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Damage detection of moment frames using ensemble Empirical Mode Decomposition and clustering techniques
Abstract Most of the damage detection methods implement dynamic response of linear structures for indication of damage. Time-frequency methods employ signal processing techniques to detect temporal changes in frequency characteristics of vibration response of the system. However, most of these techniques fail facing with nonlinear systems due to consecutive changes in stiffness that lead to fake damage indications. This study is divided into two parts. First, the Empirical Mode Decomposition method (EMD) is compared to Ensemble EMD (EEMD) to clarify the capability of these methods as signal processing tools. For this reason, the acceleration responses of a nonlinear steel moment frame are extracted by EMD and EEMD. Second, Hilbert transform based on EEMD is employed in conjunction with a density-based clustering technique to classify the changes in frequency and amplitude patterns to identify and estimate the extent of damage. Simulation results demonstrate that frequency is not an appropriate parameter to determine the extent of damage for nonlinear systems.
Damage detection of moment frames using ensemble Empirical Mode Decomposition and clustering techniques
Abstract Most of the damage detection methods implement dynamic response of linear structures for indication of damage. Time-frequency methods employ signal processing techniques to detect temporal changes in frequency characteristics of vibration response of the system. However, most of these techniques fail facing with nonlinear systems due to consecutive changes in stiffness that lead to fake damage indications. This study is divided into two parts. First, the Empirical Mode Decomposition method (EMD) is compared to Ensemble EMD (EEMD) to clarify the capability of these methods as signal processing tools. For this reason, the acceleration responses of a nonlinear steel moment frame are extracted by EMD and EEMD. Second, Hilbert transform based on EEMD is employed in conjunction with a density-based clustering technique to classify the changes in frequency and amplitude patterns to identify and estimate the extent of damage. Simulation results demonstrate that frequency is not an appropriate parameter to determine the extent of damage for nonlinear systems.
Damage detection of moment frames using ensemble Empirical Mode Decomposition and clustering techniques
Amiri, Gholamreza Ghodrati (author) / Darvishan, Ehsan (author)
KSCE Journal of Civil Engineering ; 19 ; 1302-1311
2015-05-18
10 pages
Article (Journal)
Electronic Resource
English
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