27 March 2018 Discussion of signal decomposition techniques on feature extraction from structural dynamic response data
Author Affiliations +
Modal parameters identification research based on using vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. Since time–frequency analysis for non-linear and non-stationary signals is extraordinarily challenging for structural response under extreme loading. To capture features in these signals, it is necessary for the analysis methods to be local, adaptive and stable. Classical approach to compute its instantaneous frequency is to consider the amplitude–frequency modulated formulation of its complex (or analytic) signal extension, via the Hilbert transform. The aim of this paper is therefore to present and discuss some non-parametric methods of time frequency analysis improved with respect to the classical implementation, and compare their performance under different conditions with respect to the signals to be examined. In particular, the following methods will be presented:
    Modified Complex Morlet Wavelet with Variable Central Frequency (MCMV+VCF)
    Enhanced Time-Frequency Analysis Through SVD-based WPT
    Synchro-Squeeze wavelet transform method (SSWT)
    Reassigned Smoothed Pseudo Wigner-Ville Distribution (RSPWVD)
To demonstrate the applicability of the proposed method responses from synthetic signals and instrumented responses are used to demonstrate the capability of feature extraction of the methods, analysis on the synthetic data as well as the response data.
Conference Presentation
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Chin-Hsiung Loh, Chin-Hsiung Loh, Chuan-Fu Li, Chuan-Fu Li, Wen Hsueh, Wen Hsueh, "Discussion of signal decomposition techniques on feature extraction from structural dynamic response data", Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981Y (27 March 2018); doi: 10.1117/12.2295887; https://doi.org/10.1117/12.2295887

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