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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.


Benchmark signals denoising by db4 wavelet using different thresholding schemes (SNR = 2.0000), (a) Blocks signal; (b) Bumps signal; (c) Heavy sine signal; (d) Doppler signal.
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f6-sensors-12-11205: Benchmark signals denoising by db4 wavelet using different thresholding schemes (SNR = 2.0000), (a) Blocks signal; (b) Bumps signal; (c) Heavy sine signal; (d) Doppler signal.

Mentions: The detailed results of the three thresholding methods are illustrated in Table 1. As known, sensitivity in the vibration tests could be enhanced by increasing SNR. It can be seen from the table, decrease of the RMSE of residuals and increase of sensitivity are achieved simultaneously. All of the applied thresholding approaches provide at least two-fold improvements in relation to the initial SNR value. One can also see from the comparison, the SNR obtained by using the sigmoid function based thresholding scheme is higher than the hard- and soft-thresholding (at least the same as hard thresholding). This trend is more evident when the light blurred case is considered which reveals that the sigmoid function based thresholding scheme to be an effective filter to some extent, no matter whether the SNR is high or low. The four kinds of noisy signals with the SNR at 2.0000 dB and 10.0000 dB as well as their processed results are displayed in Figures 6 and 7. In the two figures, there are four sub-figures representing the four benchmark signals, respectively. It can be visually appreciated that a great amount of noise has been suppressed. In heavy blurred cases the huge noise is removed and the outline of benchmark signals is recovered; moreover, in light blurred cases the detail of benchmark signals are only slightly harmed after thresholding. Comparing the four sub-figures in Figure 7, one can easily see that the thresholding scheme is very important. The curves obtained by the proposed procedure are more similar to the original signals. This is due to the sigmoid function based thresholding scheme is flexible and adaptive, while the hard- and soft-thresholding method are coarse and rough, hence the proposed method performs the better performance to simulations.


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

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

Benchmark signals denoising by db4 wavelet using different thresholding schemes (SNR = 2.0000), (a) Blocks signal; (b) Bumps signal; (c) Heavy sine signal; (d) Doppler signal.
© Copyright Policy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC3472880&req=5

f6-sensors-12-11205: Benchmark signals denoising by db4 wavelet using different thresholding schemes (SNR = 2.0000), (a) Blocks signal; (b) Bumps signal; (c) Heavy sine signal; (d) Doppler signal.
Mentions: The detailed results of the three thresholding methods are illustrated in Table 1. As known, sensitivity in the vibration tests could be enhanced by increasing SNR. It can be seen from the table, decrease of the RMSE of residuals and increase of sensitivity are achieved simultaneously. All of the applied thresholding approaches provide at least two-fold improvements in relation to the initial SNR value. One can also see from the comparison, the SNR obtained by using the sigmoid function based thresholding scheme is higher than the hard- and soft-thresholding (at least the same as hard thresholding). This trend is more evident when the light blurred case is considered which reveals that the sigmoid function based thresholding scheme to be an effective filter to some extent, no matter whether the SNR is high or low. The four kinds of noisy signals with the SNR at 2.0000 dB and 10.0000 dB as well as their processed results are displayed in Figures 6 and 7. In the two figures, there are four sub-figures representing the four benchmark signals, respectively. It can be visually appreciated that a great amount of noise has been suppressed. In heavy blurred cases the huge noise is removed and the outline of benchmark signals is recovered; moreover, in light blurred cases the detail of benchmark signals are only slightly harmed after thresholding. Comparing the four sub-figures in Figure 7, one can easily see that the thresholding scheme is very important. The curves obtained by the proposed procedure are more similar to the original signals. This is due to the sigmoid function based thresholding scheme is flexible and adaptive, while the hard- and soft-thresholding method are coarse and rough, hence the proposed method performs the better performance to simulations.

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.