scholarly journals Noise reduction by recycling dynamically coupled time series

2011 ◽  
Vol 21 (4) ◽  
pp. 043110 ◽  
Author(s):  
M. Eugenia Mera ◽  
Manuel Morán
Keyword(s):  
Time Series ◽  
Noise Reduction

Nonlinear noise reduction of chaotic time series based on multidimensional recurrent LS-SVM

2008 ◽  
Vol 71 (16-18) ◽  
pp. 3675-3679 ◽  
Author(s):  
Jiancheng Sun ◽  
Chongxun Zheng ◽  
Yatong Zhou ◽  
Yaohui Bai ◽  
Jianguo Luo
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Chaotic Time Series ◽  
Nonlinear Noise

A systematic approach to noise reduction in chaotic hydrological time series

1999 ◽  
Vol 219 (3-4) ◽  
pp. 103-135 ◽  
Author(s):  
B. Sivakumar ◽  
K.-K. Phoon ◽  
S.-Y. Liong ◽  
C.-Y. Liaw
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Systematic Approach

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

A SMOOTHING ALGORITHM FOR NONLINEAR TIME SERIES

2004 ◽  
Vol 14 (03) ◽  
pp. 1037-1051 ◽  
Author(s):  
S. A. BILLINGS ◽  
K. L. LEE
Keyword(s):  
Time Series ◽  
Noise Reduction ◽  
Cross Validation ◽  
User Interaction ◽  
Nonlinear Time Series ◽  
Smoothing Algorithm ◽  
Wavelet Thresholding ◽  
Numerical Examples ◽  
Validation Method

A new NARMA based smoothing algorithm is introduced for chaotic and nonchaotic time series. The new algorithm employs a cross-validation method to determine the smoother structure, requires very little user interaction, and can be combined with wavelet thresholding to further enhance the noise reduction. Numerical examples are included to illustrate the application of the new algorithm.

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