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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 羅俊雄 | |
dc.contributor.author | Chien-Hong Mao | en |
dc.contributor.author | 毛建閎 | zh_TW |
dc.date.accessioned | 2021-06-15T01:51:18Z | - |
dc.date.available | 2009-07-16 | |
dc.date.copyright | 2009-07-16 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-07-03 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43351 | - |
dc.description.abstract | 一個成功的結構物健康監測(Structural health monitoring)必須能夠經由分析結構物的反應訊號後,即能快速分辨破壞是否產生,左右其成功的關鍵在於結構破壞識別的運算方法。結構破壞識別主要可以區分為兩種:線性系統識別與非線性系統識別。本研究將考慮非線性系統識別方法(nonlinear system identification)的應用,其中以非參數方法(nonparametric method)為主,進行各種非線性指標的探討,包含了頻率域的非線性指標以及時間域的非線性指標。頻率域的非線性指標包含: (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; 時間域的非線性指標包含: (1) instantaneous frequency, (2) instantaneous phase difference, (3) Holder exponent, (4) discrete wavelet transform, and (5) singular spectrum analysis (SSA). 在介紹各個非線性指標的基本理論之後,本研究將會以一個一層樓的鋼筋混泥土架構的振動台試驗,進行各種非線性指標應用於真實結構物上的效果評估。分析的結果顯示,這些非線性指標可以有效探測出結構物是否有非線性行為,而這些非線性行為主要是由於結構物勁度折減、強度折減以及裂縫產生所致。另一方面,本研究也進行了SSA的延伸應用,包含:擷取結構物永久位移,去除噪音的影響,以及從加速度訊號推估結構物的永久位移量。從加速度訊號推估結構物的殘餘位移量是一個未來值得發展的主題,因為這個技術將有助於簡化結構健康監測的硬體設施,並使分析者能夠獲得更多結構破壞的資訊。此研究最終證實,經由適當的訊號分析以及非線性指標的應用,分析者能夠直接從量測資料中辨識出結構的損壞。 | zh_TW |
dc.description.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).
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. 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. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T01:51:18Z (GMT). No. of bitstreams: 1 ntu-98-R96521209-1.pdf: 3363308 bytes, checksum: 28cbae2ad6a5c6bcb6937ccd4def5dd4 (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | 口試委員審定書 I
ACKNOWLEDGMENT II ABSTRACT (IN CHINESE) III ABSTRACT (IN ENGLISH) IV CONTENTS VI LIST OF TABLES IX LIST OF FIGURES X CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 LITERATURE REVIEW 3 1.2.1 System Identification without Knowing a Priori Model 3 1.2.2 System Identification with Known Mathematical Model 5 1.3 RESEARCH SCOPE AND OBJECTIVES 7 CHAPTER 2 NONLINEAR INDICATORS: METHODOLOGY 10 2.1 GENERAL DESCRIPTION 10 2.1.1 Simulation Studies 11 2.2 BASIC SIGNAL PROCESSING TOOLS 12 2.2.1 Fourier Transform 12 2.2.2 Overview of Wavelet 14 2.2.3 Continuous Wavelet Transform (CWT) 14 2.2.4 Discrete Wavelet Transform (DWT) 16 2.2.5 Wavelet Packet Transform (WPT) 17 2.2.6 Implementation Guideline for Wavelet Analysis 18 2.2.7 Hilbert Transform (HT) 22 2.3 SINGULAR SPECTRUM ANALYSIS (SSA) 23 2.3.1 Overview of SSA 23 2.3.2 Procedures of SSA 24 2.3.3 Selection of SSA Parameters 26 2.4 PARAMETRIC IDENTIFICATION METHOD 28 2.4.1 Equivalent Linear System 28 2.5 FREQUENCY DOMAIN NON-PARAMETRIC IDENTIFICATION METHOD 30 2.5.1 Hilbert Transform of Frequency Response Function (FRF) 30 2.5.2 Coherence Function 31 2.5.3 Hilbert Marginal Spectrum 32 2.5.4 WPT Component Correlation Coefficient 33 2.5.5 Bispectral Analysis 34 2.6 TIME DOMAIN NON-PARAMETRIC IDENTIFICATION METHOD 35 2.6.1 Instantaneous Frequency 35 2.6.2 Instantaneous Phase Difference 37 2.6.3 Holder Exponent 38 2.6.4 Level-1 Detail Component of DWT 41 2.6.5 Residual Component of SSA 41 2.7 CHAPTER SUMMARY 42 CHAPTER 3 EXPERIMENTAL SURVEY 45 3.1 DESIGN DETAILS OF THE ONE-STORY TWO-BAY RC FRAME 45 3.2 PRELIMINARY ANALYSIS OF EXPERIMENTAL MEASUREMENTS 46 3.2.1 Physical parameters 46 3.2.2 Extraction of the Residual Displacement by SSA 49 3.2.3 Improvement of Numerical Differentiation Scheme by SSA 50 3.2.4 Estimation of Residual Deformation from Acceleration data by SSA 52 3.3 FREQUENCY-DOMAIN IDENTIFICATION OF SHAKE TABLE TEST DATA 54 3.3.1 Hilbert Transform of Frequency Response Function 54 3.3.2 Coherence Function 56 3.3.3 Hilbert Marginal Spectrum 57 3.3.4 WPT Component Correlation 58 3.3.5 Bispectral Analysis 59 3.4 TIME-DOMAIN NONLINEARITY IDENTIFICATION OF SHAKE TABLE TEST DATA 60 3.4.1 Instantaneous Frequency 60 3.4.2 Instantaneous Phase Difference 61 3.4.3 Holder Exponent 62 3.4.4 Level-1 Detail Component of DWT 63 3.4.5 Residual Component of SSA 63 3.5 SUPPLEMENTARY STUDY ON RCF6 64 3.6 CHAPTER SUMMARY 67 CHAPTER 4 CONCLUSIONS AND RECOMMENDATIONS 70 4.1 RESEARCH CONCLUSIONS 70 4.2 RESEARCH CONTRIBUTIONS AND LIMITATIONS 73 4.3 RECOMMENDATIONS FOR FUTURE WORK 74 LIST OF REFERENCES 76 | |
dc.language.iso | en | |
dc.title | 非線性系統識別方法於結構健康監測之應用:非線性指標的探討 | zh_TW |
dc.title | Nonlinear System Identification Method for Structural Health Monitoring: Techniques for the Detection of Nonlinear Indicators | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 潘則建 | |
dc.contributor.oralexamcommittee | 張國鎮,廖文義 | |
dc.subject.keyword | 結構健康監測,非線性系統識別,訊號處理,非線性指標,奇異譜分析,永久變位, | zh_TW |
dc.subject.keyword | structural health monitoring,nonlinear system identification,signal processing,nonlinear indicator,singular spectrum analysis,permanent deformation, | en |
dc.relation.page | 151 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-07-03 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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