Nonlinear System Identification Method for Structural Health Monitoring: Techniques for the Detection of Nonlinear Indicators
Date Issued
2009
Date
2009
Author(s)
Mao, Chien-Hong
Abstract
With the progress of signal processing technologies, structural health monitoring (SHM) has received more and more attentions. The core algorithm in SHM is based on the detection of damage-sensitive indicator. In the recent decades, engineers already have the ability to deal with nonlinear problem. A literature survey of nonlinear indicators is firstly examined in the study. It is found that a successful SHM requires the monitoring technologies have their flexibility, simplicity, and, of course, accuracy. The nonparametric system identification method is a potential candidate which can meet these requirements. Therefore, several nonlinear indicators corresponding to the nonparametric system identification method are studied in this research, both from frequency and time domain analysis. In this research, the frequency-domain nonlinear indicators included: (1) Hilbert transform of frequency response function, (2) coherence function, (3) Hilbert marginal spectrum, (4) wavelet packet transform component correlation coefficient, and (5) bispectral analysis; and the time-domain nonlinear indicators included: (1) instantaneous frequency, (2) instantaneous phase difference, (3) Holder exponent, (4) discrete wavelet transform, and (5) singular spectrum analysis (SSA).est data from a series of shake table test to the 1-story 2-bay RC frame is generated from NCREE (National Center for Research on Earthquake Engineering), Taiwan. For these shake table tests data from two groups of specimens are analysed using the proposed nonlinear indicators. The first group of seismic response data is to consider the response from different specimen subjected to different level of seismic excitation (TCU082). The second group of data is to examine the damage level through a series of excitation back to back on a specimen. In cooperated with the experimental data, the result shows that nonlinear indicators can provide the identification of structural nonlinearities, which include stiffness degradation and cracks. Finally, the singular spectrum analysis (SSA) technique was used to extract structural residual deformation and to eliminate the noise effect. Furthermore, the SSA method can be used to derive residual displacement using the measured acceleration signal if there is no information on displacement measurement. In this thesis, a deeper realization to nonlinear indicators can be achieved. And it is possible to execute an effective SHM and nonlinear identification of the structure directly from the measurement by using appropriate nonlinear indicators.
Subjects
structural health monitoring
nonlinear system identification
signal processing
nonlinear indicator
singular spectrum analysis
permanent deformation
Type
thesis
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