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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.


PSD of the signals before and after filtering.
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f11-sensors-12-11205: PSD of the signals before and after filtering.

Mentions: Figure 11 illustrates the power spectrum density (PSD) of signals at the third story of the model before and after processing. It is clear that the noise spectrum is distributed along almost the whole frequency band. Comparing the PSD, one can see the good effect of the proposed method in real signal denoising, even when embedded in broad band noises. From the identified results shown in Table 2, it can be seen, as expected, that the model frequencies are exactly the same. Therefore, although the proposed method works well for the noise, it doesn't destroy the real frequency components of the signal at all.


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

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

PSD of the signals before and after filtering.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-12-11205: PSD of the signals before and after filtering.
Mentions: Figure 11 illustrates the power spectrum density (PSD) of signals at the third story of the model before and after processing. It is clear that the noise spectrum is distributed along almost the whole frequency band. Comparing the PSD, one can see the good effect of the proposed method in real signal denoising, even when embedded in broad band noises. From the identified results shown in Table 2, it can be seen, as expected, that the model frequencies are exactly the same. Therefore, although the proposed method works well for the noise, it doesn't destroy the real frequency components of the signal at all.

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.