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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.


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f9-sensors-12-11205: Schematic diagram of accelerator location.

Mentions: The test was conducted at the State Key Laboratory of Coastal and Offshore Engineering in Dalian University of Technology, China [19]. The tests model the scale of a three-story reinforced concrete frame-shear wall structure with an asymmetric shear wall distribution under simulated seismic excitations, as shown in Figure 8. The height of the structural model is 3.1 m, and the total mass is nearly 3 tons. The cross-section area of the column and beam is 0.08 m × 0.08 m and 0.06 m × 0.1 m, respectively. The thickness of the shear wall is 0.03 m. Accelerometers were installed in each story of the model and the shaking table deck, and they were placed at the center of mass of the structure, and near and away from the sides of the shear wall, respectively (see Figure 9). The base excitations were used by white noise and the data were acquired simultaneously at the rate of 500 Hz from 16 channels.


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

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

Schematic diagram of accelerator location.
© Copyright Policy
Related In: Results  -  Collection

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

f9-sensors-12-11205: Schematic diagram of accelerator location.
Mentions: The test was conducted at the State Key Laboratory of Coastal and Offshore Engineering in Dalian University of Technology, China [19]. The tests model the scale of a three-story reinforced concrete frame-shear wall structure with an asymmetric shear wall distribution under simulated seismic excitations, as shown in Figure 8. The height of the structural model is 3.1 m, and the total mass is nearly 3 tons. The cross-section area of the column and beam is 0.08 m × 0.08 m and 0.06 m × 0.1 m, respectively. The thickness of the shear wall is 0.03 m. Accelerometers were installed in each story of the model and the shaking table deck, and they were placed at the center of mass of the structure, and near and away from the sides of the shear wall, respectively (see Figure 9). The base excitations were used by white noise and the data were acquired simultaneously at the rate of 500 Hz from 16 channels.

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.