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Noise smoothing for structural vibration test signals using an improved wavelet thresholding technique.

Yi TH, Li HN, Zhao XY - Sensors (Basel) (2012)

Bottom Line: In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered.To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals.The procedure is validated by using four benchmarks signals with three degrees of degradation as well as a real measured signal obtained from a three-story reinforced concrete scale model shaking table experiment.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Infrastructure Engineering, State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116023, China. yth@dlut.edu.cn

ABSTRACT
In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered. To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals. The denoising performance of DWT is discussed by several processing parameters, including the type of wavelet, decomposition level, thresholding method, and threshold selection rules. To overcome the disadvantages of the traditional hard- and soft-thresholding methods, an improved thresholding technique called the sigmoid function-based thresholding scheme is presented. The procedure is validated by using four benchmarks signals with three degrees of degradation as well as a real measured signal obtained from a three-story reinforced concrete scale model shaking table experiment. The performance of the proposed method is evaluated by computing the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) after denoising. Results reveal that the proposed method offers superior performance than the traditional methods no matter whether the signals have heavy or light noises embedded.

No MeSH data available.


Plot of the modified sigmoid function.
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getmorefigures.php?uid=PMC3472880&req=5

f3-sensors-12-11205: Plot of the modified sigmoid function.

Mentions: Figure 3 shows the typical plot of the modified sigmoid function with β equal to 0.05, 0.1, 0.5, 1, 2, 5, 10 and 100.


Noise smoothing for structural vibration test signals using an improved wavelet thresholding technique.

Yi TH, Li HN, Zhao XY - Sensors (Basel) (2012)

Plot of the modified sigmoid function.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472880&req=5

f3-sensors-12-11205: Plot of the modified sigmoid function.
Mentions: Figure 3 shows the typical plot of the modified sigmoid function with β equal to 0.05, 0.1, 0.5, 1, 2, 5, 10 and 100.

Bottom Line: In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered.To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals.The procedure is validated by using four benchmarks signals with three degrees of degradation as well as a real measured signal obtained from a three-story reinforced concrete scale model shaking table experiment.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Infrastructure Engineering, State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116023, China. yth@dlut.edu.cn

ABSTRACT
In structural vibration tests, one of the main factors which disturb the reliability and accuracy of the results are the noise signals encountered. To overcome this deficiency, this paper presents a discrete wavelet transform (DWT) approach to denoise the measured signals. The denoising performance of DWT is discussed by several processing parameters, including the type of wavelet, decomposition level, thresholding method, and threshold selection rules. To overcome the disadvantages of the traditional hard- and soft-thresholding methods, an improved thresholding technique called the sigmoid function-based thresholding scheme is presented. The procedure is validated by using four benchmarks signals with three degrees of degradation as well as a real measured signal obtained from a three-story reinforced concrete scale model shaking table experiment. The performance of the proposed method is evaluated by computing the signal-to-noise ratio (SNR) and the root-mean-square error (RMSE) after denoising. Results reveal that the proposed method offers superior performance than the traditional methods no matter whether the signals have heavy or light noises embedded.

No MeSH data available.