Identification of Diesel Engine Events from Acoustic Signals using Independent Component Analysis and Time-Frequency Analysis

2007 ◽  
Author(s):  
B. A. Badawi ◽  
M. Kholosy ◽  
A. A. Omer ◽  
M. A. Shahin
Keyword(s):  
Diesel Engine ◽  
Independent Component Analysis ◽  
Frequency Analysis ◽  
Acoustic Signals ◽  
Component Analysis ◽  
Independent Component ◽  
Time Frequency Analysis ◽  
Time Frequency

Vibration Analysis of Rotor System by Combined Adaptive Time-Frequency Analysis and Independent Component Analysis

2011 ◽  
Vol 474-476 ◽  
pp. 1406-1411
Author(s):  
Bin Li ◽  
Yu Guo ◽  
Yan Chun Ding ◽  
Ting Wei Liu ◽  
Jing Na ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Frequency Analysis ◽  
Component Analysis ◽  
Rotor System ◽  
Independent Component ◽  
Time Frequency Analysis ◽  
Multiple Faults ◽  
Rotor Systems ◽  
Time Frequency ◽  
Adaptive Time

Traditional time-frequency analysis methods such as short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD) cannot always work effectively for the complex rotor systems where the multiple faults are involved. A noise cancellation method for the rotor faults detection is proposed in this paper by combining the independent component analysis (ICA) scheme and the adaptive time-frequency analysis (ATFA) approach. In the proposed method, the raw picked data are first separated into different independent components (ICs) via the ICA according to the different vibration sources. Then the ICs are processed by the ATFA to obtain a clear vibration character for the fault diagnosis. Experiments on a rotor system with hybrid fault of the rotor imbalance and the bolt looseness are introduced to verify the feasibility and validity of the proposed scheme.

Separation and Reproduction of Mixed Images Using 2-D Complex Wavelet Transform

2010 ◽  
Vol 36 ◽  
pp. 466-475
Author(s):  
Tsutomu Matsuura ◽  
Amirul Faiz ◽  
Kouji Kiryu
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Wavelet Transform ◽  
Time Frequency ◽  
Frequency Information ◽  
Complex Wavelet ◽  
Complex Valued

The differences method between 1-D wavelet transform and 2-D wavelet transform in image processing is discussed. Both proposed method uses the quotient of complex valued time-frequency information of observed signals to detect the number of sources. No less number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. Using the same given Algorithm and parameters for both method, the result on separated images are compared.

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