一种基于数据融合的结构损伤特征提取方法
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摘要
目的针对单一传感器测量结构状态信息的诸多局限性,研究一致性数据融合算法,提出一种基于数据融合的结构损伤状态特征提取方法来描述结构的工作状态.方法采用小波分析方法对融合后的多传感器测量数据进行处理,以五层框架结构数值算例为例,对比研究结构各层加速度信号与融合后加速度信号构造的频带能量特征值.结果利用融合后的加速度构造的频带能量特征值的平均方差最大,表明其频带能量特征值的分布离散性最大.融合后的加速度信号包含了结构不同位置的不同状态信息,因此利用融合后加速度信号进行损伤识别效果最好.结论采用改进一致性算法和"能量-损伤"方法提取的结构损伤特征值包含了结构不同位置不同状态的信息,能够更好地描述结构状态.
For overcoming limitations of structure status measurement by using a single sensor,a data fusion based method for feature extraction from structure status is presented in this paper.In this method,the measured data from multi-sensor are fused by an improved consensus algorithm.Comparing to traditional consensus algorithm,it overcomes the shortcoming of the traditional consensus algorithm with two sensors,which has different confidence distance for different measuring precision.And the supporting matrix is fuzzified,which can avoid the subjective error in determining the threshold value.Structural frequency band energy eigenvalue extracted from the acceleration signals by data fusion and by no data fusion are compared through a numerical example.The result shows that the structural frequency band energy eigenvalue extracted by data fusion distributes most discreteness and includes more structural status information in different site,which can describe structural status better.
For overcoming limitations of structure status measurement by using a single sensor,a data fusion based method for feature extraction from structure status is presented in this paper.In this method,the measured data from multi-sensor are fused by an improved consensus algorithm.Comparing to traditional consensus algorithm,it overcomes the shortcoming of the traditional consensus algorithm with two sensors,which has different confidence distance for different measuring precision.And the supporting matrix is fuzzified,which can avoid the subjective error in determining the threshold value.Structural frequency band energy eigenvalue extracted from the acceleration signals by data fusion and by no data fusion are compared through a numerical example.The result shows that the structural frequency band energy eigenvalue extracted by data fusion distributes most discreteness and includes more structural status information in different site,which can describe structural status better.
引文
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[15]韩西,崔璟.小波分析在T梁结构损伤识别中的应用研究[J].地震工程与工程振动,2011,31(1):101-105.(Han Xi,Cui Jing.Application of wavelet analysis instructural damage identification of T-beam[J].Jour-nal of Earthquake Engineering and Engineering Vi-bration,2011,31(1):101-105.)
[2]陈福增.多传感器数据融合的数学方法[J].数学的实践与认识,1995,25(2):11-16.(Chen Fuzeng.Mathematical method to multi-sensordata fusion[J].Mathematics in Practice and Theory,1995,25(2):11-16.)
[3]刁联旺,王常武.多传感器一致性数据融合方法的改进与推广[J].系统工程与电子技术,2002,24(9):60-61.(Diao Lianwang,Wang Changwu.Improvement andextension of consensus multi-sensor data fusionmethod[J].Journal of Systems Engineering and E-lectronics,2002,24(9):60-61.)
[4]张尧庭,方开泰.多元统计分析[M].北京:科学出版社,1982.(Zhang Yaoting,Fang Kaitai.Multivariate statisticalanalysis[M].Beijing:Science Press,1982.)
[5]焦莉,李宏男.一种基于改进的一致性算法数据融合技术[J].防灾减灾工程学报,2006,26(2):170-174.(Jiao Li,Li Hongnan.A data fusion method basedon improved consensus algorithm[J].Journal of Dis-aster Prevention and Mitigation Engineering,2006,26(2):170-174.)
[6]焦莉,李宏男,孙威.基于数据融合和“能量-损伤”的结构状态特征提取[J].振动、测试与诊断,2010,30(1):83-86.(Jiao Li,Li Hongnan,Sun Wei.Feature extraction ofstructure status based on data fusion and wavelet a-nalysis[J].Journal of Vibration,Measurement andDiagnosis,2010,30(1):83-86.)
[7]焦莉,李宏男.基于数据融合和小波分析的结构损伤诊断[J].振动与冲击,2006,25(5):85-88.(Jiao Li,Li Hongnan.Diagnosis of structural damagebased on data fusion and wavelet analysis method[J].Journal of Vibration and Shock,2006,25(5):85-88.)
[8]焦莉,李宏男.基于数据融合和“能量-损伤”的结构多损伤模式识别[J].振动与冲击,2010,29(8):120-123.(Jiao Li,Li Hongnan.Structural multi-damages pat-tern recognition based on data fusion and energy-damage[J].Journal of Vibration and Shock,2010,29(8):120-123.)
[9]Sun Z,Chang C C.Structural damage assessmentbased on wavelet packet transform[J].Journal ofStructural Engineering,ASCE,2002,128(10):1354-1361.
[10]Mahmoud M,Reda T.A neural-wavelet techniquefor damage identification in the ASCE benchmark
[11]Ovanesova A V,Suarez L E.Applications of waveletstructure using phase II experimental data[J].Ad-vances in Civil Engineering,2010,5:1-13.transforms to damage detection in frame structures[J].Engineering Structures,2004,26(1):39-49.
[12]Melhem H,Kim H.Damage detection in concrete byfourier and wavelet analyses[J].Journal of Engi-neering Mechanics,2003,129(5):571-577.
[13]刘涛,李爱群,丁幼亮.小波分析在结构损伤识别中的应用[J].地震工程与工程振动,2008,28(2):29-35.(Liu Tao,Li Aiqun,Ding Youliang.Application ofwavelet analysis to structural damage identification[J].Journal of Earthquake Engineering and Engi-neering Vibration,2008,28(2):29-35.)
[14]Adriana Hera,Zhikun Hou.Application of waveletapproach for ASCE structural health monitoringbenchmark studies[J].Journal of Engineering Me-chanics,2004,130(1):96-104.
[15]韩西,崔璟.小波分析在T梁结构损伤识别中的应用研究[J].地震工程与工程振动,2011,31(1):101-105.(Han Xi,Cui Jing.Application of wavelet analysis instructural damage identification of T-beam[J].Jour-nal of Earthquake Engineering and Engineering Vi-bration,2011,31(1):101-105.)
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