dc.contributor.authorMao, Chien Hong
dc.date.accessioned2010-04-30T04:37:16Z
dc.date.accessioned2017-07-23T08:27:03Z
dc.date.available2010-04-30T04:37:16Z
dc.date.available2017-07-23T08:27:03Z
dc.date.copyright2009en_US
dc.date.issued2009
dc.identifier.citationMao, C. H. (2009). Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators. Master’s thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/36291
dc.description.abstractWith 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). Test 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.en_US
dc.format.extent161 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Civil engineering::Structures and designen_US
dc.titleNonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicatorsen_US
dc.typeThesis
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.contributor.supervisorLoh Chin-Hsiung
dc.contributor.supervisorPan Tso-Chienen_US
dc.description.degreeMASTER OF ENGINEERING(CEE)en_US


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