结构健康监测与智能诊断技术研究
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摘要
结构健康监测技术己经成为土木工程领域研究的热点,对已建成的建筑结构设施的健康状态采取有效的措施进行诊断、准确评估和预示,具有重要的科学理论意义和工程应用价值。本文以结构健康监测与智能诊断为目的,研究了结构的损伤特征提取、智能诊断和趋势预测。
从结构损伤特征提取的角度出发,提出了一种基于EEMD和小波包变换的结构损伤特征提取方法。首先对原始信号进行EEMD分解,提取包含结构损伤信息的IMF分量,再对其进行正交小波包分解,并计算小波包相对能量分布进行ASCE结构损伤特征提取。实验证明：(1) EEMD方法应用了白噪声的剔除特性,可避免模式混叠的发生；(2)不同检测节点处不同损伤工况的IMF小波包相对能量分布有显著的差异,因而可以作为一种理想的指标来表征结构损伤特征。
为解决损伤诊断中样本缺乏的问题,提出了一种基于支持向量机的智能诊断方法。将结构振动信号进行EEMD分解后提取反映损伤信息的IMF经小波包分解后的频带能量作为特征,输入到多分类的SVM中,对结构的损伤进行诊断。该方法在学习样本数较少的情况下仍然具有较好的适应性和分类能力；选取径向基核函数取得了较高的诊断精度；但是,对于同一损伤源,采用不同节点的信号分析时,SVM的识别正确率不同。
针对单一节点信号进行损伤诊断的不确定性和片面性,提出了一种基于多传感器特征融合的SVM智能诊断方法。研究结果表明：多传感器特征融合能够使不同传感器采集的信息得到充分利用,减小了损伤检测信息的不确定性和片面性,从而提高了损伤诊断准确率。
土木结构的损伤在理论上是一个渐进过程,为了能够有效地监测这个损伤过程,研究了一种基于EEMD和HT变换的结构渐进损伤特征提取方法。首先对原始信号进行EEMD分解,提取包含结构损伤信息的IMF分量,再对其进行HT变换计算瞬时频率。对单自由度模型和多自由度模型结构刚度渐进损伤进行了仿真实验,并把这一方法应用到实际工程中。研究表明：结构损伤前后瞬时频率会发生明显的变化,并且可以准确地体现结构刚度变化的趋势,从而反映结构健康状态的发展趋势。
为了解决结构早期损伤难以正确识别的问题,结合EEMD解决随机不确定性问题和SVM解决预测问题这两者的优势,提出了一种基于EEMD特征提取的SVR结构状态趋势预测方法。通过在结构工程仿真数据和实际振动数据中的预测研究表明：该方法可以准确地、高精度地预测结构状态趋势。
Research of structural health monitoring (SHM) technology has become a hot topic in civil engineering. So it has important theoretical significance and engineering value to take effective measures to diagnose, evaluate and predict precisely the health condition of buildings in service. For the purpose of SHM and damage diagnosis, the damage feature extraction intelligent diagnosis and trend prediction for engineering structure are studied in this paper.
In order to extract damage feature, the methods of damage feature extraction are developed based on Ensemble Empirical Mode Decomposition (EEMD) and wavelet packet transform (WPT). The response signals of the ASCE benchmark structure are processed by using EEMD, the intrinsic mode function (IMF) which contains structural damage information are selected; then the selected IMF is decomposed by orthogonal WPT, and also wavelet package energy (WPE) on decomposition frequency bands are calculated to represent the structure condition. The main results are summarized as:(1) EEMD Methods which use the eliminating characteristics of white noise can avoid the occurrence of modes mixing; (2) For different kinds of damage their WPE distributions are different each other, and for a special damage the distribution of WPE is different at the different detection nodes, which can be used as an ideal target for structural damage characteristics.
Due to the problems of the sample shortage in damage diagnosis, an intelligent method is addressed based on EEMD, WPT and support vector machine (SVM). The vibration signal is decomposed using EEMD, so the IMF which contains structural damage information are selected, then the selected IMF is decomposed by orthogonal WPT as extracted features, which are input to a multi-classified SVMs to diagnose structure damage. The method still has good adaptability and classification capability in the case of small samples; and obtains higher diagnostic accuracy by using the radial basis function (RBF) as a kernel function; however, using signals from different detection nodes for the same damage, the recognition correct rate of SVM is different.
To aim at fixing the uncertainty caused by only using signals from single detection node in structure damage diagnosis, another diagnosis method is presented by means of multi-sensor feature fusion theory. Through fusing feature extracted from several different detection nodes, it can make different information complementary, and reduce the uncertainty of damage detection information. So precision and reliability of the diagnosis information is much more modified and the diagnosis accuracy was improved.
Theoretically, structure damage is progressive. In order to monitor the process efficiently, a feature extraction method of structure progressive damage is studied based on EEMD and Hilbert transform (HT). The response signals are processed by using EEMD, the IMF which contains structural damage information are selected; and then the selected IMF is transformed by using HT and instantaneous frequency(IF) are calculated. A single-degree of freedom structure model and a multi-degree of freedom structure model are used to simulate the progressive process in numerical experiments, and applying this method to practical engineering. It is shown that the IF is obviously changed before and after the structure damage occurrence. The IF can accurately reflect the tendency of structure rigidity change, and represent the developing trend of a structure health condition. So it can be takes as a feature index to monitor the structure progressive condition.
In order to solve the problem that is difficult to identify the early damage of structure, a trend prediction model of SVM based on EEMD is proposed, where the EEMD method processing stochastic uncertainty signal and the SVM regression solving the small-sample pattern recognition problem are integrated. The prediction of structural engineering simulation data and vibration data shows that the method can predict the trends of structure conditions accurately and precisely.
从结构损伤特征提取的角度出发,提出了一种基于EEMD和小波包变换的结构损伤特征提取方法。首先对原始信号进行EEMD分解,提取包含结构损伤信息的IMF分量,再对其进行正交小波包分解,并计算小波包相对能量分布进行ASCE结构损伤特征提取。实验证明：(1) EEMD方法应用了白噪声的剔除特性,可避免模式混叠的发生；(2)不同检测节点处不同损伤工况的IMF小波包相对能量分布有显著的差异,因而可以作为一种理想的指标来表征结构损伤特征。
为解决损伤诊断中样本缺乏的问题,提出了一种基于支持向量机的智能诊断方法。将结构振动信号进行EEMD分解后提取反映损伤信息的IMF经小波包分解后的频带能量作为特征,输入到多分类的SVM中,对结构的损伤进行诊断。该方法在学习样本数较少的情况下仍然具有较好的适应性和分类能力；选取径向基核函数取得了较高的诊断精度；但是,对于同一损伤源,采用不同节点的信号分析时,SVM的识别正确率不同。
针对单一节点信号进行损伤诊断的不确定性和片面性,提出了一种基于多传感器特征融合的SVM智能诊断方法。研究结果表明：多传感器特征融合能够使不同传感器采集的信息得到充分利用,减小了损伤检测信息的不确定性和片面性,从而提高了损伤诊断准确率。
土木结构的损伤在理论上是一个渐进过程,为了能够有效地监测这个损伤过程,研究了一种基于EEMD和HT变换的结构渐进损伤特征提取方法。首先对原始信号进行EEMD分解,提取包含结构损伤信息的IMF分量,再对其进行HT变换计算瞬时频率。对单自由度模型和多自由度模型结构刚度渐进损伤进行了仿真实验,并把这一方法应用到实际工程中。研究表明：结构损伤前后瞬时频率会发生明显的变化,并且可以准确地体现结构刚度变化的趋势,从而反映结构健康状态的发展趋势。
为了解决结构早期损伤难以正确识别的问题,结合EEMD解决随机不确定性问题和SVM解决预测问题这两者的优势,提出了一种基于EEMD特征提取的SVR结构状态趋势预测方法。通过在结构工程仿真数据和实际振动数据中的预测研究表明：该方法可以准确地、高精度地预测结构状态趋势。
Research of structural health monitoring (SHM) technology has become a hot topic in civil engineering. So it has important theoretical significance and engineering value to take effective measures to diagnose, evaluate and predict precisely the health condition of buildings in service. For the purpose of SHM and damage diagnosis, the damage feature extraction intelligent diagnosis and trend prediction for engineering structure are studied in this paper.
In order to extract damage feature, the methods of damage feature extraction are developed based on Ensemble Empirical Mode Decomposition (EEMD) and wavelet packet transform (WPT). The response signals of the ASCE benchmark structure are processed by using EEMD, the intrinsic mode function (IMF) which contains structural damage information are selected; then the selected IMF is decomposed by orthogonal WPT, and also wavelet package energy (WPE) on decomposition frequency bands are calculated to represent the structure condition. The main results are summarized as:(1) EEMD Methods which use the eliminating characteristics of white noise can avoid the occurrence of modes mixing; (2) For different kinds of damage their WPE distributions are different each other, and for a special damage the distribution of WPE is different at the different detection nodes, which can be used as an ideal target for structural damage characteristics.
Due to the problems of the sample shortage in damage diagnosis, an intelligent method is addressed based on EEMD, WPT and support vector machine (SVM). The vibration signal is decomposed using EEMD, so the IMF which contains structural damage information are selected, then the selected IMF is decomposed by orthogonal WPT as extracted features, which are input to a multi-classified SVMs to diagnose structure damage. The method still has good adaptability and classification capability in the case of small samples; and obtains higher diagnostic accuracy by using the radial basis function (RBF) as a kernel function; however, using signals from different detection nodes for the same damage, the recognition correct rate of SVM is different.
To aim at fixing the uncertainty caused by only using signals from single detection node in structure damage diagnosis, another diagnosis method is presented by means of multi-sensor feature fusion theory. Through fusing feature extracted from several different detection nodes, it can make different information complementary, and reduce the uncertainty of damage detection information. So precision and reliability of the diagnosis information is much more modified and the diagnosis accuracy was improved.
Theoretically, structure damage is progressive. In order to monitor the process efficiently, a feature extraction method of structure progressive damage is studied based on EEMD and Hilbert transform (HT). The response signals are processed by using EEMD, the IMF which contains structural damage information are selected; and then the selected IMF is transformed by using HT and instantaneous frequency(IF) are calculated. A single-degree of freedom structure model and a multi-degree of freedom structure model are used to simulate the progressive process in numerical experiments, and applying this method to practical engineering. It is shown that the IF is obviously changed before and after the structure damage occurrence. The IF can accurately reflect the tendency of structure rigidity change, and represent the developing trend of a structure health condition. So it can be takes as a feature index to monitor the structure progressive condition.
In order to solve the problem that is difficult to identify the early damage of structure, a trend prediction model of SVM based on EEMD is proposed, where the EEMD method processing stochastic uncertainty signal and the SVM regression solving the small-sample pattern recognition problem are integrated. The prediction of structural engineering simulation data and vibration data shows that the method can predict the trends of structure conditions accurately and precisely.
引文
[1]张玲,陈国华.事故调查分析方法与技术述评[J].中国安全科学学报,2009,19(4)：169.
[2]KO J.M., Ni Y.Q. Technology developments in structural health monitoring of large-scale bridge [J]. Engineering structures,2005,27:1715-1725.
[3]Ou J.P. Research and Practice of Intelligent health monitoring Systems for Civil Infrastructures in mainland China[C]. Proc. of Third China-Japan-US Symposium on Structural Health Monitoring and Control [A], Dalian, China,2004:3-15.
[4]Ou J.P. Recent advances on research and practical application of structural control and structural health monitoring in mainland China[C].The 4th China-Japan-US Symposium on structural Control and Monitoring & 5th Symposium on IASCM China Panel [A], Zhejiang University, China, Oct.15-17,2006:1-18.
[5]欧进萍,何政,龙旭等.振戎中学食堂耗能减震分析与设计(Ⅱ)-能力谱法与地震损伤性能控制设计[J].地震工程与工程振动,2001,21(1):115-122.
[6]李惠,欧进萍.斜拉桥结构健康监测系统的设计与实现[J].土木工程学报,2006,39(4):40-53.
[7]欧进萍,肖仪清,黄虎杰等.海洋平台结构实时安全监测系统[J].海洋工程,2001,19(2)：1-6.
[8]Huynh D., He J., Tran D.Damage location vector: A non-destructive structural damage detection technique [J]. Computers and Structures,2005,83:2353-2367.
[9]Guest editorial.Structural health monitoring, damage detection and long-term performance [J]. Engineering Structures,2005,27:1713-1714.
[10]Gaetan K., Keith W., Alexander F. V., et al. Past, present and future of nonlinear system identification in structural dynamics [J]. Mechanical Systems and Signal Processing, 2006,20:505-592.
[11]Montalvao D., Maia N. M., Ribeiro M.R. A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials [J]. The Shock and Vibration Digest,2006,38(4):295-324.
[12]Wang S.S, Ren Q.W., Qiao P.Z. Structural Damage Detection Using Local Damage Factor [J]. Journal of Vibration and Control,2006,12(9):955-973.
[13]Alberto E. J., Jesu's S. J., Roberto G. Structural damage detection using the transformation matrix [J]. Computers and Structures,2005,38:357-368.
[14]Caleb White, Henry C.H. Li, Brendan.Whittingham, et al. Damage detection in repairs using frequency response techniques [J]. Composite Structures,2009,87:175-181.
[15]Joshi M. Damage Detection in a Cantilever Beam using Energy Mode Shapes in an ANN Environment[C]. International Conference on Smart Materials Structures and Systems [A], Bangalore, India,2005,154-161.
[16]曹晖,Michael I.F.基于模态柔度曲率的损伤检测方法[J].工程力学,2006,23(4)：33-38.
[17]M. Mehrjoo, N. Khaji, H. Moharrami, et al.Damage detection of truss bridge joints using Artificial Neural Networks [J]. Expert Systems with Applications,2008, 35:1122-1131.
[18]Yan Y.J., Yam L.H., Cheng L., et al. FEM modeling method of damage structures for structural damage detection [J]. Composite Structures,2006,72:193-199.
[19]Bijaya J.S., Ren W.X. Damage Detection by Finite Element Model Updating Using Modal Flexibility Residual [J]. Journal of Sound and Vibration,2006,290(1):369-387.
[20]高维成,刘伟,邹经湘.基于结构振动参数变化的损伤探测方法综述[J].振动与冲 击,2004,23(4)：1-9.
[21]Asma F., Bouazzouni A. Finite element model updating using FRF measurements [J]. Shock and Vibration,2005,12(5):377-388.
[22]葛哲学,陈仲生.Matlab时频分析技术及其应用[M].北京：人民邮电出版社,2006,1.
[23]Claasen T A C M and Mecklenbraker W F G. The Wigner distribution - A tool for time-frequency signal analysis - Part III:Relations with other time-frequency signal transformations. Philips J. Res.,1980,35:372-389
[24]何正嘉,訾艳阳,张西宁.现代信号处理技术及应用[M].西安：西安交通大学出版社,2006:4-8.
[25]何正嘉,孟庆丰,赵纪元.机械设备非平稳信号的故障诊断原理及应用[M].北京：高等教育出版社,2001:2-4.
[26]Arnaud I. Z. The Hilbert-Huang Transform for Damage Detection in Plate Structures[J]. Master of Science,2006.
[27]钟珞,宋华珠.基于PCA与ICA的结构损伤识别[J].武汉理工大学学报,2006,28(7):93-95.
[28]严刚,周丽.应用遗传算法和散射Lamb波的板结构损伤识别[J].振动工程学报,2007,20(3):291-296.
[29]姜绍飞.基于神经网络的结构优化与损伤检测[M].北京：科学出版社,2002:110-127.
[30]Yuan S.F, Wang L., Peng G. Neural network method based on a new damage signature for structural health monitoring [J]. Thin-Walled Structures,2005,43:553-563.
[31]Xu H.P., Humar J.Mn. Damage Detection in a Girder Bridge by Artificial Neural Network Technique [J].Computer-Aided Civil and Infrastructure Engineering, 2006, 21(6):450.
[32]Yuen K., Lam H. On the complexity of artificial neural networks for smart structures monitoring [J]. Engineering Structures,2006,28:977-984.
[33]孙宗光,高赞明,倪一清.基于神经网络的损伤构件及损伤程度识别[J].工程力学,2006,23(2)：18-22.
[34]杜永峰,陈文元.小波分析与神经网络在结构损伤监测中的应用[J].兰州理工大学学报,2005,31(5)：121-124.
[35]Vapnik V N. The nature of statistical learning theory [M]. New York:Springer,1995.
[36]Vapnik V N. Statistical learning theory [M]. John Wiley & Sons, Inc.,1998
[37]边肇祺,张学工.模式识别(第二版)[M].北京：清华大学出版社,2000.
[38]Victor G., Adrian C.Embedded Non-destructive Evaluation for Structural Health Monitoring, Damage Detection, and Failure Prevention [J]. The Shock and Vibration Digest,2005,37 (2):83-105.
[39]Chang C., Chen L. Detection of the location and size of cracks in the multiple cracked beam by spatial wavelet based approach [J]. Mechanical Systems and Signal Processing, 2005,19:139-155.
[40]Knitter-Piatkowska A, Pozorski Z, Garstecki A . Application of discrete wavelet transformation in damage detection [J]. PartⅠ:Static and dynamic experiments, CAMES, 2006,1(13):21-38.
[41]Ovanesova A V, Suarez L E . Applications of wavelet transforms to damage detection in frame structures [J]. Engineering Structures,2004,26:39-49.
[42]Basu Biswajit. Identification of stiffness degradation in structures using wavelet analysis [J]. Construction and Building Materials,2005,19,713-721.
[43]Hou Z.K, Hera A., Shinde A. Wavelet-Based Structural Health Monitoring of Earthquake Excited Structures [J]. Computer-Aided Civil and Infrastructure Engineering, 2006:8467-8667.
[44]Han J.G., Ren W.X., Huang Y. A wavelet-based stochastic finite element method of thin plate bending [J]. Applied Mathematical Modelling,2007,31:181-193.
[45]Wei-Xin Ren, Zeng-Shou Sun, Yong Xia.Damage Identification of Shear Connectors with Wavelet Packet Energy: Laboratory Test Study [J]. Journal of Structural Engineering,2008,134(5):832-841.
[46]Yan Y. J., Hao H. N., Yam L. H. Vibration-based construction and extraction of structural damage feature index [J]. International Journal of Solids and Structures,2004, 41:6661-6676.
[47]郭健,孙炳楠.基于小波变换的桥梁健康监测多分辨率分析[J].浙江大学学报(工学版)2005,1,39(1)：114-118.
[48]韩建刚,任伟新,孙增寿.结构损伤识别的小波包分析试验研究[J].振动与冲击,2006,26(1):47-50.
[49]余龙,姜节胜,闫云聚.最优小波包分解在基于响应信号的结构损伤检测中的应用[J].机械强度,2007,29(6):873～876.
[50]张伟伟,王志华,马宏伟.基于小波分析的悬臂梁裂纹参数识别方法研究[J].机械强度,2007,29(2):201～205.
[51]Huang N.E., Shen Z., Long S.R., et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proc R Soc Lond A, 1998,454:903-995.
[52]Lin S., Yang J.,Zhou L. Damage identification of a benchmark building for structural health monitoring [J]. Smart Materials and Structures,2005,14:162-169.
[53]Tua P., Quek S., Wang Q.Detection of cracks in plates using piezo-actuated Lamb waves [J]. Smart Materials and Structures,2004,13:643-660.
[54]Salvino L.W., Pines D. J. Structural damage detection using empirical mode decomposition and HHT [J]. Journal of Sound and Vibration,2006,294:97-124.
[55]Liu J.B., Wang X.W., Yuan S.F. On Hilbert-Huang Transform Approach for Structural Health Monitoring [J].Journal of Intelligent Material Systems and Structures,2006, 17(8-9):721-728.
[56]Pines D., Salvino L.M. Structural health monitoring using empirical mode decomposition and the Hilbert phase [J]. Journal of Sound and Vibration,2006, 294:97-124.
[57]Hui Li, Yuping Zhang, Haiqi Zheng. Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings [J]. Journal of Mechanical Science and Technology,2009,23:291-301.
[58]Jian Xun, Shaoze Yan. A revised Hilbert-Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure [J]. Mechanical Systems and Signal Processing,2008,22:1705-1723.
[59]X. Q. Zhu, S. S. Law. Nonlinear Characteristics of Damaged Reinforced Concrete Beam from Hilbert-Huang Transform [J]. Journal of Structral Engineering,2007, 133(8):1186-1191.
[60]Dong Yinfeng, Li Yingmin, Xiao Mingkui, etal. Analysis of earthquake ground motions using an improved Hilbert-Huang transform [J]. Soil Dynamics and Earthquake Engineering,2008,28:7-19.
[61]熊飞,程远胜,刘均.基于HHT方法的时变多自由度系统的损伤识别[J].振动、测试与诊断,2008,28(2)：122-180.
[62]闫志浩,范进,左晓宝.应用Hilbert变换判断框架结构非线性的方法[J].振动与冲击,2006,25(1)：70-76.
[63]石春香,罗奇峰,施卫星.基于Hilbert-Huang变换方法的结构损伤诊断[J].同济大学学报,2005,33(1)：16-20.
[64]陈换过,闫云聚,姜节胜,等.基于振动响应和HHT技术的机翼盒段结构损伤检测方法研究[J].应用力学学报,2007,24(3)：420-425.
[65]屈文忠,陈明祥,曾又林.利用HHT方法进行结构损伤检测研究[J].武汉大学学报(工学版),2007,40(5):77-81.
[66]Wu Z H,Huang N E.Ensemble Empirical Mode Decomposition:A Noise Assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis,2009,1,1-41.
[67]Yaguo Lei, Zhengjia He, Yanyang Zi.Application of the EEMD method to rotor fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing,2009, 23(4):
[68]Haitao Li, Chengguo Wang, Yuesheng Xu.Time-Frequency Response Analysis of the Vertical Dynamics of Track-Vehicle Systems Based on EEMD[J]. RAILWAY LOCOMOTIVE & CAR,2007,28(5):24-30.
[69]Gast'on Schlotthauer, Mar'ia Eugenia and Torres Hugo L. Rufiner. A new Algorithm for Instantaneous FO Speech Extraction Based on Ensemble Empirical mode Decomposition [C].17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland, August 24-28,2009:2347-2351.
[70]曹冲锋,杨世锡,杨将新.大型旋转机械非平稳振动信号的EEMD降噪方法[J].振动与冲击,2009,28(9)：17-21.
[71]胡桥,郝保安,吕林夏,等.基于集成EMD和DEMON谱的辐射噪声特征提取研究[J].振动与冲击,2008(S):49-51.
[72]李媛媛,牛东晓,乞建勋,等.基于因散经验模式分解的电力负荷混合预测方法[J].电网技术,2008,32(8):58-62.
[73]Jianhong, Xie.Structural damage detection based on fuzzy LS-SVM integrated quantum genetic algorithm [J]. Information Technology for Manufacturing Systems,2010, 20-23:1365-1371.
[74]Hagiwara H., Mita A. Local damage assessment of a building using Support Vector Machine [J]. Advances in Earthquake Engineering,2003,61(13):235-244.
[75]Seunghee Park, Daniel J. Inman,Jong-Jae Lee,et al. Piezoelectric Sensor-Based Health Monitoring of Railroad Tracks Using a Two-Step Support Vector Machine Classifier[J]. Journal of Infrastructure Systems,2008,14(1):80-88.
[76]三田彰著.薛松涛,陈铭译.结构健康监测动力学[M].西安：西安交通大学出版社,2004.
[77]樊可清,倪一清,高赞明.基于频域系统辨识和支持向量机的桥梁状态监测方法[J].工程力学,2004,21(5)：25-30.
[78]晋侃.结构损伤检测的最小二乘支持向量回归方法研究[D].上海：同济大学,2005.
[79]何浩祥,闫维明,彭凌云.基于支持向量机的钢筋混凝土桥梁损伤识别[J].公路交通科技,2008,25(3)：65-69.
[80]赵学风,段晨东,刘义艳,等.基于小波包变换的支持向量机结构损伤诊断方法[J].振动、测试与诊断.2008,28(2)：104-107.
[81]Lu Jiyun,Liang Dakai, Zhang Xiaoli.Research on structural health monitoring for wing box based on support vector regression machine[J]. Journal of Scientific Instrument,2009, 30(3):486-491.
[82]Guo Gao, Zhang Jiang-She.Reducing examples to accelerate support vector regression [J]. Pattern Recognintion Letters 2007,7:2173-2183.
[83]Lau K W, Wu Q.H.Local prediction of non-line time series using support vector regression [J].Pattern Recongnition,2007, (41):1539-1547.
[84]Zhang Jian, Sato Tadanobu, Iai Susumu. Support vector regression for on-line health monitoring of large-scale structures [J].Structural Safety,2007, (28):392-406.
[85]Zhang Jian, Sato Tadanobu, Iai Susumu, etc.A pattern recognition technique for structural identification using observed vibration signals:Linear case studies [J]. Engineering Structures.2007,08,006.
[86]Zhang Jian, Sato Tadanobu, Iai Susumu, etc.A pattern recognition technique for structural identification using observed vibration signals:Nonlinear case studies [J]. Engineering Structures.2007,08,007.
[87]Miao Q, Wang S F.Nonlinear model predictive control based on support vectorregression. Proceedings of the First International Conference on Machine Learningand Cybernetics, Beijing,2002:1657-1661.
[88]Zhong, WM.SVM with polynomial kernel function based nonlinearmodel one-step-ahead predictive control [J]. Chinese Journal of Chemical Engineering.2005, 13(3):373-379.
[89]Bao Zhejing.SVM based one-step-ahead NMPC for MIMO systems. TheInternational Conference on Sensing, Computing, and Automation,2006.
[90]刘龙,孟光.基于支持向量机和模态频率的结构损伤诊断[J].机械强度,2006,28(3)：349-352.
[91]张浩然,韩正之,李昌刚.基于支持向量机的非线性预测控制[J].系统工程与电子技术,2003,25(03)：330-334.
[92]刘斌,苏宏业,褚健.一种基于最小二乘支持向量机的预测控制算法[J].控制与决策,2004,19(12)：1399-1402.
[93]Guo H Y.Structural damage detection using information fusion technique [J].Mechanical Systems and Signal Processing,2006,20:1173-1188.
[94]Gros X E.NDT data fusion at pixel level [J]. NDT& E International,1999(32):283-292.
[95]Verma A., Lbragimov A., Ramachandran S. Wavelet-based neural net application for feature detection and classification [J]. Neural, Parallel & Scientific Computations.2003, 11(2):19-40.
[96]Zhao Xiukuan, Limin, Gangbing, et al. Hierarchical ensemble-based data fusion for structural health monitoring [J]. Smart Materials and Structures,2010,19(4):1361-1365.
[97]Xiao Chun, Qu Weilian, Tan Dongmei.An application of data fusion technology in structural health monitoring and damage identification [J]. Smart Structures and Materials, 2005,5758:451-461.
[98]刘义艳,段晨东,巨永锋,等.基于神经网络与特征融合的损伤诊断方法[J].长安大学学报,2008,28(6)：106-110.
[99]刘涛,李爱群,缪长青,等.基于数据融合的结构损伤识别方法研究[J].工程力学,2008,25(1)：16-21.
[100]姜绍飞,张帅.基于概率神经网络分类器的数据融合损伤检测方法[J].计算力学学报,2008,25(5)：700-705.
[101]沈志熙,黄席樾,马笑潇.基于EMD和支持向量机的柴油机故障诊断[J].振动、测试与诊断,2010,30(1)：19-22.
[102]王宏禹.信号处理相关理论综合与统一法[M].北京：国防工业出版社,2005,208-244.
[103]Shekel J. Instantaneous frequency. Proc.IRE,1953,41:548.
[104]Wen-Xian Yang. Interpretation of mechanical signals using an improved Hilbert-Huang transform [J]. Mechanical Systems and Signal Processing,2008, 28:1061-1071.
[105]钟佑明,秦树人.HHT的理论依据探讨-Hilbert变换的局部乘积定理[J].振动与 冲击,2006,26(2)：12-15.
[106]Wu Z, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Process of the Royal Society of London,2003, A 460: 1597-1611.
[107]Alexander Stroeer, John K. Cannizzo, and Jordan B. Camp. Methods for detection and characterization of signals in noisy data with the Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2009,79(12):124022-1-124022-10.
[108]Wickerhauser M. V. Lecture on wavelet packet algorithms [R]. Math. Depart. Washington Univ., St. Lowis Missouri, U. S.,1991.
[109]王大凯,彭进业.小波分析及其在信号处理中的应用[M].北京：电子工业出版社,2006.
[110]刘明才.小波分析及其应用[M].北京：清华大学出版社,2005.
[111]Johnson E A, Lam H F, Katafygiotis L S. et al. Phase ⅠIASC-ASCE structural health monitoring benchmark problem using simulated data [J]. J. Eng. Mech.,2004,130(1): 3-15.
[112]Wu J R, Li Q S. Structural parameter identification and damage detection for a steel structure using a two-stage finite element model updating method [J]. Journal of constructional steel research,2006,62:231-239.
[113]Vladimir N Vapnik.张学工译.统计学习理论的本质[M].北京：清华大学出版社,2000.
[114]Theodoros Evgeniou. Learning with kernel machine architectures [D]. JMassachusetts :Massachusetts Institute Technology,2000
[115]Rich C, Steve L, Giles C L. Overfitting in Neural Networks: Backpropagation, Conjugate Gradient, and Early Stopping [A]. Advances in Neural Information Processing Systems 13 [C]. Colorado:MIT Press,2001,402-408.
[116]Steve L, Giles C L,Tsoi A C. Lessons in Neural Network Training:Overfitting May be Harder than Expected[A]. Proceedings of the Fourteenth National Conference on Artificial Intelligence[C]. California: AAAI Press,1997,545-550.
[117]边肇祺,张学工.模式识别(第二版)[M].北京：清华大学出版社,2000
[118]Hsu C W, Lin C J.A Comparion of Methods for Multiclass Support Vector Machines[J]. IEEE Transactions on Neural Network,2002,13(2):415-425.
[119]Sasiadek S. J.Z. SENSOR FUSION [J]. Annual Reviews in Control,2002, 26:203-228.
[120]Sun S.L. Multi-sensor optimal information fusion Kalman filters with applications [J]. Aerospace Science and Technology,2004,8:57-62.
[121]Telmoudi A., Chakhar S., Telmoudi A., et al. Data fusion application from evidential databases as a support for decision making [J]. Information and Software Technology, 2004,46:547-555.
[122]吕大刚,王光远.结构智能健康诊断的信息融合原理[J].哈尔滨建筑大学学报,2002,35(4)：1-5.
[123]Guo H.Y. Structural damage detection using information fusion technique [J].Mechanical Systems and Signal Processing,2006,20:1173-1188.
[124]杨露傔,耗威.多传感器目标识别的神经网络与证据理论结合方法[J].探测与控制学报,2006,28(1)：40-43.
[125]刘敏华,萧得云.基于信息熵的多传感器数据分类方法[J].控制与决策,2006,21(4):410-416.
[126]http://cive.seas.wustl.edu/wusceel/ASCE.shm/experim_phase_2.htm
[127]Men-Tzung Lo, Vera Novak, C.-K. Peng,.etal.Nonlinear phase interaction between nonstationary signals:A comparison study of methods based on Hilbert-Huang and Fourier transforms [J]. PHYSICAL REVIEW E,2009,79(6):061924-1:061924-11.
[128]Kijewski C. T., Kareem A. Efficacy of Hilbert and Wavelet Transforms for Time-Frequency Analysis [J]. JOURNAL OF ENGINEERING MECHANICS,2006, 10:1038-1049.
[129]薛定宇.控制系统计算机辅助设计[M].北京：清华大学出版社,2006:52-63.
[130]王济,胡晓编著.MATLAB在振动信号处理中的应用[M].北京：中国水利水电出版社,2006,1.
[131]周云,陈存恩,黄镇梁等.土木工程减灾防灾学[M].广州：华南理工大学出版社,2002,8.
[132]Itani Y, Tsutsumoto K, Kagawa N. Time series prediction of acoustic signals using neural network model and wavelet shrinkage [A]. Proceedings of the Tenth International Congress on Sound and Vibration[C]. Stockholm, Sweden: IIAV,2003,4189-4196.
[133]Smola A J.Learning with Kernes [D].Berlin: Technical University Berlin,1998.
[134]Chen-Chia Chuang, Shun-Feng Su, Hsiao Chih-Ching.Robust Support Vector Regression Networks for Function Approximation WithOutliers [J]. IEEE Transation on neural netwoks,2002,13(6):1322-1330.
[135]Lu Weizhen, Wan Wenjian g,Andrew Y T Leung,et al.Air Pollution Paremeter Forecasting Using Support Vector machines [A].Neural Networks,2002.Proceedings of the 2002 International Joint Conference IJCNN'02.[C],Vol:1,2002:630-635.
[136]Chen Bo-Juen, Chang Ming-Wei, Lin Chih-Jen.Load Forecasting Using Support Vector Machines:A Study on EUNITE Competition 2001 [R].Reprt for EUNITE 2001 Competition, Available at http://neuron.tuke.sk/competition/.
[2]KO J.M., Ni Y.Q. Technology developments in structural health monitoring of large-scale bridge [J]. Engineering structures,2005,27:1715-1725.
[3]Ou J.P. Research and Practice of Intelligent health monitoring Systems for Civil Infrastructures in mainland China[C]. Proc. of Third China-Japan-US Symposium on Structural Health Monitoring and Control [A], Dalian, China,2004:3-15.
[4]Ou J.P. Recent advances on research and practical application of structural control and structural health monitoring in mainland China[C].The 4th China-Japan-US Symposium on structural Control and Monitoring & 5th Symposium on IASCM China Panel [A], Zhejiang University, China, Oct.15-17,2006:1-18.
[5]欧进萍,何政,龙旭等.振戎中学食堂耗能减震分析与设计(Ⅱ)-能力谱法与地震损伤性能控制设计[J].地震工程与工程振动,2001,21(1):115-122.
[6]李惠,欧进萍.斜拉桥结构健康监测系统的设计与实现[J].土木工程学报,2006,39(4):40-53.
[7]欧进萍,肖仪清,黄虎杰等.海洋平台结构实时安全监测系统[J].海洋工程,2001,19(2)：1-6.
[8]Huynh D., He J., Tran D.Damage location vector: A non-destructive structural damage detection technique [J]. Computers and Structures,2005,83:2353-2367.
[9]Guest editorial.Structural health monitoring, damage detection and long-term performance [J]. Engineering Structures,2005,27:1713-1714.
[10]Gaetan K., Keith W., Alexander F. V., et al. Past, present and future of nonlinear system identification in structural dynamics [J]. Mechanical Systems and Signal Processing, 2006,20:505-592.
[11]Montalvao D., Maia N. M., Ribeiro M.R. A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials [J]. The Shock and Vibration Digest,2006,38(4):295-324.
[12]Wang S.S, Ren Q.W., Qiao P.Z. Structural Damage Detection Using Local Damage Factor [J]. Journal of Vibration and Control,2006,12(9):955-973.
[13]Alberto E. J., Jesu's S. J., Roberto G. Structural damage detection using the transformation matrix [J]. Computers and Structures,2005,38:357-368.
[14]Caleb White, Henry C.H. Li, Brendan.Whittingham, et al. Damage detection in repairs using frequency response techniques [J]. Composite Structures,2009,87:175-181.
[15]Joshi M. Damage Detection in a Cantilever Beam using Energy Mode Shapes in an ANN Environment[C]. International Conference on Smart Materials Structures and Systems [A], Bangalore, India,2005,154-161.
[16]曹晖,Michael I.F.基于模态柔度曲率的损伤检测方法[J].工程力学,2006,23(4)：33-38.
[17]M. Mehrjoo, N. Khaji, H. Moharrami, et al.Damage detection of truss bridge joints using Artificial Neural Networks [J]. Expert Systems with Applications,2008, 35:1122-1131.
[18]Yan Y.J., Yam L.H., Cheng L., et al. FEM modeling method of damage structures for structural damage detection [J]. Composite Structures,2006,72:193-199.
[19]Bijaya J.S., Ren W.X. Damage Detection by Finite Element Model Updating Using Modal Flexibility Residual [J]. Journal of Sound and Vibration,2006,290(1):369-387.
[20]高维成,刘伟,邹经湘.基于结构振动参数变化的损伤探测方法综述[J].振动与冲 击,2004,23(4)：1-9.
[21]Asma F., Bouazzouni A. Finite element model updating using FRF measurements [J]. Shock and Vibration,2005,12(5):377-388.
[22]葛哲学,陈仲生.Matlab时频分析技术及其应用[M].北京：人民邮电出版社,2006,1.
[23]Claasen T A C M and Mecklenbraker W F G. The Wigner distribution - A tool for time-frequency signal analysis - Part III:Relations with other time-frequency signal transformations. Philips J. Res.,1980,35:372-389
[24]何正嘉,訾艳阳,张西宁.现代信号处理技术及应用[M].西安：西安交通大学出版社,2006:4-8.
[25]何正嘉,孟庆丰,赵纪元.机械设备非平稳信号的故障诊断原理及应用[M].北京：高等教育出版社,2001:2-4.
[26]Arnaud I. Z. The Hilbert-Huang Transform for Damage Detection in Plate Structures[J]. Master of Science,2006.
[27]钟珞,宋华珠.基于PCA与ICA的结构损伤识别[J].武汉理工大学学报,2006,28(7):93-95.
[28]严刚,周丽.应用遗传算法和散射Lamb波的板结构损伤识别[J].振动工程学报,2007,20(3):291-296.
[29]姜绍飞.基于神经网络的结构优化与损伤检测[M].北京：科学出版社,2002:110-127.
[30]Yuan S.F, Wang L., Peng G. Neural network method based on a new damage signature for structural health monitoring [J]. Thin-Walled Structures,2005,43:553-563.
[31]Xu H.P., Humar J.Mn. Damage Detection in a Girder Bridge by Artificial Neural Network Technique [J].Computer-Aided Civil and Infrastructure Engineering, 2006, 21(6):450.
[32]Yuen K., Lam H. On the complexity of artificial neural networks for smart structures monitoring [J]. Engineering Structures,2006,28:977-984.
[33]孙宗光,高赞明,倪一清.基于神经网络的损伤构件及损伤程度识别[J].工程力学,2006,23(2)：18-22.
[34]杜永峰,陈文元.小波分析与神经网络在结构损伤监测中的应用[J].兰州理工大学学报,2005,31(5)：121-124.
[35]Vapnik V N. The nature of statistical learning theory [M]. New York:Springer,1995.
[36]Vapnik V N. Statistical learning theory [M]. John Wiley & Sons, Inc.,1998
[37]边肇祺,张学工.模式识别(第二版)[M].北京：清华大学出版社,2000.
[38]Victor G., Adrian C.Embedded Non-destructive Evaluation for Structural Health Monitoring, Damage Detection, and Failure Prevention [J]. The Shock and Vibration Digest,2005,37 (2):83-105.
[39]Chang C., Chen L. Detection of the location and size of cracks in the multiple cracked beam by spatial wavelet based approach [J]. Mechanical Systems and Signal Processing, 2005,19:139-155.
[40]Knitter-Piatkowska A, Pozorski Z, Garstecki A . Application of discrete wavelet transformation in damage detection [J]. PartⅠ:Static and dynamic experiments, CAMES, 2006,1(13):21-38.
[41]Ovanesova A V, Suarez L E . Applications of wavelet transforms to damage detection in frame structures [J]. Engineering Structures,2004,26:39-49.
[42]Basu Biswajit. Identification of stiffness degradation in structures using wavelet analysis [J]. Construction and Building Materials,2005,19,713-721.
[43]Hou Z.K, Hera A., Shinde A. Wavelet-Based Structural Health Monitoring of Earthquake Excited Structures [J]. Computer-Aided Civil and Infrastructure Engineering, 2006:8467-8667.
[44]Han J.G., Ren W.X., Huang Y. A wavelet-based stochastic finite element method of thin plate bending [J]. Applied Mathematical Modelling,2007,31:181-193.
[45]Wei-Xin Ren, Zeng-Shou Sun, Yong Xia.Damage Identification of Shear Connectors with Wavelet Packet Energy: Laboratory Test Study [J]. Journal of Structural Engineering,2008,134(5):832-841.
[46]Yan Y. J., Hao H. N., Yam L. H. Vibration-based construction and extraction of structural damage feature index [J]. International Journal of Solids and Structures,2004, 41:6661-6676.
[47]郭健,孙炳楠.基于小波变换的桥梁健康监测多分辨率分析[J].浙江大学学报(工学版)2005,1,39(1)：114-118.
[48]韩建刚,任伟新,孙增寿.结构损伤识别的小波包分析试验研究[J].振动与冲击,2006,26(1):47-50.
[49]余龙,姜节胜,闫云聚.最优小波包分解在基于响应信号的结构损伤检测中的应用[J].机械强度,2007,29(6):873～876.
[50]张伟伟,王志华,马宏伟.基于小波分析的悬臂梁裂纹参数识别方法研究[J].机械强度,2007,29(2):201～205.
[51]Huang N.E., Shen Z., Long S.R., et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proc R Soc Lond A, 1998,454:903-995.
[52]Lin S., Yang J.,Zhou L. Damage identification of a benchmark building for structural health monitoring [J]. Smart Materials and Structures,2005,14:162-169.
[53]Tua P., Quek S., Wang Q.Detection of cracks in plates using piezo-actuated Lamb waves [J]. Smart Materials and Structures,2004,13:643-660.
[54]Salvino L.W., Pines D. J. Structural damage detection using empirical mode decomposition and HHT [J]. Journal of Sound and Vibration,2006,294:97-124.
[55]Liu J.B., Wang X.W., Yuan S.F. On Hilbert-Huang Transform Approach for Structural Health Monitoring [J].Journal of Intelligent Material Systems and Structures,2006, 17(8-9):721-728.
[56]Pines D., Salvino L.M. Structural health monitoring using empirical mode decomposition and the Hilbert phase [J]. Journal of Sound and Vibration,2006, 294:97-124.
[57]Hui Li, Yuping Zhang, Haiqi Zheng. Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings [J]. Journal of Mechanical Science and Technology,2009,23:291-301.
[58]Jian Xun, Shaoze Yan. A revised Hilbert-Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure [J]. Mechanical Systems and Signal Processing,2008,22:1705-1723.
[59]X. Q. Zhu, S. S. Law. Nonlinear Characteristics of Damaged Reinforced Concrete Beam from Hilbert-Huang Transform [J]. Journal of Structral Engineering,2007, 133(8):1186-1191.
[60]Dong Yinfeng, Li Yingmin, Xiao Mingkui, etal. Analysis of earthquake ground motions using an improved Hilbert-Huang transform [J]. Soil Dynamics and Earthquake Engineering,2008,28:7-19.
[61]熊飞,程远胜,刘均.基于HHT方法的时变多自由度系统的损伤识别[J].振动、测试与诊断,2008,28(2)：122-180.
[62]闫志浩,范进,左晓宝.应用Hilbert变换判断框架结构非线性的方法[J].振动与冲击,2006,25(1)：70-76.
[63]石春香,罗奇峰,施卫星.基于Hilbert-Huang变换方法的结构损伤诊断[J].同济大学学报,2005,33(1)：16-20.
[64]陈换过,闫云聚,姜节胜,等.基于振动响应和HHT技术的机翼盒段结构损伤检测方法研究[J].应用力学学报,2007,24(3)：420-425.
[65]屈文忠,陈明祥,曾又林.利用HHT方法进行结构损伤检测研究[J].武汉大学学报(工学版),2007,40(5):77-81.
[66]Wu Z H,Huang N E.Ensemble Empirical Mode Decomposition:A Noise Assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis,2009,1,1-41.
[67]Yaguo Lei, Zhengjia He, Yanyang Zi.Application of the EEMD method to rotor fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing,2009, 23(4):
[68]Haitao Li, Chengguo Wang, Yuesheng Xu.Time-Frequency Response Analysis of the Vertical Dynamics of Track-Vehicle Systems Based on EEMD[J]. RAILWAY LOCOMOTIVE & CAR,2007,28(5):24-30.
[69]Gast'on Schlotthauer, Mar'ia Eugenia and Torres Hugo L. Rufiner. A new Algorithm for Instantaneous FO Speech Extraction Based on Ensemble Empirical mode Decomposition [C].17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland, August 24-28,2009:2347-2351.
[70]曹冲锋,杨世锡,杨将新.大型旋转机械非平稳振动信号的EEMD降噪方法[J].振动与冲击,2009,28(9)：17-21.
[71]胡桥,郝保安,吕林夏,等.基于集成EMD和DEMON谱的辐射噪声特征提取研究[J].振动与冲击,2008(S):49-51.
[72]李媛媛,牛东晓,乞建勋,等.基于因散经验模式分解的电力负荷混合预测方法[J].电网技术,2008,32(8):58-62.
[73]Jianhong, Xie.Structural damage detection based on fuzzy LS-SVM integrated quantum genetic algorithm [J]. Information Technology for Manufacturing Systems,2010, 20-23:1365-1371.
[74]Hagiwara H., Mita A. Local damage assessment of a building using Support Vector Machine [J]. Advances in Earthquake Engineering,2003,61(13):235-244.
[75]Seunghee Park, Daniel J. Inman,Jong-Jae Lee,et al. Piezoelectric Sensor-Based Health Monitoring of Railroad Tracks Using a Two-Step Support Vector Machine Classifier[J]. Journal of Infrastructure Systems,2008,14(1):80-88.
[76]三田彰著.薛松涛,陈铭译.结构健康监测动力学[M].西安：西安交通大学出版社,2004.
[77]樊可清,倪一清,高赞明.基于频域系统辨识和支持向量机的桥梁状态监测方法[J].工程力学,2004,21(5)：25-30.
[78]晋侃.结构损伤检测的最小二乘支持向量回归方法研究[D].上海：同济大学,2005.
[79]何浩祥,闫维明,彭凌云.基于支持向量机的钢筋混凝土桥梁损伤识别[J].公路交通科技,2008,25(3)：65-69.
[80]赵学风,段晨东,刘义艳,等.基于小波包变换的支持向量机结构损伤诊断方法[J].振动、测试与诊断.2008,28(2)：104-107.
[81]Lu Jiyun,Liang Dakai, Zhang Xiaoli.Research on structural health monitoring for wing box based on support vector regression machine[J]. Journal of Scientific Instrument,2009, 30(3):486-491.
[82]Guo Gao, Zhang Jiang-She.Reducing examples to accelerate support vector regression [J]. Pattern Recognintion Letters 2007,7:2173-2183.
[83]Lau K W, Wu Q.H.Local prediction of non-line time series using support vector regression [J].Pattern Recongnition,2007, (41):1539-1547.
[84]Zhang Jian, Sato Tadanobu, Iai Susumu. Support vector regression for on-line health monitoring of large-scale structures [J].Structural Safety,2007, (28):392-406.
[85]Zhang Jian, Sato Tadanobu, Iai Susumu, etc.A pattern recognition technique for structural identification using observed vibration signals:Linear case studies [J]. Engineering Structures.2007,08,006.
[86]Zhang Jian, Sato Tadanobu, Iai Susumu, etc.A pattern recognition technique for structural identification using observed vibration signals:Nonlinear case studies [J]. Engineering Structures.2007,08,007.
[87]Miao Q, Wang S F.Nonlinear model predictive control based on support vectorregression. Proceedings of the First International Conference on Machine Learningand Cybernetics, Beijing,2002:1657-1661.
[88]Zhong, WM.SVM with polynomial kernel function based nonlinearmodel one-step-ahead predictive control [J]. Chinese Journal of Chemical Engineering.2005, 13(3):373-379.
[89]Bao Zhejing.SVM based one-step-ahead NMPC for MIMO systems. TheInternational Conference on Sensing, Computing, and Automation,2006.
[90]刘龙,孟光.基于支持向量机和模态频率的结构损伤诊断[J].机械强度,2006,28(3)：349-352.
[91]张浩然,韩正之,李昌刚.基于支持向量机的非线性预测控制[J].系统工程与电子技术,2003,25(03)：330-334.
[92]刘斌,苏宏业,褚健.一种基于最小二乘支持向量机的预测控制算法[J].控制与决策,2004,19(12)：1399-1402.
[93]Guo H Y.Structural damage detection using information fusion technique [J].Mechanical Systems and Signal Processing,2006,20:1173-1188.
[94]Gros X E.NDT data fusion at pixel level [J]. NDT& E International,1999(32):283-292.
[95]Verma A., Lbragimov A., Ramachandran S. Wavelet-based neural net application for feature detection and classification [J]. Neural, Parallel & Scientific Computations.2003, 11(2):19-40.
[96]Zhao Xiukuan, Limin, Gangbing, et al. Hierarchical ensemble-based data fusion for structural health monitoring [J]. Smart Materials and Structures,2010,19(4):1361-1365.
[97]Xiao Chun, Qu Weilian, Tan Dongmei.An application of data fusion technology in structural health monitoring and damage identification [J]. Smart Structures and Materials, 2005,5758:451-461.
[98]刘义艳,段晨东,巨永锋,等.基于神经网络与特征融合的损伤诊断方法[J].长安大学学报,2008,28(6)：106-110.
[99]刘涛,李爱群,缪长青,等.基于数据融合的结构损伤识别方法研究[J].工程力学,2008,25(1)：16-21.
[100]姜绍飞,张帅.基于概率神经网络分类器的数据融合损伤检测方法[J].计算力学学报,2008,25(5)：700-705.
[101]沈志熙,黄席樾,马笑潇.基于EMD和支持向量机的柴油机故障诊断[J].振动、测试与诊断,2010,30(1)：19-22.
[102]王宏禹.信号处理相关理论综合与统一法[M].北京：国防工业出版社,2005,208-244.
[103]Shekel J. Instantaneous frequency. Proc.IRE,1953,41:548.
[104]Wen-Xian Yang. Interpretation of mechanical signals using an improved Hilbert-Huang transform [J]. Mechanical Systems and Signal Processing,2008, 28:1061-1071.
[105]钟佑明,秦树人.HHT的理论依据探讨-Hilbert变换的局部乘积定理[J].振动与 冲击,2006,26(2)：12-15.
[106]Wu Z, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Process of the Royal Society of London,2003, A 460: 1597-1611.
[107]Alexander Stroeer, John K. Cannizzo, and Jordan B. Camp. Methods for detection and characterization of signals in noisy data with the Hilbert-Huang transform[J]. Mechanical Systems and Signal Processing,2009,79(12):124022-1-124022-10.
[108]Wickerhauser M. V. Lecture on wavelet packet algorithms [R]. Math. Depart. Washington Univ., St. Lowis Missouri, U. S.,1991.
[109]王大凯,彭进业.小波分析及其在信号处理中的应用[M].北京：电子工业出版社,2006.
[110]刘明才.小波分析及其应用[M].北京：清华大学出版社,2005.
[111]Johnson E A, Lam H F, Katafygiotis L S. et al. Phase ⅠIASC-ASCE structural health monitoring benchmark problem using simulated data [J]. J. Eng. Mech.,2004,130(1): 3-15.
[112]Wu J R, Li Q S. Structural parameter identification and damage detection for a steel structure using a two-stage finite element model updating method [J]. Journal of constructional steel research,2006,62:231-239.
[113]Vladimir N Vapnik.张学工译.统计学习理论的本质[M].北京：清华大学出版社,2000.
[114]Theodoros Evgeniou. Learning with kernel machine architectures [D]. JMassachusetts :Massachusetts Institute Technology,2000
[115]Rich C, Steve L, Giles C L. Overfitting in Neural Networks: Backpropagation, Conjugate Gradient, and Early Stopping [A]. Advances in Neural Information Processing Systems 13 [C]. Colorado:MIT Press,2001,402-408.
[116]Steve L, Giles C L,Tsoi A C. Lessons in Neural Network Training:Overfitting May be Harder than Expected[A]. Proceedings of the Fourteenth National Conference on Artificial Intelligence[C]. California: AAAI Press,1997,545-550.
[117]边肇祺,张学工.模式识别(第二版)[M].北京：清华大学出版社,2000
[118]Hsu C W, Lin C J.A Comparion of Methods for Multiclass Support Vector Machines[J]. IEEE Transactions on Neural Network,2002,13(2):415-425.
[119]Sasiadek S. J.Z. SENSOR FUSION [J]. Annual Reviews in Control,2002, 26:203-228.
[120]Sun S.L. Multi-sensor optimal information fusion Kalman filters with applications [J]. Aerospace Science and Technology,2004,8:57-62.
[121]Telmoudi A., Chakhar S., Telmoudi A., et al. Data fusion application from evidential databases as a support for decision making [J]. Information and Software Technology, 2004,46:547-555.
[122]吕大刚,王光远.结构智能健康诊断的信息融合原理[J].哈尔滨建筑大学学报,2002,35(4)：1-5.
[123]Guo H.Y. Structural damage detection using information fusion technique [J].Mechanical Systems and Signal Processing,2006,20:1173-1188.
[124]杨露傔,耗威.多传感器目标识别的神经网络与证据理论结合方法[J].探测与控制学报,2006,28(1)：40-43.
[125]刘敏华,萧得云.基于信息熵的多传感器数据分类方法[J].控制与决策,2006,21(4):410-416.
[126]http://cive.seas.wustl.edu/wusceel/ASCE.shm/experim_phase_2.htm
[127]Men-Tzung Lo, Vera Novak, C.-K. Peng,.etal.Nonlinear phase interaction between nonstationary signals:A comparison study of methods based on Hilbert-Huang and Fourier transforms [J]. PHYSICAL REVIEW E,2009,79(6):061924-1:061924-11.
[128]Kijewski C. T., Kareem A. Efficacy of Hilbert and Wavelet Transforms for Time-Frequency Analysis [J]. JOURNAL OF ENGINEERING MECHANICS,2006, 10:1038-1049.
[129]薛定宇.控制系统计算机辅助设计[M].北京：清华大学出版社,2006:52-63.
[130]王济,胡晓编著.MATLAB在振动信号处理中的应用[M].北京：中国水利水电出版社,2006,1.
[131]周云,陈存恩,黄镇梁等.土木工程减灾防灾学[M].广州：华南理工大学出版社,2002,8.
[132]Itani Y, Tsutsumoto K, Kagawa N. Time series prediction of acoustic signals using neural network model and wavelet shrinkage [A]. Proceedings of the Tenth International Congress on Sound and Vibration[C]. Stockholm, Sweden: IIAV,2003,4189-4196.
[133]Smola A J.Learning with Kernes [D].Berlin: Technical University Berlin,1998.
[134]Chen-Chia Chuang, Shun-Feng Su, Hsiao Chih-Ching.Robust Support Vector Regression Networks for Function Approximation WithOutliers [J]. IEEE Transation on neural netwoks,2002,13(6):1322-1330.
[135]Lu Weizhen, Wan Wenjian g,Andrew Y T Leung,et al.Air Pollution Paremeter Forecasting Using Support Vector machines [A].Neural Networks,2002.Proceedings of the 2002 International Joint Conference IJCNN'02.[C],Vol:1,2002:630-635.
[136]Chen Bo-Juen, Chang Ming-Wei, Lin Chih-Jen.Load Forecasting Using Support Vector Machines:A Study on EUNITE Competition 2001 [R].Reprt for EUNITE 2001 Competition, Available at http://neuron.tuke.sk/competition/.