An application of the independent component analysis to monitor acoustic emission signals generated by termite activity in wood

Measurement ◽  
2005 ◽  
Vol 37 (1) ◽  
pp. 63-76 ◽  
Author(s):  
J.J. Gonzalez de la Rosa ◽  
C.G. Puntonet ◽  
I. Lloret
2013 ◽  
Vol 373-375 ◽  
pp. 677-680
Author(s):  
Wei Li ◽  
Yu Li Gong ◽  
Yang Yu

Based on the characteristics of the acoustic emission (AE) signals from low carbon steel pitting corrosion, a new extraction method was proposed with wavelet transformation and independent component analysis. The experiment result shows that the new method can overcome the influence induced by the uncertainty of the independent source of low carbon steel pitting corrosion and good extraction result can be achieved.


2011 ◽  
Vol 142 ◽  
pp. 180-183
Author(s):  
Yang Yu ◽  
Jia Zhao

When tank bottom is detected by acoustic emission method, many corrosion acoustic emission signals can be obtained and adulterated many noise signals, which influence badly the estimation to the corrosion situation of tank bottom. In order to identify acoustic emission sources and disturbance sources without changing the characterization of acoustic emission sources, independent component analysis is used to deal with the denoising of corrosion acoustic emission signals of tank bottom in this paper. In the paper, acoustic emission signals of double exponential model is respectively mixed with white noise signals and stochastic noise signals, and acoustic emission sources and disturbance sources are respectively represented by double exponential model of acoustic emission signals and noise signals, which are independent on statistics, and then FastICA is used to simulation analysis, which is successful to identify acoustic emission signals and white noise signals. The results demonstrate that fastICA is effective to denoise acoustic emission signals.


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