New approach for blind source separation using time-frequency distributions

1996 ◽  
Author(s):  
Adel Belouchrani ◽  
Moeness G. Amin
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
New Approach ◽  
Frequency Distributions ◽  
Time Frequency

Application of Time-Frequency Distributions to the Blind Source Separation of Mechanical Fault Signals

2011 ◽  
Vol 383-390 ◽  
pp. 395-399
Author(s):  
Zhi Hua Hao ◽  
Hong Xia Tian ◽  
Li Xin Tian
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Fault Classification ◽  
Vibration Signals ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Stationary Vibration ◽  
Ar Modeling ◽  
Mutually Independent ◽  
Separation Results

In this study, blind source separation (BSS) method was applied to separate the multi-channel fault vibration signals generated by a rotor. As the signals were non-stationary, an algorithm based on spatial time-frequency distributions was applied to the experimental vibration signals to obtain the non-stationary vibration sources that were mutually independent. Further, AR modeling estimates of these sources were calculated with BURG method. A neural network was applied to the AR modeling parameters to perform the fault classification. The separation results of an experiment on a rotor’s multi-fault show that this method is feasible for fault diagnosis.

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