hilbert Transformation ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Order Spline

Empirical mode decomposition (EMD) provides a powerful tool for the nonlinear and nonstationary signals. This paper presents an application of the signature analysis based on high-order spline interpolation. The time-frequency analysis method based on EMD is introduced. The series data are separated into intrinsic mode functions (IMFs) with different time scale using EMD. The spectrum of Hilbert transformation is obtained by applying Hilbert transformation to every IMF. Based on cubic spline interpolation, high-order spline interpolation is used to improve the precision of the algorithm. Furthermore some strategies for improving the computational efficiency are proposed. The simulation result shows that the precision of the time-frequency analysis can be improved effectively using the proposed new algorithm.

Time-frequency analysis of seismic data using local attributes

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. P23-P34 ◽  
Author(s):  
Guochang Liu ◽  
Sergey Fomel ◽  
Xiaohong Chen
Keyword(s):  
Frequency Analysis ◽  
Seismic Data ◽  
Low Frequency ◽  
Average Frequency ◽  
Time Varying ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Frequency Map

Time-frequency analysis is an important technology in seismic data processing and interpretation. To localize frequency content in time, we have developed a novel method for computing a time-frequency map for nonstationary signals using an iterative inversion framework. We calculated time-varying Fourier coefficients by solving a least-squares problem that uses regularized nonstationary regression. We defined the time-frequency map as the norm of time-varying coefficients. Time-varying average frequency of the seismic data can also be estimated from the time-frequency map calculated by our method. We tested the method on benchmark synthetic signals and compared it with the well-known S-transform. Two field data examples showed applications of the proposed method for delineation of sand channels and for detection of low-frequency anomalies.

High Resolution Time-Frequency Analysis Base on Ricker Atom Matching Pursuit Decomposition

2013 ◽  
Vol 284-287 ◽  
pp. 3115-3119
Author(s):  
Wei Song ◽  
Jia Hui Zuo ◽  
Peng Cheng Hu
Keyword(s):  
Frequency Analysis ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Seismic Signal ◽  
Dominant Frequency ◽  
Frequency Resolution ◽  
Frequency Variation ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
The Impact

The high accuracy time-frequency representation of non-stationary signals is one of the key researches in seismic signal analysis. Low-frequency part of the seismic data often has a higher frequency resolution, on the contrary it tends to have lower frequency resolution in the high frequency part. It’s difficult to fine characterize the time-frequency variation of non-stationary seismic signals by conventional time-frequency analysis methods due to the limitation of the window function. Therefore based on the Ricker wavelet, we put forward the matching pursuit seismic trace decomposition method. It decomposes the seismic records into a series of single component atoms with different centre time, dominant frequency and energy, by making use of the Wigner-Ville distribution, has the time-frequency resolution of seismic signal reach the limiting resolution of the uncertainty principle and skillfully avoid the impact of interference terms in conventional Wigner-Ville distribution.

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