29 July 2004 Covariance-driven asymptotic wavelet analysis for modal identification
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In this study, a technique that integrates the wavelet transform with a covariance-driven modal analysis scheme, is proposed to address the output-only modal analysis problem. Under the assumption that the ambient excitation can be modeled as a white-noise process, the output covariance is computed firstly to separate the effect of random excitation from the response measurement. The wavelet transform is then employed to convert the covariance vector in the time domain to the power scalogram in the time-scale plane. The wavelet coefficients along the energy concentrated curve are extracted and the structural modal parameters including the resonant frequency, modal damping and mode shape vector can then be estimated based on the amplitude and the phase of the extracted wavelet coefficients. As the wavelet transform has a capacity to capture both stationary and transient information from the original measurement, the proposed technique provides a promising approach for identifying modal properties of both linear and nonlinear structures. Both numerical and experimental studies are performed to demonstrate the proposed technique and verify its accuracy. The results show the proposed method works very well in identifying modal parameters of structures with multiple degrees of freedom.
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Zhi Sun, Zhi Sun, Chih-Chen Chang, Chih-Chen Chang, "Covariance-driven asymptotic wavelet analysis for modal identification", Proc. SPIE 5391, Smart Structures and Materials 2004: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, (29 July 2004); doi: 10.1117/12.540281; https://doi.org/10.1117/12.540281

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