Published In

Sensors

Document Type

Article

Publication Date

3-2020

Subjects

Structural Vibration Data -- Noise Reduction

Abstract

With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified.

Rights

© 2020 by the authors. Licensee MDPI, Basel, Switzerland

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Locate the Document

https://doi.org/10.3390/s20051423

DOI

10.3390/s20051423

Persistent Identifier

https://archives.pdx.edu/ds/psu/32834

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