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.
Highlights
Vibration-based structural damage detection methods have attracted considerable attention in recent years for the assessment of health and safety of large civil structures [1]
Since Donoho and Johnstone [8,9] originally proposed a method known as the wavelet transform (WT) shrinkage to estimate an unknown smoothed signal from data with noise, the WT has rapidly become very popular for signal denoising
The traditional hard thresholding exhibits some discontinuities and may be unstable or more sensitive to small changes in the data, while in soft thresholding the wavelet coefficients are reduced by a quantity equal to the threshold value which will induce the deviation when the filtered wavelet coefficients is reconstructed by the inverse WT (IWT)
Summary
Vibration-based structural damage detection methods have attracted considerable attention in recent years for the assessment of health and safety of large civil structures [1]. A large number of signal denoising algorithms have been proposed in the literature, such as the linear low-pass filter [2], the Kalman filter [3], the median filter [4] and adaptive filtering based on neural networks [5] These conventional methods all have inherent failings; for example, a linear low-pass filter is not good in a case whose signal overlaps the noise often in many frequency bands [6]. Since Donoho and Johnstone [8,9] originally proposed a method known as the wavelet transform (WT) shrinkage (thresholding) to estimate an unknown smoothed signal from data with noise, the WT has rapidly become very popular for signal denoising. This paper is concerned with the rational selection of processing parameters in the wavelet based filtering method, and is especially relevant to a novel thresholding method, which generalizes the hard- and soft-shrinkage proposed by Donoho and Johnstone [8].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
