Study on Denoising of Corrosion Acoustic Emission Signals of Tank Bottom Based on Independent Component Analysis

2011 ◽  
Vol 142 ◽  
pp. 180-183
Author(s):  
Yang Yu ◽  
Jia Zhao
Keyword(s):  
Acoustic Emission ◽  
Independent Component Analysis ◽  
White Noise ◽  
Component Analysis ◽  
Exponential Model ◽  
Independent Component ◽  
Emission Sources ◽  
Double Exponential ◽  
Tank Bottom ◽  
Double Exponential Model

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.

Feature Extraction of the Acoustic Emission Signals of Low Carbon Steel Pitting Corrosion Based on Independent Component Analysis and Wavelet Transform

2013 ◽  
Vol 373-375 ◽  
pp. 677-680
Author(s):  
Wei Li ◽  
Yu Li Gong ◽  
Yang Yu
Keyword(s):  
Acoustic Emission ◽  
Carbon Steel ◽  
Independent Component Analysis ◽  
Pitting Corrosion ◽  
Wavelet Transformation ◽  
Low Carbon Steel ◽  
Component Analysis ◽  
Independent Component ◽  
Low Carbon ◽  
Ae Signals

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.

scholarly journals Spatiotemporal Filtering and Noise Analysis for Regional GNSS Network in Antarctica Using Independent Component Analysis

2019 ◽  
Vol 11 (4) ◽  
pp. 386 ◽  
Author(s):  
Wenhao Li ◽  
Fei Li ◽  
Shengkai Zhang ◽  
Jintao Lei ◽  
Qingchuan Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
White Noise ◽  
Flicker Noise ◽  
Component Analysis ◽  
Independent Component ◽  
Noise Model ◽  
Residual Time ◽  
Noise Characteristics ◽  
Noise Models

The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.

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