Blind Separation of Non-Stationary Convoluted Mixtures Based on Time-Frequency Analysis

2012 ◽  
Vol 538-541 ◽  
pp. 2571-2575
Author(s):  
Peng Wang ◽  
Ji Hua Cao ◽  
Xiao Chang Ni
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Varying ◽  
Blind Separation ◽  
Frequency Method ◽  
Distribution Method ◽  
Time Frequency Analysis ◽  
Joint Diagonalization ◽  
Time Frequency ◽  
Analysis Methods

The signals of convoluted mixtures have a stated of non-stationary identity, and the change of their spectrum with time-varying usually could not be observed from the frequency domain, but they can be observed by the time-frequency method. Therefore, the blind separation of non-stationary convoluted mixtures based on time-frequency analysis is proposed in this paper. For the non-stationary identity, the space-albinism of the mixed matrices and the joint diagonalization of the time-frequency matrices are simulated to separate the convoluted mixtures. Two kinds of time-frequency analysis methods, Wigner-Ville distribution and improved Wigner-Ville distribution, are introduced, which are calculated with MATLAB 7.0 software. The simulated results show the improved Wigner-Ville distribution method has a better performance for blind separating of non-stationary convoluted and mixed signals.

Time–frequency analysis methods to quantify the time-varying microstructure of sleep EEG spindles: Possibility for dementia biomarkers?

2009 ◽  
Vol 185 (1) ◽  
pp. 133-142 ◽  
Author(s):  
P.Y. Ktonas ◽  
S. Golemati ◽  
P. Xanthopoulos ◽  
V. Sakkalis ◽  
M.D. Ortigueira ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Sleep Eeg ◽  
Time Varying ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods

Reassigned Time Frequency Analysis for PD Signals in GIS

2013 ◽  
Vol 448-453 ◽  
pp. 1959-1962
Author(s):  
Hui Wang ◽  
Xiu Wei Li ◽  
Yu Xin Yun ◽  
Hai Yan Yuan
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Varying ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
The Cross ◽  
Non Stationary Signal

Partial discharge signal in GIS is a kind of typical non-stationary signal, using the time or frequency domain simply is not enough to describe the time-varying information of PD. Based on the reason above, this paper introduces a joint time-frequency analysis method according to the reassignment theory for analyzing the PD of GIS. After the processing of the PD signals simulated and on field, we conclude that this method provides a higher concentration in the time-frequency plane and reduces the most influence of the cross-interference terms.

Application of Joint-Time Frequency Analysis Methods for Nondestructive Evaluation

1999 ◽  
Author(s):  
Ki-Woo Nam ◽  
Kun-Chan Lee ◽  
Jeong-Hwan Oh
Keyword(s):  
Crack Propagation ◽  
Nondestructive Evaluation ◽  
Frequency Analysis ◽  
Cyclic Fatigue ◽  
Frequency Characteristics ◽  
Time Frequency Analysis ◽  
Ultrasonic Signals ◽  
Time Frequency ◽  
Analysis Methods ◽  
Ae Signals

Abstract Application of signal processing techniques to nondestructive evaluation (NDE) in general and acoustic emission (AE) studies in particular has become a standard tool in determining the frequency characteristics of the signals and relating these characteristics to the integrity of the structure under consideration. Recent studies have shown that the frequency characteristics of ultrasonic signals from evolving damage during cyclic (fatigue) and dynamic loads change with time; in other words, the signals are nonstationary, and that these changes can be related to the nature of the damage taking place during loading. A joint time-frequency analysis such as Short Time Fourier Transform (STFT) and Wigner-Ville distribution (WVD), can in principle be used to determine the time dependent frequency characteristics of nonstationary signals in presence of background noise. In this study these techniques are applied to analyze AE signals from fatigue crack propagation in 5083 aluminum alloys and ultrasonic signals in degraded austenitic 316 stainless steels, to study the evolution of damage in these materials. It is demonstrated that the nonstationary characteristics of both AE and ultrasonic signals could be analyzed effectively by these methods. STFT was found to be more effective in analyzing AE signals, and WVD was more effective for analyzing the attenuation and frequency characteristics of degraded materials through ultrasonics. It is indicated that the time-frequency analysis methods should also be useful in evaluating crack propagation and final fracture process resulting from various damages and defects in structural members.

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