基于重构相空间的结构损伤检测方法及可视化研究
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摘要
针对目前结构损伤检测和健康监测遇到的问题，本文从技术上可实施、工程上实用、易观测的角度，提出新的结构损伤检测方法和可视化展示方法。首先介绍本课题的研究意义，并将现阶段损伤检测和健康监测方法进行分类，对各类方法国内外研究现状进行分析和调研。总结出现阶段结构损伤检测和健康监测的研究热点，以及当前现有损伤检测方法在实际应用中存在的问题及挑战。
利用当前应用成熟的多个动力指纹指标对一圆拱结构进行损伤检测，分析了测点个数对检测结果的影响，和环境因素影响下动力指纹方法的鲁棒性，以及实际工程应用的适应性分析；结果表明，当在较少的有限个测点下，模态信息缺失导致识别可靠性和精确性下降，甚至不能检测出结构损伤。而增加噪音影响后，识别结果完全无效，说明了动力指纹方法在工程应用中的不适应性。
因此本文提出基于响应重构相空间的损伤检测方法来解决以上不足。首先介绍相空间和重构相空间的概念，以及重构相空间方法中延迟时间和嵌入维数的选取。将混沌时间序列研究领域的重构相空间方法引入结构损伤检测中，以一维弹簧振子为例，研究了基于重构相空间的损伤检测法的可行性。提出基于重构相空间拓扑结构变化的损伤特征量，用提出的方法对第二章所述的拱结构进行损伤检测并和动力指纹方法作比较。针对提出的方法做了圆拱结构损伤检测数值模拟和实验研究。结果表明，本方法比动力指纹方法要敏感的多，并且在噪音的影响下表现为良好的鲁棒性，证明了本方法的可行性和可靠性。本方法只须单个测点就能计算出该点的损伤特征量，可作为结构整体参量来监测结构损伤的存在和损伤程度。为了证明提出方法的可靠性，做了进一步的大型实验研究，对一6m长的两跨钢筋混凝土板进行损伤检测和健康监测。结果表明，基于重构相空间拓扑结构变化的CPST指标成功地检测出钢筋混凝土板的损伤及损伤程度。并且证明了嵌入维数对检测结果影响较大。
本文接着提出基于非线性杜芬混沌振子输出相空间的最大李雅普诺夫指数法的结构损伤检测法。介绍了本方法的理论依据，以及最大李雅普诺夫指数的计算方法。本方法直接将振动信号输入杜芬混沌检测系统。在单个测点情况下，应用此方法对一简支梁进行损伤检测研究，并与传统动力指纹方法进行比较。结果表明，此方法能够成功识别出结构的损伤。
提出了结构损伤检测多指标分层与信息融合可视化的概念，使结构的损伤信息以可视化的图像展示出来。介绍了可视化方法流程，以及信息融合的方法，将前述结果进行可视化。本方法以颜色的差异来标识损伤信息，并直接反映在原结构上，以便直观显示损伤信息，利于实际工程的应用和推广。
In this peper, based on the current problems and challenges of structural damage detectionand health monitoring, a new damage detection method, and the method of visualization ofdamage information are proposed according to the fisibility of implementation, practicalapplicability and observation. Firstly, the significance of this research field is introduced, and thecurrent structural damage detection and strucrural health monitoring methods are classified.Based on the analysis of current research situation of this field in the world, the hot researchissues, current problems, difficulties and challenges in the application of real engineeringstructures are summarized.
Several matural used modal-based methods are used to identify the damages of a circulararch. Then, the influence of the damage detection results under using different number ofmeasurent points to extract the modal parameters is analized. And also the robustness of theresults under different environments such as diferent levels of noise is researched. Theadaptability of practical application of modal-based methods is analized for the comparison withthe proposed method in the following studies. The simulation results indicate that the reliabilityand accuracy of modal-based methods decline because the loss of modal information when usingless number of measurement points, even some parameters can not identify the damage at all.When the mode shapes are smeared with noise to simulate the inference of environment, theresults indicate that none of the modal-based methods can detect the damage of the arch.
In order to overcome the disadvantage of the modal-based methods, a new damagedetection method based on vibration reconstructed phase space is proposed. Firstly, thedefination of the phase space and reconstructed phase space of vibration system are introduced.The methods of determination of embedding demenssion and delay time are analized. In thismethod, the technology of phase space reconstruction sterming from the study of chaotic series isintroduced to structural damage detection. Then the fisibility of this proposed method is studiedusing a one-demenssion spring oscillator. The results indicate that the reduction of stiffnessintroduces the changes on the topology of phase space, which indicates that this method is fisibleand reliable. The damage parameter is proposed based on the changes of reconstructed phasespace to detect the damage which is set as the same in modal-based analysis. The simulation andexperiment are carried out on the circular arch and the results are compared with the modal-based methods. The results indicate that the proposed method is much more sensitive thanmodal-based method, and presents good robustness to the noise, which prove that the method isfisible abd reliable. An advantage of this method is that response of only one measurement pointcan be sued to caucluate the damage parameter of this point, which proves that this parametrercan be considered as a global index to monitor the health of the structure. In order to furtherprove the reliability of this method, an experiment of damage detection and health monitoring ona6m length reinforced concrete slab with two spans is carried out. The results indicate that thismethod can succefully identify the damage of the slab. It is also indicated that the influence ofembedding dimension to the detection result is significant. The result of embedded twodimensions condition is not as good as three embedding dimensions because some damageinformation which only affetc the third embedding dimension direction is missed.
Another new damage detetion method is also proposed based on the nonlinear chaoticDuffing system. The largest Lyapunov Exponent of the phase space of the output signal of thesystem is extracted and considered as the damage parameter. This method sterms from the weeksignal detection using Duffing chaotic system. The theory of this method and the method of thelargest Lyapunov Exponent calculation are introduced. In this mehod, the vibration signals areinputted into the Duffing system directly, which makes the system at the critical status become tochatic status quickly, and then the largest Lyapunov Exponent calculation is calculated. Usingthis proposed method, the damage of a simple beam is identified successfully with only onesensor. The results indicate that it is more sensitive than natural frequencies.
Last, the method of hierarchical and fused visualizations of damage detection using manyindices is proposed. This method makes the damage information be displayed directly withvisual image. The process of visualization is introduced and the informations fusion method isproposed. This metod uses the difference of the colors to indicate the damage location and level.Then the damage information is mapped onto the real structure, so as to present the damagevisually and easy to be understood. Thus, it can be promoted and used in the real structuraldamage detection and health monitoring as a new display platform.
利用当前应用成熟的多个动力指纹指标对一圆拱结构进行损伤检测，分析了测点个数对检测结果的影响，和环境因素影响下动力指纹方法的鲁棒性，以及实际工程应用的适应性分析；结果表明，当在较少的有限个测点下，模态信息缺失导致识别可靠性和精确性下降，甚至不能检测出结构损伤。而增加噪音影响后，识别结果完全无效，说明了动力指纹方法在工程应用中的不适应性。
因此本文提出基于响应重构相空间的损伤检测方法来解决以上不足。首先介绍相空间和重构相空间的概念，以及重构相空间方法中延迟时间和嵌入维数的选取。将混沌时间序列研究领域的重构相空间方法引入结构损伤检测中，以一维弹簧振子为例，研究了基于重构相空间的损伤检测法的可行性。提出基于重构相空间拓扑结构变化的损伤特征量，用提出的方法对第二章所述的拱结构进行损伤检测并和动力指纹方法作比较。针对提出的方法做了圆拱结构损伤检测数值模拟和实验研究。结果表明，本方法比动力指纹方法要敏感的多，并且在噪音的影响下表现为良好的鲁棒性，证明了本方法的可行性和可靠性。本方法只须单个测点就能计算出该点的损伤特征量，可作为结构整体参量来监测结构损伤的存在和损伤程度。为了证明提出方法的可靠性，做了进一步的大型实验研究，对一6m长的两跨钢筋混凝土板进行损伤检测和健康监测。结果表明，基于重构相空间拓扑结构变化的CPST指标成功地检测出钢筋混凝土板的损伤及损伤程度。并且证明了嵌入维数对检测结果影响较大。
本文接着提出基于非线性杜芬混沌振子输出相空间的最大李雅普诺夫指数法的结构损伤检测法。介绍了本方法的理论依据，以及最大李雅普诺夫指数的计算方法。本方法直接将振动信号输入杜芬混沌检测系统。在单个测点情况下，应用此方法对一简支梁进行损伤检测研究，并与传统动力指纹方法进行比较。结果表明，此方法能够成功识别出结构的损伤。
提出了结构损伤检测多指标分层与信息融合可视化的概念，使结构的损伤信息以可视化的图像展示出来。介绍了可视化方法流程，以及信息融合的方法，将前述结果进行可视化。本方法以颜色的差异来标识损伤信息，并直接反映在原结构上，以便直观显示损伤信息，利于实际工程的应用和推广。
In this peper, based on the current problems and challenges of structural damage detectionand health monitoring, a new damage detection method, and the method of visualization ofdamage information are proposed according to the fisibility of implementation, practicalapplicability and observation. Firstly, the significance of this research field is introduced, and thecurrent structural damage detection and strucrural health monitoring methods are classified.Based on the analysis of current research situation of this field in the world, the hot researchissues, current problems, difficulties and challenges in the application of real engineeringstructures are summarized.
Several matural used modal-based methods are used to identify the damages of a circulararch. Then, the influence of the damage detection results under using different number ofmeasurent points to extract the modal parameters is analized. And also the robustness of theresults under different environments such as diferent levels of noise is researched. Theadaptability of practical application of modal-based methods is analized for the comparison withthe proposed method in the following studies. The simulation results indicate that the reliabilityand accuracy of modal-based methods decline because the loss of modal information when usingless number of measurement points, even some parameters can not identify the damage at all.When the mode shapes are smeared with noise to simulate the inference of environment, theresults indicate that none of the modal-based methods can detect the damage of the arch.
In order to overcome the disadvantage of the modal-based methods, a new damagedetection method based on vibration reconstructed phase space is proposed. Firstly, thedefination of the phase space and reconstructed phase space of vibration system are introduced.The methods of determination of embedding demenssion and delay time are analized. In thismethod, the technology of phase space reconstruction sterming from the study of chaotic series isintroduced to structural damage detection. Then the fisibility of this proposed method is studiedusing a one-demenssion spring oscillator. The results indicate that the reduction of stiffnessintroduces the changes on the topology of phase space, which indicates that this method is fisibleand reliable. The damage parameter is proposed based on the changes of reconstructed phasespace to detect the damage which is set as the same in modal-based analysis. The simulation andexperiment are carried out on the circular arch and the results are compared with the modal-based methods. The results indicate that the proposed method is much more sensitive thanmodal-based method, and presents good robustness to the noise, which prove that the method isfisible abd reliable. An advantage of this method is that response of only one measurement pointcan be sued to caucluate the damage parameter of this point, which proves that this parametrercan be considered as a global index to monitor the health of the structure. In order to furtherprove the reliability of this method, an experiment of damage detection and health monitoring ona6m length reinforced concrete slab with two spans is carried out. The results indicate that thismethod can succefully identify the damage of the slab. It is also indicated that the influence ofembedding dimension to the detection result is significant. The result of embedded twodimensions condition is not as good as three embedding dimensions because some damageinformation which only affetc the third embedding dimension direction is missed.
Another new damage detetion method is also proposed based on the nonlinear chaoticDuffing system. The largest Lyapunov Exponent of the phase space of the output signal of thesystem is extracted and considered as the damage parameter. This method sterms from the weeksignal detection using Duffing chaotic system. The theory of this method and the method of thelargest Lyapunov Exponent calculation are introduced. In this mehod, the vibration signals areinputted into the Duffing system directly, which makes the system at the critical status become tochatic status quickly, and then the largest Lyapunov Exponent calculation is calculated. Usingthis proposed method, the damage of a simple beam is identified successfully with only onesensor. The results indicate that it is more sensitive than natural frequencies.
Last, the method of hierarchical and fused visualizations of damage detection using manyindices is proposed. This method makes the damage information be displayed directly withvisual image. The process of visualization is introduced and the informations fusion method isproposed. This metod uses the difference of the colors to indicate the damage location and level.Then the damage information is mapped onto the real structure, so as to present the damagevisually and easy to be understood. Thus, it can be promoted and used in the real structuraldamage detection and health monitoring as a new display platform.
引文
[1]中国桥梁加固网,1999-2006年全国桥梁基本状况[M/OL].[2010-11-09].http://www.zgqljg.com/te_techtopic_qlsg/2010-11-09/732.chtml.
[2]聂振华.基于应变模态的圆拱结构损伤识别方法研究[D].广州：暨南大学理工学院,2008.
[3] C.R. Farrar, W.E. Baker, T.M. Bell et.al. Dynamic characterization and damage detection in the I-40bridge over the Rio Grand[R]. Los Alamos national Laboratory report. LA-12767-MS.1994.
[4]张伟伟.基于模态分析和应力波技术的结构裂纹识别方法研究[D].广州：暨南大学理工学院，2010.
[5] D. Diamantidis. Implementation of reliability-based criteria for structural integrity assessment ofexisting structures [J]. key engineering materials,2009,413-414:203-210.
[6] J.R. Maguire. Residual life assessment of electricity pylons-a case study [J]. Key Engineering materials,2009,413-414:219-228.
[7] J.D. Hios, S.D. Fassois. Statistical damage detection in a smart structure under different temperatures viavibration testing: a gloable model based approach [J]. Key engineering materials,2009,413-414:261-268.
[8]马宏伟,杨桂通.结构损伤探测的基本方法和研究进展[J].力学进展,1999,29(4):513-527.
[9] S.W. Doebling, C.R. Farrar, M.B. Prime. A summary review of vibration-based damage identificationmethods [J]. The Shock and Vibration Digest,1998,30(2):91-105.
[10]张伟伟,程良彦,王志华,马宏伟.阶梯悬臂梁的双裂纹参数两步识别法[J].机械强度,2008,30(4):534-538.
[11] R.D. Adams et.al. A Vibration Technique for Non-Destructively Assessing the Integrity of Structures [J].Journal of Mechanical Engineering Science,1978,20:93-100.
[12] P. Cawley, R.D. Adams. The Location of Defects in Structures from Measurements of NaturalFrequencies [J]. Journal of Strain Analysis,1979,14(2):49-57.
[13] C.R. Farrar, P.J. Comwell et. Structual health monitoring studies of the Alamosa Canyon and I-40bridge
[R]. Los Alamos, NU, USA: Los Alamos National Laboratory,2000.
[14] O.S. Salawu. Detection of Structural Damage through changes in Frequency: A Review [J]. EngineeringStructures,1997,19(9):718-723.
[15] Y. Xia, H. Hao, G. Zanardo, A. Deeks. Long term vibration monitoring of an RC slab: Temperature andhumidity effect [J]. Engineering Structures,2006,28(3):441-452.
[16] R.J. Allemany, D.L. Brown. A Correlation Coefficient for Modal Vector Analysis [C]. Proc. of the1stInternational Modal Analysis Conference,1982,110-116.
[17] N. Lieven, D. Ewins. Spatial Correlation of Modal Shapes, the Coordinate Modal Assurance Criterion(COMAC)[C]. Proceedings6th IMAC,1998, I:690-695.
[18] D.L. Hunt. Applicrion of an Enhanced Coordinate Modal Assurance Criteria [C].10th InternationalModal Analysis Conference, San Diego, Califomia,1992.
[19] Zhenhua Nie, Hongwei Ma. Visualization of Structural Damage Detection Using Vibration-BasedIdentification Techniques [J]. Advanced Science Letters,2011,4:3124-3130.
[20] A.K. Pandey, M. Biswas, M.M. Samman. Damage detection from change in curvature mode shapes [J].Journal of Sound and Vibration,1991,145(2):321-332.
[21]孙晓丹,欧进萍.基于动力检测的损伤指标评价方法[J].振动与冲击,2009,28(1):9-13.
[22] A.K. Pandy, M. Biswas. Damage detection in structures using changes in flexibility [J]. Journal ofSound and Vibration.1994,169(1):3-17.
[23] A.E Aktan, K.L. Lee, C. Chuntavan, T. Aksel. Modal testing for structural identification and conditionassessment of constructed facilities [C]. Proceedings of12th International Modal AnalysisConference,1994.
[24] E.P. Carden, James M.W. Brownjohn. ARMA modelled time-series classification for structural healthmonitoring of civil infrastructure [J]. Mechanical Systems and Signal Processing,2008,22:295-314.
[25] K. Krishnan Naira, Anne S. Kiremidjianb, Kincho H. Law. Time series-based damage detection andlocalization algorithm with application to the ASCE benchmark structure [J]. Journal of Sound andVibration,2006,291:349-368.
[26] Mustafa Gul, F. Necati Catbas. Structural health monitoring and damage assessment using a novel timeseries analysis methodology with sensor clustering [J]. Journal of Sound and Vibration,2011,330:1196-1210.
[27] S. Choia, N. Stubbs. Damage identification in structures using the time-domain response [J]. Journal ofSound and Vibration,2004,275(3-5):577-590.
[28] Luna Majumder, C.S. Manohar. A time-domain approach for damage detection in beam structures usingvibration data with a moving oscillator as an excitation source [J]. Journal of Sound and Vibration,2003,268(4):699-716.
[29] M. Link, M. Weiland. Damage identification by multi-model updating in the modal and in the timedomain [J]. Mechanical Systems and Signal Processing,2009,23(6):1734-1746.
[30] I. Trendafilova, E. Manoach. Vibration-based damage detection in plates by using time series analysis [J].Mechanical Systems and Signal Processing,2008,22(5):1092-1106.
[31] Hayashi T, Tamayama C, Murase M. Wave structure analysis of guided waves in a bar with an arbitrarycross-section.[J]. Ultrasonics.2006,44,17-24.
[32]马宏伟,吴斌.弹性动力学及其数值方法[M].中国建材工业出版社,2000,65-89.
[33] J.L. Rose, X.L. Zhao. Flexural Mode Tuning for Pipe Elbow Testing [J]. Material Evaluation,2001,59:621-624.
[34] J.L. Rose, S.P. Pelts, M.J. Quarry. A comb transducer model for guided wave [J]. NDE, Ultrasonics,1998,36(1-5):163-169.
[35] M.G. Silk, K.F. Bainton. The propagation in metal tubing of ultrasonic wave modes equivalent to waves[J]. Ultrasonics,1979,17(1):11-19.
[36] Alleyne, D.N. and Cawley, P. Long range propagation of Lamb waves in chemical plant pipework [J],Materials Evaluation,1997,55:504-508.
[37] I. Daubechies. CBM S-NSF regional conference series in applied mathematics [R]. Dept. ofMathematics, Univ. of Low ell MA., Society for Industrial and Applied Mathematics, Philadelphia,1992.
[38] K. Gurley, A. Kareem. Application of wavelet transform in earthquake, wind and ocean engineering [J].Eng. Struct.,1999,21:149-167.
[39]邱颖,任青文,朱建华.基于小波奇异性的梁结构损伤诊断[J].工程力学,2005,22:146-151.
[40]马宏伟,张伟伟,王志华.小波分析在悬臂梁裂纹识别中的应用[J].计算力学学报,2008,25(5):734-740.
[41] Z.K. Hou, M. Noori, R.S. Amand. Wavelet-based approach for structural damage detection [J]. Journalof EM, ASCE,2000,126(7):677-683.
[42] G. Yan, Z. Duan, J. Ou, A. De Stefano. Structural damage detection using residual forces based onwavelet transform [J]. Mechanical Systems and Signal Processing,2010,24(1):224-239.
[43] W. Fan, P. Qiao. A2-D continuous wavelet transform of mode shape data for damage detection of platestructures [J]. International Journal of Solids and Structures,2009,46(25-26):4379-4395.
[44] J. Grabowska, M. Palacz, M. Krawczuk. Damage identification by wavelet analysis [J]. MechanicalSystems and Signal Processing,2008,22(7):1623-1635.
[45] M. Mehrjoo, N. Khaji, H. Moharrami, A. Bahreininejad. Damage detection of truss bridge joints usingArtificial Neural Networks [J]. Expert Systems with Applications,2008,35(3):1122-1131.
[46] X. Fang, H. Luo, J. Tang. Structural damage detection using neural network with learning rateimprovement [J]. Computers&Structures,2005,83(25–26):2150-2161.
[47] S. Jiang, C. Zhang, S. Zhang. Two-stage structural damage detection using fuzzy neural networks anddata fusion techniques [J]. Expert Systems with Applications,2011,38(1):511-519.
[48] Ahmed A. Elshafey, Mahmoud R. Haddara, H. Marzouk. Damage detection in offshore structures usingneural networks [J]. Marine Structures,2010,23(1):131-145.
[49] A.J. Oberholster, P.S. Heyns. On-line fan blade damage detection using neural networks [J]. MechanicalSystems and Signal Processing,2006,20(1):78-93.
[50] M. Sahin, R.A. Shenoi. Quantification and localization of damage in beam-like structures by usingartificial neural networks with experimental validation [J]. Engineering Structures,25(14):1785-1802.
[51]陈长征,罗跃纲,白秉三,唐忠.结构损伤识别与智能诊断[M].科学出版社.2001.
[52] R. Perera, A. Ruiz, C. Manzano. Performance assessment of multicriteria damage identification geneticalgorithms [J]. Computers and Structures,2009,87(1-2):120-127.
[53] M. Nobahari, S.M. Seyedpoor. Structural damage detection using an efficient correlation-based indexand a modified genetic algorithm [J]. Mathematical and Computer Modelling,2011,53(9-10):1798-1809.
[54] H.M. Gomes, N.R.S. Silva. Some comparisons for damage detection on structures using geneticalgorithms and modal sensitivity method [J]. Applied Mathematical Modelling,2008,32(11):2216-2232.
[55] Chaekuk Na, Sun-Pil Kim, Hyo-Gyoung Kwak. Structural damage evaluation using genetic algorithm[J]. Journal of Sound and Vibration,2011,330(12):2772-2783.
[56] C. Mares, C. Surace. An application of genetic algorithms to identify damage in elastic structure [J].Journal of Sound and Vibration,1996,195(2):195-215.
[57] M.A Rao, J. Srinivas, B.S.N. Murthy. Damage detection in vibrating bodies using genetic algorithms [J].Computers&Structures,2004,82:963-968.
[58] J. Chou, J. Ghaboussi. Genetic algorithm in structural damage detection [J]. Computers&Structures,2001,79(14):1335-1353.
[59] M. Bassevillea, L. Mevelb, M. Goursat. Statistical model-based damage detection and localization:subspace-based residuals and damage-to-noise sensitivity ratios [J]. Journal of Sound and Vibration,2004,275(3-5):769-794.
[60] X.Y. Li, S.S. Law. Structural damage detection with statistical analysis from support excitation [J].Mechanical Systems and Signal Processing,2008,22(8):1793-1808.
[61] S. Gupta, D.S. Singh, A. Ray. Statistical pattern analysis of ultrasonic signals for fatigue damagedetection in mechanical structures [J]. NDT&E International,2008,41(7):491-500.
[62] A. Tomaszewska. Influence of statistical errors on damage detection based on structural flexibility andmode shape curvature [J]. Computers and Structures,2010,88(3-4):154-164.
[63] J.L. Zapico-Valle, M. Garcia-Dieguez, M.P. Gonzalez-Martinez, K.Worden. Experimental validation of anew statistical process control feature for damage detection [J]. Mechanical Systems and SignalProcessing,2011,25(7):2513-2525.
[64] D.D. Rizosa, S.D. Fassoisa, Z.P. Marioli-Rigab, A.N. Karanika.Vibration-based skin damage statisticaldetection and restoration assessment in a stiffened aircraft panel [J]. Mechanical Systems and SignalProcessing,2008,22(2):315-337.
[65] D.C. Kammer, R.D. Brillhart. Optimal sensor placement for modal identification usingsystem-realization methods. Journal of Guidance [J], Control and Dynamics,1996,19:729-31.
[66] N. Imamovic, D. Moore. Model validation of large finite element model using test data [D]. Ph.Ddissertion, imperial College London,1998.
[67] Heo, M.L.Wang, D.Satpathi. Optimal Transducer Placement for Health Monitoring of Long Span Bridge[J]. Soil Dynamics and Earthquake Engineering,1997,16:495-502.
[68] T. Breitfeld. A Method for Identification of a Set of Optimal Measurement Points for ExperimentalModal Ayalysis [J]. The International Journal of Analytical and Experimental Modal Analysis,1996,11:254-273.
[69] G.C. Thomas, R.D. Clark. A modal test design strategy for modal correlation [C]. In: Proceedings of13th international modal analysis conference, New York,1995:927-933.
[70] H.B. Kin, Y.S. Park. Sensor placement guide for structural joint stiffness model improvement [J].Mechanical systems and signal processing,1997,11(5):651-672.
[71] Z.N. Li, J.Tang, Q.S. Li. Optimal sensor location for structural vibration measurements [J]. AppliedAcoustics,2004,65:807-818.
[72]卢伟,腾军.基于数据融合的传感器优化布置方法[J].振动与冲击,2009,28(9):52-55.
[73]谢强,薛松涛.结构健康监测传感器优化布置得混合算法[J].同济大学学报(自然科学版),2006，34(6):726-731.
[74]何浩祥,闫维明,张爱林.面向结构健康监测的传感器数量及位置优化研究[J].振动与冲击,2008,27(9):131-134.
[75] T.G. Cobb, B.S. Liebst. Sensor location prioritization and structural damage localization using minimalsensor information [J]. AIAA,1996,35(2):369-374.
[76] Z.Y. Shi, S. Law, L.M. Zhang. Optimizing sensor placement for structural damage detection[J], Journalof Energy Machines,2000,126(11):1173-1179.
[77]刘晖,翟伟廉,袁润章.基于灵敏度分析的结构损伤识别中的传感器优化布置[J].地震工程与工程振动,2003,23(6):85-90.
[78]赵俊.结构健康监测中的测点优化布置方法研究[D].广州:暨南大学理工学院,2011.
[79] J. Li, S.S. Law. Damage identification of a target substructure with moving load excitation [J].Mechanical Systems and Signal Processing,2012,30:78-90.
[80] L. Yu, T. Chan. Recent research on identification of moving loads on bridges [J]. Journal of Sound andVibration,2007,305:3-21.
[81] D.F. Mazurek, J.T. DeWolf. Experimental Study of Bridge Monitoring Technique [J].Journal ofStructural Engineering,1990,116(9):2532-2549.
[82] S.S. Law, X.Q. Zhu. Dynamic behavior of damaged concrete bridge structures under moving vehicularloads [J]. Engineering Structures,2004,26:1279-1293.
[83] H.P. Lin, S.C. Chang. Forced responses of cracked cantilever beams subjected to a concentrated movingload [J]. International Journal of Mechanical Sciences,2006,48:1456-1463.
[84] J. Yang, Y. Chen, Y. Xiang, X.L. Jia. Free and forced vibration of cracked inhomogeneous beams underan axial force and a moving load [J]. Journal of Sound and Vibration,2008,312:166-181.
[85] Z.R. Lu, J.K. Liu，Huang M.，Xu W.H. Identification of local damages in coupled beam systems frommeasured dynamic responses [J]. Journal of Sound and Vibration,2009,326:177–189.
[86] X.Q. Zhu, S.S. Law. Wavelet-based crack identification of bridge beam from operational deflection timehistory [J]. International Journal of Solids and Structures,2006,43:2299–2317.
[87] Khoa Viet Nguyen，Hai Thanh Tran.Multi-cracks detection of a beam-like structure based on theon-vechicle vibration signal and wavelet analysis [J]. Journal of Sound and Vibration,2010,329:4455-4465
[88] Z. Sun, C.C. Chang. Statistical Wavelet-based method for structural health monitoring [J]. Journal ofStructural Engineering, ASCE.2004,130(7):1055-1062.
[89]张辉等.基于小波包变换的滚动轴承故障诊断方法的研究[J].振动与冲击,2004(4):127-130.
[90] Ding Y L, Li A Q, Miao C Q. Theoretical research on structural damage alarming of long-span bridgesusing wavelet packet analysis [J]. Journal of Southeast University (English Edition),2005,21(4):459-462.
[91]丁幼亮,李爱群,缪长青.基于小波包能量谱的结构损伤预警方法研究[J].工程力学,2006,23(8):42-48.
[92] G.H. James, G.T. Garne. The natural excitation technique (NExT) for modal parameter extraction fromambientoperating structure [J]. Int J Analy Exper Mod Anal,1995,10(4):260-277.
[93]段忠东,闫桂荣,欧进萍.土木工程结构振动损伤识别面临的挑战[J].哈尔滨工业大学学报,2008,40(4):505-513.
[94] M. Todd, J.M. Nichols, L.M. Pecora, L. Virgin. Vibration-based damage assessment utilizing state spacegeometry changes: local attractor variance ratio [J], Smart Materials and Structures,2001,10:1000-1008.
[95] I. Trendafilova. State space modeling and representation for vibration-based damage assessment [J]. KeyEngineering Materials,2003,245-246:547-556.
[96] J.M. Nichols, L.N. Virgin, M.D. Todd, J.D. Nichols. On the use of attractor dimension as a feature instructural health monitoring [J]. Mechanical Systems and Signal Processing,2003,17(6):1305-1320.
[97] J.M. Nichols, M.D. Todd, J.R. Wait. Using state space predictive modeling with chaotic interrogation indetecting joint preload loss in a frame structure experiment [J]. Smart Materials and Structures,2003,12:580-601
[98] E. Figueiredo, M.D. Todd, C.R. Farrar, E. Flynn. Autoregressive modeling with state-space embeddingvectors for damage detection under operational variability [J]. International Journal of EngineeringScience,2010,48:822-834.
[99] L. Moniza, J.M. Nichols, C.J. Nichols, M. Seaver, S.T. Trickey, M.D. Todd, L.M. Pecora, L.N. Virgin.A multivariate, attractor-based approach to structural health monitoring [J]. Journal of Sound andVibration,2005,283:295-310.
[100] L. A. Overbey, M.D. Todd. Analysis of Local State Space Models for Feature Extraction in StructuralHealth Monitoring [J]. Structural Health Monitoring,2007,6:145-172.
[101] J.M. Nichols, C.J. Nichols, M.D. Todd, M. Seaver, S.T. Trickey, L.N. Virgin. Use of data-driven phasespace models in assessing the strength of a bolted connection in a composite beam [J]. Smart Materialsand Structures,2004,13:241-250.
[102] J.M. Nichols, S.T. Trickey, M. Seaver. Damage detection using multivariate recurrence quantificationanalysis [J]. Mechanical Systems and Signal Processing,2006,20:421-437.
[103] J.M. Nichols, S.T. Trickey, M.D. Todd, L.N. Virgin. Structural Health Monitoring Through ChaoticInterrogation [J]. Meccanica,2003,38:239-250.
[104] C.C. Olson, M.D. Todd, K. Worden, C.R Farrar. Improving Excitations for Active Sensing in StructuralHealth Monitoring via Evolutionary Algorithms [J]. Journal of Vibration and Acoustics,2007,129:784-802.
[105] S Torkamani, E.A. Butcher, M.D. Todd, G. Park. Detection of system changes due to damage using atuned hyperchaotic probe [J]. Smart Materials and Structures,2011,20:1-16.
[106] J. Ryue, P.R. White. The detection of cracks in beams using chaotic excitations [J]. Journal of Soundand Vibration,2007,307:627-638.
[107] J.M. Nichols. Structural health monitoring of offshore structures using ambient excitation [J]. AppliedOcean Research,2003,25:101-114.
[108] L.A. Overbey, C.C. Olson, M.D. Todd. A parametric investigation of state-space-based prediction errormethods with stochastic excitation for structural health monitoring [J]. Smart Materials and Structures,2007,16:1621-1638.
[109] Vikram Pakrashi, Alan O’Connor, Biswajit Basu. A Bridge-Vehicle Interaction Based ExperimentalInvestigation of Damage Evolution [J]. Structural Health Monitoring,2010,9:285-296.
[110] Zhenhua Nie, Hong Hao, Hongwei Ma. Structural Damage Detection Using Phase Space GeometryChanges [C]. EVACS’11,2011,1:407-416.
[111] H. Leung, X. Huang, Parameter estimation in chaotic noise [J]. IEEE Trans. Signal Process,1996,44(10):2456-2463.
[112]聂春燕.混沌系统与弱信号检测[M].清华大学出版社,北京,2009.
[113] Yue Li, Baojun Yang, Ye Yuan, Xiaohua Liu. Analysis of a kind of Duffing oscillator system used todetect weak signals [J]. Chinese Physics,2007,16(4):1072-1076.
[114] Wang Yongsheng, Ma Xiangling, Wei Yu, Fan Hongda. A New Method of Weak Signal DetectionUsing Chaos Phase Change [C]. The Eighth International Conference on Electronic Measurement andInstruments,2007,3:812-816.
[115]李义方,陈艳峰.基于混沌振子的微弱生命周期信号频率检测方[J].计算机工程与科学,2011,33(5):183-189.
[116]李月,杨宝俊,石要武.色噪声背景下微弱正弦信号的混沌检测[J].物理学报,2003,52(3):526-530.
[117] N.Q. Hu, X.S. Wen.The application of Duffing oscillator in characteristic signal detection of early fault[J]. Journal of Sound and Vibration,2003,268:917-931.
[118] J.M. Nelson, D. Cruikshank, S. Galt. A flexible methodology for analysis of multiple-modality NDEdata [J]. Review of progress in QNDE1989,8:819-826.
[119] M.A. Shahawy, M. Arocklasamy. Field instrument to study the time-dependent behavior in sunshineskyway bridge [J]. Journal of bridge engineering, ASME,1996,1(2):76-86.
[120] K.Y. Wong. Planning and implementation of the structural health monitoring system for cable-supportedbridges in Hong Kong [C]. Nondestructive evaluation of highways, Utilities, and Pipelines IV,Proceedings of SPIE,2000,3995:275-288.
[121] U. Lee, H.S. Yoo. Spectral elementmethod: applica-tion to structural damage identification [C].Proceed-ings of SPIE.2003,5062:712-719.
[122]聂振华,程良彦,马宏伟.基于结构动力特性的损伤检测可视化方法[J].振动与冲击,2011,30(12):7-13.
[123] Zhenhua Nie, Hongwei Ma. Visualization of Structural Damage Detection Using Vibration-BasedIdentification Techniques [J]. Advanced Science Letters,2011,4:3124-3130.
[124] Zhenhua Nie, Liangyan Cheng, Jun Zhao, Hongwei Ma. Visualization of Damage Detection for CircularArch Based on Stochastic Subspace Identification [C]. International Conference on ArtificialIntelligence and Computational Intelligence,2010,2:127-130.
[125] F. Takens. Detecting strange attractors in turbulence [R]. In: Rand, D. and Young, L.S.(eds), DynamicalSystems and Turbulence: Springer Lecture Notes in Mathematics,1981,898:366-381, New York, NY:Springer.
[126] N.H. Packard, J.P. Crutchfield, J.D. Farmer et al.. Geometry from a time series [J]. Physical ReviewLetters,1980,45(3):712-716.
[127] P.E. Rapp, T.I Schmah, A.I. Mees. Models of knowing and the investigation of dynamical systems [J].Physica D,1992,132:133-149.
[128] M.T. Rosenstein, J.J. Collins, C.J. De Luca. A Practical method for calculating largest Lyapunovexponents from small data sets [J]. Physica D,1993,65:117-134.
[129] H. Tong. Non-linear time series analysis [D]. Oxford University, UK,1990.
[130] A.M. Fraser, H.L. Swinney. Independent coordinates for strange attractors from mutual information [J].Physical Review A,1986,33(2):1134-1140.
[131]杨志安，王光瑞，陈式刚.用等间距分格子法计算互信息函数确定延迟时间[J].计算物理,1995,12(4):442-448.
[132] M.T. Rosenstein, J.J. Collins, C.J. De Luca. Reconstruction expansion as a geometry-based frameworkfor choosing proper delay times [J]. Physica D,1994,73:82-98.
[133] G.A. Dyrbellay, D. Wuertz. The entropy as a tool for analyzing statistical dependences in financial timeseries [J]. Physica A,2000,287:429-439.
[134] D.S. Broomhead, G.P. King. Extracting qualitative dynamics from experimental data [J]. Physica D,1986,20(2-3):217-236.
[135] L.A. Overbey, C.C. Olson, M.D. Todd. A parametric investigation of state-space-based prediction errormethods with stochastic excitation for structural health monitoring [J]. Smart Materials and Structures,2007,16(5):1621-1638.
[136] M.B. Kennel, R. Brown, H.D.I. Abarbanel. Determining embedding dimension for phase-spacereconstruction using a geometrical construction [J]. Physical Review A,1992,45(6):3403-3411.
[137] Daniel T. Kaplan, Leon Glass. Coarse-grained embeddings of time series: random walks, Gaussianrandom processes, and deterministic chaos [J]. Physica D: Nonlinear Phenomena,1993,64(4):431-454.
[138]王跃科,林嘉宇,黄芝平.混沌信号处理[J].国防科技大学学报,2000,22(5):73-77.
[139]龙海辉.基于混沌时间序列相空间重构研究[D].长沙：中南大学,2009.
[140] J.M. Nichols, J.D. Nichols. Attractor reconstruction for non-linear system: a methodological note [J].Mathematical Biosciences,2001,171(1):21-32.
[141] J.M. Nichols, M.D. Todd, M. Seaver, L.N. Virgin, Use of chaotic excitation and attractor propertyanalysis in structural health Monitoring [J]. Physical Review E,2003,67(1):1-8.
[142] Trickey ST, Todd MD, Nichols JM, Seaver ME. Considerations for attractor property analysis invibration based damage detection [C]. Proceedings of the SPIE,2003,5047-5056.
[143] Cunha A，Caetano E，Calcada R，Delgado R. Modal Identification and Correlation with Finite ElementParameters of Vasco da Gama Bridge In Proceedings of IMAC l7[C]. FL, USA: Kissimmee, l999,705-7l1.
[144] F. Necati Catbas, Mustafa Gul, Jason L, Burkett. Conceptual Damage-sensitive Features for StructuralHealth Monitoring: Laboratory and Field Demonstrations [J]. Mechanical Systems and SignalProcessing,2008,22:1650-1669.
[145] F. Necati Catbas, David L.Brown, A. Emin Aktan. Parameter Estimation for Multiple-InputMultiple-Output Modal Analysis of Large Structures [J]. Journal of Engineering Mechanics,2004,130(8):921-930.
[146]王兆辉，樊可清，李霆.系统辨识在桥梁状态监测中的应用[J].中国公路工程，2006,31(3):159-163.
[147] Jian-Huang Weng, Chin-Hsiung Loh, Jerome P. Lynch, Kung-Chun Lu, Pei-Yang Lin, Yang Wang.Output-only Modal Identification of a Cable-stayed Bridge Using Wireless Monitoring Systems [J].Engineering Structures,2008,30:1820-1830.
[148]许可,孙守光,金新灿,李强.随机减量法识别转向架构架的频率[J].铁道机车车辆,2007,27(2):16-18.
[149] Z.Y. Shi, S.S.Law, X.Xu. Identification of Linear Time-varying MODF Dynamic Systems from ForcedExcitation Using Hilbert Transform and EMD Method [J]. Journal of Sound and Vibration,2009,321:572-589.
[150] Wei-Xin Ren, I. E. Harik. Modal analysis of the Cumberland river bridge on I-24highway in WestKentucky [A]. Proc. Of IMAC19[C], Kissimmee, Florida, USA,2001,21-27.
[151] G.H. James, T.G. Came, J.P. Lauffer. The Natural Excitation Technique (NExT) for Modal ParameterExtraction from operating Structures [J]. The International Journal of Analytication Experimental ModalAnalysis,1995,10(4):260-277.
[152]王冠宇,陈大军,林建亚. Duffing振子微弱信号检测方法的统计特性研究[J].电子学报,1998,26(10):38-43.
[153]王冠宇,陶国良,陈行.混沌振子在强噪声背景信号检测中的应用[J].仪器仪表学报,1997,18(2):209-212.
[154]汤杰.基于混沌振子系统的管道超声导波检测技术研究[D].暨南大学,2010.
[155]张雨.时间序列的混沌和符号分析及实践[M].国防科技大学出版社,长沙,2007.
[156] Perter Smith. Explaining chaos[M]. Cambridge University Press,1998.
[157]聂春燕,石要武.基于混沌检测弱信号的混沌特性判别方法的研究[J].计量力学,2000,21(4):308-311.
[158]陈予恕．非线性动力学和混沌基础[M].东北师范大学出版社,长春,1994.
[159]刘秉正，彭建华.非线性动力学[M].北京:高等教育出版社，2004.
[160] H. Haken. Advanced Synergetics[M]. Berlin: Springer-Verlag,1983.
[161] A.Wolf, J.B. Swift, H.L. Swinney, J.A. Vastano. Determining Lyapunov exponents from a time series[J]. Physica16D,1985,16(3):285-317.
[162] M.T. Rosenstein, J. Collins, C.J. De Luca. A practical method for calculating largest Lyapunovexponents from small data sets [J]. Physica D,1993,65:117-134.
[163]杨绍清,章新华,赵长安.一种最大李雅普诺夫指数估计的稳健算法[J].物理学报,2000,49(4):636-640.
[164] Romaric Audigier, Roberto Lotufo, Alexandre Falcao.3D visualization to assist iterative objectdefinition from medical images [J]. Computerized Medical Imaging and Graphics,2006,30:217-230.
[165] Deng-Hua Zhong, Ming-Chao Li, Ling-Guang Song, Gang Wang. Enhanced NURBS modeling andvisualization for large3D geoengineering applications: An example from the Jinpingfirst-levelhydropower engineering project, China [J]. Computers&Geosciences,2006,32:1270-1282.
[166] H. Haase, M. Bock, E. Hergenrokther, C. Knok. Meteorology meets computer graphics-a look at a widerange of weather visualisations for diverse audiences [J]. Computers&Graphics,2000,24:391-397.
[167] X.E. Gros. NDT Data Fusion [M]. London: Arnold Publisher,1996.
[168] X.E. Gros, Strachan P, Lowden DW, Edwards1. NDT data fusion [C]. In: Proc. of the6th EuropeanConf. on NDT,1994,1:31-35.
[169] K. Worden, G. Manson, T. Denoeux. An evidence-based approach to damage location on an aircraftstructure [J]. Mechanical Systems and Signal Processing,2009,23(6):1792-1804.
[170] Heung Fai Lam, Ching Tai Ng. The selection of pattern features for structural damage detection using anextended Bayesian ANN algorithm [J]. Engineering Structures,2008,30(10):2762-2770.
[171]廖惜春，丘敏，麦汉荣.基于参数估计的多传感器数据融合算法研究[J].传感器学报,2007,20(1):193-197.
[2]聂振华.基于应变模态的圆拱结构损伤识别方法研究[D].广州：暨南大学理工学院,2008.
[3] C.R. Farrar, W.E. Baker, T.M. Bell et.al. Dynamic characterization and damage detection in the I-40bridge over the Rio Grand[R]. Los Alamos national Laboratory report. LA-12767-MS.1994.
[4]张伟伟.基于模态分析和应力波技术的结构裂纹识别方法研究[D].广州：暨南大学理工学院，2010.
[5] D. Diamantidis. Implementation of reliability-based criteria for structural integrity assessment ofexisting structures [J]. key engineering materials,2009,413-414:203-210.
[6] J.R. Maguire. Residual life assessment of electricity pylons-a case study [J]. Key Engineering materials,2009,413-414:219-228.
[7] J.D. Hios, S.D. Fassois. Statistical damage detection in a smart structure under different temperatures viavibration testing: a gloable model based approach [J]. Key engineering materials,2009,413-414:261-268.
[8]马宏伟,杨桂通.结构损伤探测的基本方法和研究进展[J].力学进展,1999,29(4):513-527.
[9] S.W. Doebling, C.R. Farrar, M.B. Prime. A summary review of vibration-based damage identificationmethods [J]. The Shock and Vibration Digest,1998,30(2):91-105.
[10]张伟伟,程良彦,王志华,马宏伟.阶梯悬臂梁的双裂纹参数两步识别法[J].机械强度,2008,30(4):534-538.
[11] R.D. Adams et.al. A Vibration Technique for Non-Destructively Assessing the Integrity of Structures [J].Journal of Mechanical Engineering Science,1978,20:93-100.
[12] P. Cawley, R.D. Adams. The Location of Defects in Structures from Measurements of NaturalFrequencies [J]. Journal of Strain Analysis,1979,14(2):49-57.
[13] C.R. Farrar, P.J. Comwell et. Structual health monitoring studies of the Alamosa Canyon and I-40bridge
[R]. Los Alamos, NU, USA: Los Alamos National Laboratory,2000.
[14] O.S. Salawu. Detection of Structural Damage through changes in Frequency: A Review [J]. EngineeringStructures,1997,19(9):718-723.
[15] Y. Xia, H. Hao, G. Zanardo, A. Deeks. Long term vibration monitoring of an RC slab: Temperature andhumidity effect [J]. Engineering Structures,2006,28(3):441-452.
[16] R.J. Allemany, D.L. Brown. A Correlation Coefficient for Modal Vector Analysis [C]. Proc. of the1stInternational Modal Analysis Conference,1982,110-116.
[17] N. Lieven, D. Ewins. Spatial Correlation of Modal Shapes, the Coordinate Modal Assurance Criterion(COMAC)[C]. Proceedings6th IMAC,1998, I:690-695.
[18] D.L. Hunt. Applicrion of an Enhanced Coordinate Modal Assurance Criteria [C].10th InternationalModal Analysis Conference, San Diego, Califomia,1992.
[19] Zhenhua Nie, Hongwei Ma. Visualization of Structural Damage Detection Using Vibration-BasedIdentification Techniques [J]. Advanced Science Letters,2011,4:3124-3130.
[20] A.K. Pandey, M. Biswas, M.M. Samman. Damage detection from change in curvature mode shapes [J].Journal of Sound and Vibration,1991,145(2):321-332.
[21]孙晓丹,欧进萍.基于动力检测的损伤指标评价方法[J].振动与冲击,2009,28(1):9-13.
[22] A.K. Pandy, M. Biswas. Damage detection in structures using changes in flexibility [J]. Journal ofSound and Vibration.1994,169(1):3-17.
[23] A.E Aktan, K.L. Lee, C. Chuntavan, T. Aksel. Modal testing for structural identification and conditionassessment of constructed facilities [C]. Proceedings of12th International Modal AnalysisConference,1994.
[24] E.P. Carden, James M.W. Brownjohn. ARMA modelled time-series classification for structural healthmonitoring of civil infrastructure [J]. Mechanical Systems and Signal Processing,2008,22:295-314.
[25] K. Krishnan Naira, Anne S. Kiremidjianb, Kincho H. Law. Time series-based damage detection andlocalization algorithm with application to the ASCE benchmark structure [J]. Journal of Sound andVibration,2006,291:349-368.
[26] Mustafa Gul, F. Necati Catbas. Structural health monitoring and damage assessment using a novel timeseries analysis methodology with sensor clustering [J]. Journal of Sound and Vibration,2011,330:1196-1210.
[27] S. Choia, N. Stubbs. Damage identification in structures using the time-domain response [J]. Journal ofSound and Vibration,2004,275(3-5):577-590.
[28] Luna Majumder, C.S. Manohar. A time-domain approach for damage detection in beam structures usingvibration data with a moving oscillator as an excitation source [J]. Journal of Sound and Vibration,2003,268(4):699-716.
[29] M. Link, M. Weiland. Damage identification by multi-model updating in the modal and in the timedomain [J]. Mechanical Systems and Signal Processing,2009,23(6):1734-1746.
[30] I. Trendafilova, E. Manoach. Vibration-based damage detection in plates by using time series analysis [J].Mechanical Systems and Signal Processing,2008,22(5):1092-1106.
[31] Hayashi T, Tamayama C, Murase M. Wave structure analysis of guided waves in a bar with an arbitrarycross-section.[J]. Ultrasonics.2006,44,17-24.
[32]马宏伟,吴斌.弹性动力学及其数值方法[M].中国建材工业出版社,2000,65-89.
[33] J.L. Rose, X.L. Zhao. Flexural Mode Tuning for Pipe Elbow Testing [J]. Material Evaluation,2001,59:621-624.
[34] J.L. Rose, S.P. Pelts, M.J. Quarry. A comb transducer model for guided wave [J]. NDE, Ultrasonics,1998,36(1-5):163-169.
[35] M.G. Silk, K.F. Bainton. The propagation in metal tubing of ultrasonic wave modes equivalent to waves[J]. Ultrasonics,1979,17(1):11-19.
[36] Alleyne, D.N. and Cawley, P. Long range propagation of Lamb waves in chemical plant pipework [J],Materials Evaluation,1997,55:504-508.
[37] I. Daubechies. CBM S-NSF regional conference series in applied mathematics [R]. Dept. ofMathematics, Univ. of Low ell MA., Society for Industrial and Applied Mathematics, Philadelphia,1992.
[38] K. Gurley, A. Kareem. Application of wavelet transform in earthquake, wind and ocean engineering [J].Eng. Struct.,1999,21:149-167.
[39]邱颖,任青文,朱建华.基于小波奇异性的梁结构损伤诊断[J].工程力学,2005,22:146-151.
[40]马宏伟,张伟伟,王志华.小波分析在悬臂梁裂纹识别中的应用[J].计算力学学报,2008,25(5):734-740.
[41] Z.K. Hou, M. Noori, R.S. Amand. Wavelet-based approach for structural damage detection [J]. Journalof EM, ASCE,2000,126(7):677-683.
[42] G. Yan, Z. Duan, J. Ou, A. De Stefano. Structural damage detection using residual forces based onwavelet transform [J]. Mechanical Systems and Signal Processing,2010,24(1):224-239.
[43] W. Fan, P. Qiao. A2-D continuous wavelet transform of mode shape data for damage detection of platestructures [J]. International Journal of Solids and Structures,2009,46(25-26):4379-4395.
[44] J. Grabowska, M. Palacz, M. Krawczuk. Damage identification by wavelet analysis [J]. MechanicalSystems and Signal Processing,2008,22(7):1623-1635.
[45] M. Mehrjoo, N. Khaji, H. Moharrami, A. Bahreininejad. Damage detection of truss bridge joints usingArtificial Neural Networks [J]. Expert Systems with Applications,2008,35(3):1122-1131.
[46] X. Fang, H. Luo, J. Tang. Structural damage detection using neural network with learning rateimprovement [J]. Computers&Structures,2005,83(25–26):2150-2161.
[47] S. Jiang, C. Zhang, S. Zhang. Two-stage structural damage detection using fuzzy neural networks anddata fusion techniques [J]. Expert Systems with Applications,2011,38(1):511-519.
[48] Ahmed A. Elshafey, Mahmoud R. Haddara, H. Marzouk. Damage detection in offshore structures usingneural networks [J]. Marine Structures,2010,23(1):131-145.
[49] A.J. Oberholster, P.S. Heyns. On-line fan blade damage detection using neural networks [J]. MechanicalSystems and Signal Processing,2006,20(1):78-93.
[50] M. Sahin, R.A. Shenoi. Quantification and localization of damage in beam-like structures by usingartificial neural networks with experimental validation [J]. Engineering Structures,25(14):1785-1802.
[51]陈长征,罗跃纲,白秉三,唐忠.结构损伤识别与智能诊断[M].科学出版社.2001.
[52] R. Perera, A. Ruiz, C. Manzano. Performance assessment of multicriteria damage identification geneticalgorithms [J]. Computers and Structures,2009,87(1-2):120-127.
[53] M. Nobahari, S.M. Seyedpoor. Structural damage detection using an efficient correlation-based indexand a modified genetic algorithm [J]. Mathematical and Computer Modelling,2011,53(9-10):1798-1809.
[54] H.M. Gomes, N.R.S. Silva. Some comparisons for damage detection on structures using geneticalgorithms and modal sensitivity method [J]. Applied Mathematical Modelling,2008,32(11):2216-2232.
[55] Chaekuk Na, Sun-Pil Kim, Hyo-Gyoung Kwak. Structural damage evaluation using genetic algorithm[J]. Journal of Sound and Vibration,2011,330(12):2772-2783.
[56] C. Mares, C. Surace. An application of genetic algorithms to identify damage in elastic structure [J].Journal of Sound and Vibration,1996,195(2):195-215.
[57] M.A Rao, J. Srinivas, B.S.N. Murthy. Damage detection in vibrating bodies using genetic algorithms [J].Computers&Structures,2004,82:963-968.
[58] J. Chou, J. Ghaboussi. Genetic algorithm in structural damage detection [J]. Computers&Structures,2001,79(14):1335-1353.
[59] M. Bassevillea, L. Mevelb, M. Goursat. Statistical model-based damage detection and localization:subspace-based residuals and damage-to-noise sensitivity ratios [J]. Journal of Sound and Vibration,2004,275(3-5):769-794.
[60] X.Y. Li, S.S. Law. Structural damage detection with statistical analysis from support excitation [J].Mechanical Systems and Signal Processing,2008,22(8):1793-1808.
[61] S. Gupta, D.S. Singh, A. Ray. Statistical pattern analysis of ultrasonic signals for fatigue damagedetection in mechanical structures [J]. NDT&E International,2008,41(7):491-500.
[62] A. Tomaszewska. Influence of statistical errors on damage detection based on structural flexibility andmode shape curvature [J]. Computers and Structures,2010,88(3-4):154-164.
[63] J.L. Zapico-Valle, M. Garcia-Dieguez, M.P. Gonzalez-Martinez, K.Worden. Experimental validation of anew statistical process control feature for damage detection [J]. Mechanical Systems and SignalProcessing,2011,25(7):2513-2525.
[64] D.D. Rizosa, S.D. Fassoisa, Z.P. Marioli-Rigab, A.N. Karanika.Vibration-based skin damage statisticaldetection and restoration assessment in a stiffened aircraft panel [J]. Mechanical Systems and SignalProcessing,2008,22(2):315-337.
[65] D.C. Kammer, R.D. Brillhart. Optimal sensor placement for modal identification usingsystem-realization methods. Journal of Guidance [J], Control and Dynamics,1996,19:729-31.
[66] N. Imamovic, D. Moore. Model validation of large finite element model using test data [D]. Ph.Ddissertion, imperial College London,1998.
[67] Heo, M.L.Wang, D.Satpathi. Optimal Transducer Placement for Health Monitoring of Long Span Bridge[J]. Soil Dynamics and Earthquake Engineering,1997,16:495-502.
[68] T. Breitfeld. A Method for Identification of a Set of Optimal Measurement Points for ExperimentalModal Ayalysis [J]. The International Journal of Analytical and Experimental Modal Analysis,1996,11:254-273.
[69] G.C. Thomas, R.D. Clark. A modal test design strategy for modal correlation [C]. In: Proceedings of13th international modal analysis conference, New York,1995:927-933.
[70] H.B. Kin, Y.S. Park. Sensor placement guide for structural joint stiffness model improvement [J].Mechanical systems and signal processing,1997,11(5):651-672.
[71] Z.N. Li, J.Tang, Q.S. Li. Optimal sensor location for structural vibration measurements [J]. AppliedAcoustics,2004,65:807-818.
[72]卢伟,腾军.基于数据融合的传感器优化布置方法[J].振动与冲击,2009,28(9):52-55.
[73]谢强,薛松涛.结构健康监测传感器优化布置得混合算法[J].同济大学学报(自然科学版),2006，34(6):726-731.
[74]何浩祥,闫维明,张爱林.面向结构健康监测的传感器数量及位置优化研究[J].振动与冲击,2008,27(9):131-134.
[75] T.G. Cobb, B.S. Liebst. Sensor location prioritization and structural damage localization using minimalsensor information [J]. AIAA,1996,35(2):369-374.
[76] Z.Y. Shi, S. Law, L.M. Zhang. Optimizing sensor placement for structural damage detection[J], Journalof Energy Machines,2000,126(11):1173-1179.
[77]刘晖,翟伟廉,袁润章.基于灵敏度分析的结构损伤识别中的传感器优化布置[J].地震工程与工程振动,2003,23(6):85-90.
[78]赵俊.结构健康监测中的测点优化布置方法研究[D].广州:暨南大学理工学院,2011.
[79] J. Li, S.S. Law. Damage identification of a target substructure with moving load excitation [J].Mechanical Systems and Signal Processing,2012,30:78-90.
[80] L. Yu, T. Chan. Recent research on identification of moving loads on bridges [J]. Journal of Sound andVibration,2007,305:3-21.
[81] D.F. Mazurek, J.T. DeWolf. Experimental Study of Bridge Monitoring Technique [J].Journal ofStructural Engineering,1990,116(9):2532-2549.
[82] S.S. Law, X.Q. Zhu. Dynamic behavior of damaged concrete bridge structures under moving vehicularloads [J]. Engineering Structures,2004,26:1279-1293.
[83] H.P. Lin, S.C. Chang. Forced responses of cracked cantilever beams subjected to a concentrated movingload [J]. International Journal of Mechanical Sciences,2006,48:1456-1463.
[84] J. Yang, Y. Chen, Y. Xiang, X.L. Jia. Free and forced vibration of cracked inhomogeneous beams underan axial force and a moving load [J]. Journal of Sound and Vibration,2008,312:166-181.
[85] Z.R. Lu, J.K. Liu，Huang M.，Xu W.H. Identification of local damages in coupled beam systems frommeasured dynamic responses [J]. Journal of Sound and Vibration,2009,326:177–189.
[86] X.Q. Zhu, S.S. Law. Wavelet-based crack identification of bridge beam from operational deflection timehistory [J]. International Journal of Solids and Structures,2006,43:2299–2317.
[87] Khoa Viet Nguyen，Hai Thanh Tran.Multi-cracks detection of a beam-like structure based on theon-vechicle vibration signal and wavelet analysis [J]. Journal of Sound and Vibration,2010,329:4455-4465
[88] Z. Sun, C.C. Chang. Statistical Wavelet-based method for structural health monitoring [J]. Journal ofStructural Engineering, ASCE.2004,130(7):1055-1062.
[89]张辉等.基于小波包变换的滚动轴承故障诊断方法的研究[J].振动与冲击,2004(4):127-130.
[90] Ding Y L, Li A Q, Miao C Q. Theoretical research on structural damage alarming of long-span bridgesusing wavelet packet analysis [J]. Journal of Southeast University (English Edition),2005,21(4):459-462.
[91]丁幼亮,李爱群,缪长青.基于小波包能量谱的结构损伤预警方法研究[J].工程力学,2006,23(8):42-48.
[92] G.H. James, G.T. Garne. The natural excitation technique (NExT) for modal parameter extraction fromambientoperating structure [J]. Int J Analy Exper Mod Anal,1995,10(4):260-277.
[93]段忠东,闫桂荣,欧进萍.土木工程结构振动损伤识别面临的挑战[J].哈尔滨工业大学学报,2008,40(4):505-513.
[94] M. Todd, J.M. Nichols, L.M. Pecora, L. Virgin. Vibration-based damage assessment utilizing state spacegeometry changes: local attractor variance ratio [J], Smart Materials and Structures,2001,10:1000-1008.
[95] I. Trendafilova. State space modeling and representation for vibration-based damage assessment [J]. KeyEngineering Materials,2003,245-246:547-556.
[96] J.M. Nichols, L.N. Virgin, M.D. Todd, J.D. Nichols. On the use of attractor dimension as a feature instructural health monitoring [J]. Mechanical Systems and Signal Processing,2003,17(6):1305-1320.
[97] J.M. Nichols, M.D. Todd, J.R. Wait. Using state space predictive modeling with chaotic interrogation indetecting joint preload loss in a frame structure experiment [J]. Smart Materials and Structures,2003,12:580-601
[98] E. Figueiredo, M.D. Todd, C.R. Farrar, E. Flynn. Autoregressive modeling with state-space embeddingvectors for damage detection under operational variability [J]. International Journal of EngineeringScience,2010,48:822-834.
[99] L. Moniza, J.M. Nichols, C.J. Nichols, M. Seaver, S.T. Trickey, M.D. Todd, L.M. Pecora, L.N. Virgin.A multivariate, attractor-based approach to structural health monitoring [J]. Journal of Sound andVibration,2005,283:295-310.
[100] L. A. Overbey, M.D. Todd. Analysis of Local State Space Models for Feature Extraction in StructuralHealth Monitoring [J]. Structural Health Monitoring,2007,6:145-172.
[101] J.M. Nichols, C.J. Nichols, M.D. Todd, M. Seaver, S.T. Trickey, L.N. Virgin. Use of data-driven phasespace models in assessing the strength of a bolted connection in a composite beam [J]. Smart Materialsand Structures,2004,13:241-250.
[102] J.M. Nichols, S.T. Trickey, M. Seaver. Damage detection using multivariate recurrence quantificationanalysis [J]. Mechanical Systems and Signal Processing,2006,20:421-437.
[103] J.M. Nichols, S.T. Trickey, M.D. Todd, L.N. Virgin. Structural Health Monitoring Through ChaoticInterrogation [J]. Meccanica,2003,38:239-250.
[104] C.C. Olson, M.D. Todd, K. Worden, C.R Farrar. Improving Excitations for Active Sensing in StructuralHealth Monitoring via Evolutionary Algorithms [J]. Journal of Vibration and Acoustics,2007,129:784-802.
[105] S Torkamani, E.A. Butcher, M.D. Todd, G. Park. Detection of system changes due to damage using atuned hyperchaotic probe [J]. Smart Materials and Structures,2011,20:1-16.
[106] J. Ryue, P.R. White. The detection of cracks in beams using chaotic excitations [J]. Journal of Soundand Vibration,2007,307:627-638.
[107] J.M. Nichols. Structural health monitoring of offshore structures using ambient excitation [J]. AppliedOcean Research,2003,25:101-114.
[108] L.A. Overbey, C.C. Olson, M.D. Todd. A parametric investigation of state-space-based prediction errormethods with stochastic excitation for structural health monitoring [J]. Smart Materials and Structures,2007,16:1621-1638.
[109] Vikram Pakrashi, Alan O’Connor, Biswajit Basu. A Bridge-Vehicle Interaction Based ExperimentalInvestigation of Damage Evolution [J]. Structural Health Monitoring,2010,9:285-296.
[110] Zhenhua Nie, Hong Hao, Hongwei Ma. Structural Damage Detection Using Phase Space GeometryChanges [C]. EVACS’11,2011,1:407-416.
[111] H. Leung, X. Huang, Parameter estimation in chaotic noise [J]. IEEE Trans. Signal Process,1996,44(10):2456-2463.
[112]聂春燕.混沌系统与弱信号检测[M].清华大学出版社,北京,2009.
[113] Yue Li, Baojun Yang, Ye Yuan, Xiaohua Liu. Analysis of a kind of Duffing oscillator system used todetect weak signals [J]. Chinese Physics,2007,16(4):1072-1076.
[114] Wang Yongsheng, Ma Xiangling, Wei Yu, Fan Hongda. A New Method of Weak Signal DetectionUsing Chaos Phase Change [C]. The Eighth International Conference on Electronic Measurement andInstruments,2007,3:812-816.
[115]李义方,陈艳峰.基于混沌振子的微弱生命周期信号频率检测方[J].计算机工程与科学,2011,33(5):183-189.
[116]李月,杨宝俊,石要武.色噪声背景下微弱正弦信号的混沌检测[J].物理学报,2003,52(3):526-530.
[117] N.Q. Hu, X.S. Wen.The application of Duffing oscillator in characteristic signal detection of early fault[J]. Journal of Sound and Vibration,2003,268:917-931.
[118] J.M. Nelson, D. Cruikshank, S. Galt. A flexible methodology for analysis of multiple-modality NDEdata [J]. Review of progress in QNDE1989,8:819-826.
[119] M.A. Shahawy, M. Arocklasamy. Field instrument to study the time-dependent behavior in sunshineskyway bridge [J]. Journal of bridge engineering, ASME,1996,1(2):76-86.
[120] K.Y. Wong. Planning and implementation of the structural health monitoring system for cable-supportedbridges in Hong Kong [C]. Nondestructive evaluation of highways, Utilities, and Pipelines IV,Proceedings of SPIE,2000,3995:275-288.
[121] U. Lee, H.S. Yoo. Spectral elementmethod: applica-tion to structural damage identification [C].Proceed-ings of SPIE.2003,5062:712-719.
[122]聂振华,程良彦,马宏伟.基于结构动力特性的损伤检测可视化方法[J].振动与冲击,2011,30(12):7-13.
[123] Zhenhua Nie, Hongwei Ma. Visualization of Structural Damage Detection Using Vibration-BasedIdentification Techniques [J]. Advanced Science Letters,2011,4:3124-3130.
[124] Zhenhua Nie, Liangyan Cheng, Jun Zhao, Hongwei Ma. Visualization of Damage Detection for CircularArch Based on Stochastic Subspace Identification [C]. International Conference on ArtificialIntelligence and Computational Intelligence,2010,2:127-130.
[125] F. Takens. Detecting strange attractors in turbulence [R]. In: Rand, D. and Young, L.S.(eds), DynamicalSystems and Turbulence: Springer Lecture Notes in Mathematics,1981,898:366-381, New York, NY:Springer.
[126] N.H. Packard, J.P. Crutchfield, J.D. Farmer et al.. Geometry from a time series [J]. Physical ReviewLetters,1980,45(3):712-716.
[127] P.E. Rapp, T.I Schmah, A.I. Mees. Models of knowing and the investigation of dynamical systems [J].Physica D,1992,132:133-149.
[128] M.T. Rosenstein, J.J. Collins, C.J. De Luca. A Practical method for calculating largest Lyapunovexponents from small data sets [J]. Physica D,1993,65:117-134.
[129] H. Tong. Non-linear time series analysis [D]. Oxford University, UK,1990.
[130] A.M. Fraser, H.L. Swinney. Independent coordinates for strange attractors from mutual information [J].Physical Review A,1986,33(2):1134-1140.
[131]杨志安，王光瑞，陈式刚.用等间距分格子法计算互信息函数确定延迟时间[J].计算物理,1995,12(4):442-448.
[132] M.T. Rosenstein, J.J. Collins, C.J. De Luca. Reconstruction expansion as a geometry-based frameworkfor choosing proper delay times [J]. Physica D,1994,73:82-98.
[133] G.A. Dyrbellay, D. Wuertz. The entropy as a tool for analyzing statistical dependences in financial timeseries [J]. Physica A,2000,287:429-439.
[134] D.S. Broomhead, G.P. King. Extracting qualitative dynamics from experimental data [J]. Physica D,1986,20(2-3):217-236.
[135] L.A. Overbey, C.C. Olson, M.D. Todd. A parametric investigation of state-space-based prediction errormethods with stochastic excitation for structural health monitoring [J]. Smart Materials and Structures,2007,16(5):1621-1638.
[136] M.B. Kennel, R. Brown, H.D.I. Abarbanel. Determining embedding dimension for phase-spacereconstruction using a geometrical construction [J]. Physical Review A,1992,45(6):3403-3411.
[137] Daniel T. Kaplan, Leon Glass. Coarse-grained embeddings of time series: random walks, Gaussianrandom processes, and deterministic chaos [J]. Physica D: Nonlinear Phenomena,1993,64(4):431-454.
[138]王跃科,林嘉宇,黄芝平.混沌信号处理[J].国防科技大学学报,2000,22(5):73-77.
[139]龙海辉.基于混沌时间序列相空间重构研究[D].长沙：中南大学,2009.
[140] J.M. Nichols, J.D. Nichols. Attractor reconstruction for non-linear system: a methodological note [J].Mathematical Biosciences,2001,171(1):21-32.
[141] J.M. Nichols, M.D. Todd, M. Seaver, L.N. Virgin, Use of chaotic excitation and attractor propertyanalysis in structural health Monitoring [J]. Physical Review E,2003,67(1):1-8.
[142] Trickey ST, Todd MD, Nichols JM, Seaver ME. Considerations for attractor property analysis invibration based damage detection [C]. Proceedings of the SPIE,2003,5047-5056.
[143] Cunha A，Caetano E，Calcada R，Delgado R. Modal Identification and Correlation with Finite ElementParameters of Vasco da Gama Bridge In Proceedings of IMAC l7[C]. FL, USA: Kissimmee, l999,705-7l1.
[144] F. Necati Catbas, Mustafa Gul, Jason L, Burkett. Conceptual Damage-sensitive Features for StructuralHealth Monitoring: Laboratory and Field Demonstrations [J]. Mechanical Systems and SignalProcessing,2008,22:1650-1669.
[145] F. Necati Catbas, David L.Brown, A. Emin Aktan. Parameter Estimation for Multiple-InputMultiple-Output Modal Analysis of Large Structures [J]. Journal of Engineering Mechanics,2004,130(8):921-930.
[146]王兆辉，樊可清，李霆.系统辨识在桥梁状态监测中的应用[J].中国公路工程，2006,31(3):159-163.
[147] Jian-Huang Weng, Chin-Hsiung Loh, Jerome P. Lynch, Kung-Chun Lu, Pei-Yang Lin, Yang Wang.Output-only Modal Identification of a Cable-stayed Bridge Using Wireless Monitoring Systems [J].Engineering Structures,2008,30:1820-1830.
[148]许可,孙守光,金新灿,李强.随机减量法识别转向架构架的频率[J].铁道机车车辆,2007,27(2):16-18.
[149] Z.Y. Shi, S.S.Law, X.Xu. Identification of Linear Time-varying MODF Dynamic Systems from ForcedExcitation Using Hilbert Transform and EMD Method [J]. Journal of Sound and Vibration,2009,321:572-589.
[150] Wei-Xin Ren, I. E. Harik. Modal analysis of the Cumberland river bridge on I-24highway in WestKentucky [A]. Proc. Of IMAC19[C], Kissimmee, Florida, USA,2001,21-27.
[151] G.H. James, T.G. Came, J.P. Lauffer. The Natural Excitation Technique (NExT) for Modal ParameterExtraction from operating Structures [J]. The International Journal of Analytication Experimental ModalAnalysis,1995,10(4):260-277.
[152]王冠宇,陈大军,林建亚. Duffing振子微弱信号检测方法的统计特性研究[J].电子学报,1998,26(10):38-43.
[153]王冠宇,陶国良,陈行.混沌振子在强噪声背景信号检测中的应用[J].仪器仪表学报,1997,18(2):209-212.
[154]汤杰.基于混沌振子系统的管道超声导波检测技术研究[D].暨南大学,2010.
[155]张雨.时间序列的混沌和符号分析及实践[M].国防科技大学出版社,长沙,2007.
[156] Perter Smith. Explaining chaos[M]. Cambridge University Press,1998.
[157]聂春燕,石要武.基于混沌检测弱信号的混沌特性判别方法的研究[J].计量力学,2000,21(4):308-311.
[158]陈予恕．非线性动力学和混沌基础[M].东北师范大学出版社,长春,1994.
[159]刘秉正，彭建华.非线性动力学[M].北京:高等教育出版社，2004.
[160] H. Haken. Advanced Synergetics[M]. Berlin: Springer-Verlag,1983.
[161] A.Wolf, J.B. Swift, H.L. Swinney, J.A. Vastano. Determining Lyapunov exponents from a time series[J]. Physica16D,1985,16(3):285-317.
[162] M.T. Rosenstein, J. Collins, C.J. De Luca. A practical method for calculating largest Lyapunovexponents from small data sets [J]. Physica D,1993,65:117-134.
[163]杨绍清,章新华,赵长安.一种最大李雅普诺夫指数估计的稳健算法[J].物理学报,2000,49(4):636-640.
[164] Romaric Audigier, Roberto Lotufo, Alexandre Falcao.3D visualization to assist iterative objectdefinition from medical images [J]. Computerized Medical Imaging and Graphics,2006,30:217-230.
[165] Deng-Hua Zhong, Ming-Chao Li, Ling-Guang Song, Gang Wang. Enhanced NURBS modeling andvisualization for large3D geoengineering applications: An example from the Jinpingfirst-levelhydropower engineering project, China [J]. Computers&Geosciences,2006,32:1270-1282.
[166] H. Haase, M. Bock, E. Hergenrokther, C. Knok. Meteorology meets computer graphics-a look at a widerange of weather visualisations for diverse audiences [J]. Computers&Graphics,2000,24:391-397.
[167] X.E. Gros. NDT Data Fusion [M]. London: Arnold Publisher,1996.
[168] X.E. Gros, Strachan P, Lowden DW, Edwards1. NDT data fusion [C]. In: Proc. of the6th EuropeanConf. on NDT,1994,1:31-35.
[169] K. Worden, G. Manson, T. Denoeux. An evidence-based approach to damage location on an aircraftstructure [J]. Mechanical Systems and Signal Processing,2009,23(6):1792-1804.
[170] Heung Fai Lam, Ching Tai Ng. The selection of pattern features for structural damage detection using anextended Bayesian ANN algorithm [J]. Engineering Structures,2008,30(10):2762-2770.
[171]廖惜春，丘敏，麦汉荣.基于参数估计的多传感器数据融合算法研究[J].传感器学报,2007,20(1):193-197.
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