Time frequency-based blind source separation technique for elimination of cross-terms in Wigner distribution

2003 ◽  
Vol 39 (5) ◽  
pp. 475 ◽  
Author(s):  
J.B. Wu ◽  
J. Chen ◽  
P. Zhong
Keyword(s):  
Blind Source Separation ◽  
Wigner Distribution ◽  
Source Separation ◽  
Separation Technique ◽  
Time Frequency ◽  
Cross Terms

Higher-order time frequency-based blind source separation technique

2000 ◽  
Vol 7 (7) ◽  
pp. 193-196 ◽  
Author(s):  
A.R. Leyman ◽  
Z.M. Kamran ◽  
K. Abed-Meraim
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Higher Order ◽  
Separation Technique ◽  
Time Frequency

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

Mathematical view of a blind source separation on a time frequency space

2007 ◽  
Vol 187 (1) ◽  
pp. 153-162 ◽  
Author(s):  
Keiko Fujita ◽  
Yoshitsugu Takei ◽  
Akira Morimoto ◽  
Ryuichi Ashino
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Frequency Space ◽  
Time Frequency

Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2831-2847 ◽  
Author(s):  
Y Zhou ◽  
J Chen ◽  
G M Dong ◽  
W B Xiao ◽  
Z Y Wang
Keyword(s):  
Fault Diagnosis ◽  
Spectral Density ◽  
Wigner Distribution ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Accurate Analysis ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Cross Terms

The vibration signals of rolling element bearings are random cyclostationary when they have faults. Also, statistical properties of the signals change periodically with time. The accurate analysis of time-varying signals is an essential pre-requisite for the fault diagnosis and hence safe operation of rolling element bearings. The Wigner distribution is probably most widely used among the Cohen’s class in order to describe how the spectral content of a signal changes over time. However, the basic nature of such signals causes significant interfering cross-terms, which do not permit a straightforward interpretation of the energy distribution. To overcome this difficulty, the Wigner–Ville distribution (WVD) based on the cyclic spectral density (CSD) is discussed in this article. It is shown that the improved WVD, based on CSD of a long time series, can render the time–frequency distribution less susceptible to noise, and restrain the cross-terms in the time–frequency domain. Simulation and experiment of the rolling element-bearing fault diagnosis are performed, and the results indicate the validity of WVD based on CSD in time–frequency analysis for bearing fault detection.

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