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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.


Measured and denoised acceleration response.
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f10-sensors-12-11205: Measured and denoised acceleration response.

Mentions: The measured and denoised acceleration response at location 2 (center of mass) of every story in the Y direction is selected for analyses. Here, the measured signals are also transformed by the db4 wavelet, and the rational wavelet decomposition level is 3, which is chosen by the aforementioned method. Figure 10 shows the measured and denoised acceleration response at the third story of the model. From the visual inspection of the results, it can be observed that the measured signals are corrupted by electromagnetic interference of the environment because the signal receiver has a relative wide bandwidth. The amplitude and arrival time of every noise peak is random, causing a disturbance that masks the objective signal to some extent. After application of the proposed procedure, it can be found that the signal is clearer than the measured signal. Statistical results of before and after filtering is demonstrated in Table 2. It can be seen that the accelerations between −0.39 g and 0.45 g for the measured signal and between −0.37 g and 0.39 g for the denoised signal, which indicates the measured signal after filtering has a smaller spread of coordinates implies that proposed method is effective. In addition, the standard deviations demonstrated in Table 2 are also proved the point.


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

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

Measured and denoised acceleration response.
© Copyright Policy
Related In: Results  -  Collection

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

f10-sensors-12-11205: Measured and denoised acceleration response.
Mentions: The measured and denoised acceleration response at location 2 (center of mass) of every story in the Y direction is selected for analyses. Here, the measured signals are also transformed by the db4 wavelet, and the rational wavelet decomposition level is 3, which is chosen by the aforementioned method. Figure 10 shows the measured and denoised acceleration response at the third story of the model. From the visual inspection of the results, it can be observed that the measured signals are corrupted by electromagnetic interference of the environment because the signal receiver has a relative wide bandwidth. The amplitude and arrival time of every noise peak is random, causing a disturbance that masks the objective signal to some extent. After application of the proposed procedure, it can be found that the signal is clearer than the measured signal. Statistical results of before and after filtering is demonstrated in Table 2. It can be seen that the accelerations between −0.39 g and 0.45 g for the measured signal and between −0.37 g and 0.39 g for the denoised signal, which indicates the measured signal after filtering has a smaller spread of coordinates implies that proposed method is effective. In addition, the standard deviations demonstrated in Table 2 are also proved the point.

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.