Abstract
This paper proposes a methodology to process and interpret the complex signals acquired from the health monitoring of civil structures via scale-space empirical wavelet transform (EWT). The FREEVIB method, a widely used instantaneous modal parameters identification method, determines the structural characteristics from the individual components separated by EWT first. The scale-space EWT turns the detecting of the frequency boundaries into the scale-space representation of the Fourier spectrum. As well, to find meaningful modes becomes a clustering problem on the length of minima scale-space curves. The Otsu’s algorithm is employed to determine the threshold for the clustering analysis. To retain the time-varying features, the EWT-extracted mono-components are analyzed by the FREEVIB method to obtain the instantaneous modal parameters and the linearity characteristics of the structures. Both simulated and real SHM signals from civil structures are used to validate the effectiveness of the present method. The results demonstrate that the proposed methodology is capable of separating the signal components, even those closely spaced ones in frequency domain, with high accuracy, and extracting the structural features reliably.
Highlights
The data in Structural Health Monitoring (SHM) from civil structures contains essential information on their condition
Though relatively big differences exist in relatively big differences exist in the damping ratios identified by distinct methods, the values the damping ratios identified by distinct methods, the values obtained from empirical wavelet transform (EWT) have a satisfactory obtained from EWT have a satisfactory accordance with those determined from ARV and accordance with determined from ARV and SSI-DATA
This paper proposes a systematic methodology to extract instantaneous features about the structural condition from SHM signals of civil structures
Summary
The data in Structural Health Monitoring (SHM) from civil structures contains essential information on their condition. Time-frequency (TF) approaches were developed to obtain the instantaneous features from time-varying signals These methods can provide information of signals in both time and frequency domains, becoming more competitive to process SHM data. Time Fourier Transform (STFT) [1], Wigner-Ville Distribution (WVD) [2], Wavelet Transform (WT) [3], and Empirical Mode Decomposition (EMD) [4] were raised Even these methods can determine some useful results, their limitations are clear. The empirical wavelet transform (EWT) [17] proposed by Gilles (2013) combines the advantages of WT and EWD This method can decompose signals adaptably with high TF resolution, and have a consolidated mathematical foundation on WT. The extracted instantaneous structural features are affirmed by comparing with results of the previous studies
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