跳到主要內容

臺灣博碩士論文加值系統

(3.236.110.106) 您好!臺灣時間:2021/07/26 01:04
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:詹淳凱
研究生(外文):Chun-Kai Chan
論文名稱:考量結構反應量測為基礎之全域與局部特徵解析
論文名稱(外文):Vibration-based Structural Identification:Global and Local Dynamic Characteristics
指導教授:羅俊雄羅俊雄引用關係
口試委員:呂良正蔡克銓張家銘
口試日期:2015-06-30
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:136
中文關鍵詞:結構健康監測系統識別小波包轉換奇異譜分析向量自迴歸模型永久變位
外文關鍵詞:structural health monitoringseismic response processingwavelet packet transformsingular spectrum analysismultivariate autoregressive modelpermanent deformation
相關次數:
  • 被引用被引用:0
  • 點閱點閱:159
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本研究的目的在於分析結構物的反應訊號,經由適當的訊號處理工具識別後,了解結構物之行為與變化甚至是判定破壞的發生,即為結構健康監測之宗旨(Structural health monitoring)。現今健康監測與損壞識別使用大量儀器以獲取結構物全域或局部之反應,但會受限於現地裝設困難以及設備昂貴等限制,舉凡位移計,受到實地裝設限制,無法得知結構物受到地震力之位移反應。
因此本研究使用各種訊號處理及系統識別之方法,透過量測到之反應,獲取其系統之特徵,進而將結構物之全域與局部上的行為進行聯結。本文中使用了:(1) Hilbert transform, (2) wavelet packet transform, (3) principal component analysis, (4) singular spectrum analysis, (5) system identification using multi-variant autogressive model. 在介紹各個訊號處理方法後,將以兩種試體為例進行振動台試驗,來進行驗證,試體分別為一層樓雙跨的鋼筋混凝土構架,以及三層樓的鋼構架,並架設加速度計、位移計以及光學位移量測系統,其中加速度計與位移計可量測到結構全域之行為,而光學量測系統可設置於使用者較關注之細部結構,以獲取其局部行為。由分析結果顯示,本文所提出之方法可以識別出結構物之全域與局部特徵,其中全域特徵包含:波傳效應而引起之相位差、自然頻率與阻尼識別、時變頻率識別,及透過加速度歷時來預測位移歷時。而透過光學量測系統獲取之局部特徵包含:柱子之局部應變、變形曲率以及應力分布識別,進而與全域行為進行關聯。本文中所提出之技術將有助於簡化結構健康檢測之硬體設置,並使分析者能夠獲得更多結構特徵之資訊。


In civil engineering, health monitoring and damage detection are mostly carried out using a dense array of sensors. Typically, most methods require global measurements to extract the properties of structures. However, some sensors such as linear variable differential transformers (LVDTs) cannot be used due to in situ limitation. Thus the global deformation remains unknown. Therefore, it is necessary to develop algorithms to identify the physical features such as permanent deformation for structural health monitoring. In this study, signal processing techniques and nonlinear identification methods are used and applied to the responses of test specimens subjected to different level of earthquake excitations. Both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of the test specimens using both input and output response data or output only measurement. From the signal-based feature identification method, which include: (1) enhancement of time-frequency analysis of acceleration responses, (2) Hilbert marginal spectrum, (3) estimation of permanent deformation using directly from acceleration response data, (4) instantaneous phase difference, and (5) damage indices. For the modal-based system identification method, structural system parameters are identified, which include: (1) natural frequency, (2) damping ratio, and (3) modes shape. The extracted features are used to compare with the local information measured from the optical sensors. For the local measurements, some physical features are extracted, which include: (1) strain time history, (2) curvature time history, and (3) stress distribution. Two experiments are used to demonstrate the proposed algorithms: a one-story two-bay reinforce concrete frame and a three-story steel frame under weak and strong seismic excitation. The analysis results show that the identified global features are related to the local features and the proposed methods are capable for system identification and damage detection.

口試委員審定書 I
ACKNOWLEDGEMENT II
ABSTRACT (IN CHINESE) III
ABSTRACT (IN ENGLISH) IV
LIST OF TABLES IX
LIST OF FIGURES X
Chapter 1. Introduction 1
1.1 Background 1
1.2 Literature review 2
1.3 Research scope and objectives 4
Chapter 2. Signal Analysis Methodology 6
2.1 General Description 6
2.2 Signal Processing Tools 7
2.2.1 Hilbert Transform 7
2.2.2 Wavelet Transform 8
2.2.2.1 Continuous Wavelet Transform (CWT) 9
2.2.2.2 Discrete Wavelet Transform (DWT) 10
2.2.2.3 Wavelet Packet Transform (WPT) 11
2.2.3 Principal Component Analysis (PCA) 13
2.2.4 Singular Spectrum Analysis (SSA) 16
2.2.5 Multi-Variant Autoregressive Model (MV-AR Model) 19
2.2.5.1 Derivation of MV-AR model 19
2.2.5.2 Model order selection 22
2.2.5.3 Modal assurance criterion 24
Chapter 3. Experimental Verification 26
3.1 Description of experiments 26
3.1.1 One-story two-bay RC frame 26
3.1.2 Three-story steel frame 28
3.2 Global feature extraction 29
3.2.1 Enhanced time-frequency spectrogram 29
3.2.2 Hilbert marginal spectrum 31
3.2.3 Unwrap instantaneous phase 33
3.2.4 Permanent displacement 34
3.2.5 Modal parameter identification 37
3.2.6 Damage features extraction and evaluation 39
3.2.6.1 MV-AR model damage features 40
3.2.6.2 WPT-based energy damage index 44
3.3 Local feature extraction 46
3.3.1 Strain/ curvature estimation 48
3.3.2 Stress distribution estimation 52
3.4 Chapter summary 55
Chapter 4. Conclusion 59
4.1 Research conclusions 59
4.2 Recommendations for future work 62
LIST OF REFERENCES 63


[1]Loh, C. H., Mao, C. H., Huang J. R. and Pan, T. C. 2011. System Identification of Degrading Hysteresis of Reinforced Concrete Frames. Earthquake Engineering and Structural Dynamics, 40: page 623–640.
[2]Ceravolo, R. 2004. Use of instantaneous estimators for the evaluation of structural damping. Journal of Sound and Vibration, volume. 274, issues 1-2, page 385-401.
[3]Wu, T.H. and Loh, C.H. 2014. An assessment of recursive estimation methods for the identification of vibrating structures. The 5th Asia Conference on Earthquake Engineering.
[4]Chao, S.H. and Loh, C.H. 2013. Vibration-Based Damage Identification of Reinforced Concrete Member Using Optical Sensor Array Data. Structural health monitoring 12(5-6), page 397-410.
[5]Chan, C.K., Loh, C.H., Wu, T.H. 2015. Damage detection and quantification in a structural model under seismic excitation using time-frequency analysis. SPIE 9437, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure.
[6]Carbajo, E.S., Carbaho, R.S., Goldrick, C.M., Basu, B. 2014. An automated structural change detection algorithm based on the Hilbert-Huang transform. Mechanical Systems and Signal Processing, volume.47, issues 1-2, page 78-93.
[7]Chen, X.J., Gao, Z.F. 2011. Data processing based on wavelet analysis in structure health monitoring system. Journal of Computers, volume. 6, no. 12, page 2686-2691.
[8]Deng, X., Wang, Q., Chen, X. 2008. A Time-Frequency Localization Method for Singular Signal Detection Using Wavelet-Based Holder Exponent and Hilbert Transform. Image and Signal Processing, volume. 4, page 266-270.
[9]Yan, A.M. and Golinval, J.C. 2006. Null subspace-based damage detection of structures using vibration measurements. Mechanical Systems and Signal Processing, Vol: 20, 611–626.
[10]Zugasti1, E., Gonz´alez, A.G´., Anduaga1, J., Arregui1, M.A., and Mart´ınez1, F. 2012. NullSpace and AutoRegressive damage detection: a comparative study, Smart Materials and Structures.
[11]Yan, A.M. and Golinval, J.C., and Marin, F. 2005. Fault Detection Algorithm based on Null-Space Analysis for On-Line Structural Health Monitoring. International Modal Analysis Conference (IMAC XXIII).
[12]Feldman, M. 2011. Hilbert Transform in Vibration Analysis. Mechanical Systems and Signal Processing, page 735-802.
[13]Gabor, D. 1946. Theory of Communication. Volume. 93, no. 3, page. 429-457.
[14]Walker, J.S. 1999. A Primer on Wavelets and Their Scientific Applications. Chapman and Hall/CRC.
[15]Pearson, K. 1901. On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine Series 6, page 559-572.
[16]Jolliffe, I.T. 2002. Principal Component Analysis. volomue. 2nd, Springer.
[17]ELsner, J.B. and Tsonis, A.A. 1996. Singular Spectrum Analysis: A New Tool in Time Series Analysis. Plenum Press.
[18]Golyandina, N., Nekrutkin, V., and Zhigljavsky, A.A. 2001 Analysis of Time Series Structure: SSA and Related Techniques. Chapman and Hall/CRC.
[19]Alonso, F.J., Castillo, J.M., and Pintado, P. 2005. Application of Singular Spectrum Analysis to the Smoothing of Raw Kinematic Signals. Journal of Biomechanics, 38(5), 1085-1092.
[20]Vu, V. H., Thomas, M., Lakis, A. A. and Marcouiller, L. 2009. Operational modal analysis of non-stationary mechanics systems by short-time autoregressive (STAR) modelling. 3rd International Conference on Integrity, Reliability and Failure.
[21]Vu, V. H., Thomas, M., Lakis, A. A. and Marcouiller, L. 2011. Operational modal analysis by updating autoregressive model. Mechanical Systems and Signal Processing, 25(3), page 1028-1044.
[22]Golub, G. H., & Van Loan, C. F. (Eds.). 1996. Matrix Computations. The Johns Hopkins University Press, Baltimore, Maryland.
[23]Bjorck, A. 1996. Numerical Methods for Least Squares Problems. Philadelphia: Society for Industrial and Applied Mathematics.
[24]George, E.P.B, Gwilym, M.J., Gregory, C.R. 1994. Time Series analysis: Forecasting and control. Prentice Hall, Englewood Cliffs, NJ.
[25]Hannan, E. J. 1980. The estimation of the order of an ARMA process. Annals of Statistics, 8(5), page 1071–1081.
[26]Palle, A. 1997. Identification of Civil Engineering Structures using Vector ARMA Models. Ph.D. Thesis, Department of Building Technology and Structural Engineering, Aalborg University, Aalborg, Denmark.
[27]Palle, A., Rune, B. 1998. Estimation of modal parameters and their uncertainties. Proceedings of the 11th International Conference on Experimental Mechanics, Oxford, UK, paper no. 108.
[28]Mace, B. R., et al. 2005. Uncertainty in structural dynamics. Journal of Sound and Vibration, 288(3), page 423–429.
[29]Allenmang, R.J. 1996. The Modal Assurance Criterion – Twenty Years of Use and Abuse. Sound and Vibration.
[30]Hassanpour, H. 2007. Improved SVD-Based Technique for Enhancing the Time-Frequency Representation of Signals. Circuits and Systems, page 1819-1822.
[31]Biydreaux-Bartels, G.F. and Parks, T.W. 1996. Time-varying Filtering and Signal Estimation Using Wigner Distribution Synthesis Techniques. IEEE Transactions on Acoustics, Speech, and Signal Processing, volume. ASSP-34, NO. 3.
[32]Wu, T.H. and Loh, C.H. 2014. An assessment of recursive estimation methods for the identification of vibrating structures. The 5th Asia Conference on Earthquake Engineering.
[33]Mosavi, A.A., Dickey, D., Seracino, R., Rizkalla, S. 2012. Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances. Mechanical Systems and Signal Processing, volume. 26, page 254 (14).
[34]Pandit, S.M., Wu, S.M. 1983. Time series and system analysis with applications. Wiley, New York, US.
[35]Spruyt, V. 2014. How to draw an error ellipse representing the covariance matrix? Available at: http://www.visiondummy.com/2014/04/draw-error-ellipse-representing-covariance-matrix/ Accessed 15 June 2015.
[36]Boashash, B. 1992. Estimating and Interpreting the Instantaneous Frequency of a Signal. II. Algorithms and Applications. Proceedings of the IEEE, 80(4), page 540-568.
[37]Hsu, W.T., Loh, C.H., Chao, S.H. 2015. Uncertainty Calculation for Modal Parameters Used with Stochastic Subspace Identification: An Application to a Bridge Structure. SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems.
[38]Hsu, W.T. 2015. Automatic (operational) modal analysis for Stochastic Subspace Identification. Master thesis. Department of Civil Engineering, National Taiwan University.


QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top