Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators

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....

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Main Author: Mao, Chien Hong
Other Authors: Loh Chin-Hsiung
Format: Theses and Dissertations
Language:English
Published: 2010
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Online Access:https://hdl.handle.net/10356/36291
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-362912023-03-03T19:13:48Z Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators Mao, Chien Hong Loh Chin-Hsiung Pan Tso-Chien School of Civil and Environmental Engineering DRNTU::Engineering::Civil engineering::Structures and design 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). 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. MASTER OF ENGINEERING(CEE) 2010-04-30T04:37:16Z 2010-04-30T04:37:16Z 2009 2009 Thesis Mao, 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. https://hdl.handle.net/10356/36291 10.32657/10356/36291 en 161 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Civil engineering::Structures and design
spellingShingle DRNTU::Engineering::Civil engineering::Structures and design
Mao, Chien Hong
Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators
description 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). 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.
author2 Loh Chin-Hsiung
author_facet Loh Chin-Hsiung
Mao, Chien Hong
format Theses and Dissertations
author Mao, Chien Hong
author_sort Mao, Chien Hong
title Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators
title_short Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators
title_full Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators
title_fullStr Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators
title_full_unstemmed Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators
title_sort nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators
publishDate 2010
url https://hdl.handle.net/10356/36291
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