空间网格结构损伤识别及系统研究
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摘要
现代工程结构的大型化和复杂化,导致人们无法及时准确的了解结构的工作状况。自然灾害、结构的疲劳变形和自然环境的侵蚀都会导致工程结构的损伤。及时发现结构损伤,减少或避免人员伤亡和财产损失,显得尤为重要,这就需要一套结构损伤监测分析系统,来及时准确地反映结构的使用状况。
本文以空间网格结构为研究对象,将整个结构损伤监测分析系统分为三个模块来研究,包括结构振动数据采集系统、结构模态参数识别系统和基于BP神经网络的结构损伤识别系统。首先,基于虚拟仪器技术LabVIEW软件平台开发了结构振动数据采集系统,实现了16个通道的实时采集、海量硬盘采集和触发采集等功能,并且设置了数字滤波功能,可以对采集数据进行滤波以减少噪声干扰,同时能较好地保证数据的完整性。然后,基于MATLAB的GUI语言开发了结构模态参数识别系统,实现了最小二乘复指数法(LSCE),特征系统实现法(ERA),有理多项式拟合法(RFPM)等参数识别功能,可以通过绘制稳态图剔除识别过程中出现的虚假模态,可以对振型结果进行三维动画显示,具有界面友好,操作简单等优点,交互式操作菜单可以引导使用者逐步完成模态参数识别的整个过程。以一桁架结构模型对其进行了仿真数据的验证,结果表明,本文所开发的模态参数识别系统识别结果可靠,快捷。最后,研究了基于BP神经网络的损伤识别算法,用共轭梯度法作为BP神经网络的训练函数,选择固有频率变化率和每个自由度在前10阶的模态分量作为BP神经网络的输入参数,并使用节点数作为输出神经元数量。采用一个桁架结构模型对其进行了仿真数据的验证,通过调整BP网络的输入和输出,实现对桁架模型的损伤位置和程度的识别仿真,结果表明本文所提损伤识别算法可以对空间网格结构进行损伤识别。
本文开发的空间网格结构损伤监测分析系统,结合了LabVIEW和MATLAB各自的长处,具有采集振动信号完整,模态参数识别可靠,BP神经网络损伤识别到位的特点。
People could not know the structure work condition accuracy and timely, because the Modern engineering structures are large and complicated. The structure maybe presents damage because such reason as natural calamity, tired accumulating and erosion. It’s import to find the structure damage timely for avoiding personnel's injured and reducing property loss. And we need a structure damage monitoring and analysis system to reflect the structure work condition in time.
The study object is spatial lattice structure in Paper. The whole spatial lattice structure health monitors and analysis system be consist of studying three parts. There is digital signal acquisition, modal identify, and damage identify by BP neural network. First, the structure vibration digital signal acquisition is developed with the virtual instrument technology of LabVIEW. It can acquire 16 channels digital signal at one time as real-time, saving signal data to hard disk and trigger. It have number filter set to reduce the noise influence, and keep the digital signal complete. Second, the modal parameter identify function be developed by GUI of MATLAB. It is consisted of LSCE, RFPM and ERA algorithms. Discard the false modal parameter by drawing steady-state figure. And there is a 3D drawing function to display the result of modal identifies. The modal identify function with friendly graphical user interface and simply operation; show the completion of operating step by step. The modal identify function is validated by a truss model simulation data. Come to the conclusion, the result data from the modal identify is reliable and quick. In the finally, do some study work of structure damage identify by BP neural network. Choose conjugate gradient algorithms as training function, choose the rate of change of natural frequency and modal vector of front 10 orders at a freedom degree as the input of BP neural network, and use the number of nodes of truss as the number of output. It’s validated by a truss model simulation also, and identify the damage position and extent by modulate the value of input and output. As a result, use BP neural network to identify damage is feasible.
The spatial lattice structure damage monitoring and analysis system in the paper take the advantage of LabVIEW and MATLAB. The structure digital signal is complete, the modal identify result is reliable, and structure damage identify by BP neural network is feasible.
本文以空间网格结构为研究对象,将整个结构损伤监测分析系统分为三个模块来研究,包括结构振动数据采集系统、结构模态参数识别系统和基于BP神经网络的结构损伤识别系统。首先,基于虚拟仪器技术LabVIEW软件平台开发了结构振动数据采集系统,实现了16个通道的实时采集、海量硬盘采集和触发采集等功能,并且设置了数字滤波功能,可以对采集数据进行滤波以减少噪声干扰,同时能较好地保证数据的完整性。然后,基于MATLAB的GUI语言开发了结构模态参数识别系统,实现了最小二乘复指数法(LSCE),特征系统实现法(ERA),有理多项式拟合法(RFPM)等参数识别功能,可以通过绘制稳态图剔除识别过程中出现的虚假模态,可以对振型结果进行三维动画显示,具有界面友好,操作简单等优点,交互式操作菜单可以引导使用者逐步完成模态参数识别的整个过程。以一桁架结构模型对其进行了仿真数据的验证,结果表明,本文所开发的模态参数识别系统识别结果可靠,快捷。最后,研究了基于BP神经网络的损伤识别算法,用共轭梯度法作为BP神经网络的训练函数,选择固有频率变化率和每个自由度在前10阶的模态分量作为BP神经网络的输入参数,并使用节点数作为输出神经元数量。采用一个桁架结构模型对其进行了仿真数据的验证,通过调整BP网络的输入和输出,实现对桁架模型的损伤位置和程度的识别仿真,结果表明本文所提损伤识别算法可以对空间网格结构进行损伤识别。
本文开发的空间网格结构损伤监测分析系统,结合了LabVIEW和MATLAB各自的长处,具有采集振动信号完整,模态参数识别可靠,BP神经网络损伤识别到位的特点。
People could not know the structure work condition accuracy and timely, because the Modern engineering structures are large and complicated. The structure maybe presents damage because such reason as natural calamity, tired accumulating and erosion. It’s import to find the structure damage timely for avoiding personnel's injured and reducing property loss. And we need a structure damage monitoring and analysis system to reflect the structure work condition in time.
The study object is spatial lattice structure in Paper. The whole spatial lattice structure health monitors and analysis system be consist of studying three parts. There is digital signal acquisition, modal identify, and damage identify by BP neural network. First, the structure vibration digital signal acquisition is developed with the virtual instrument technology of LabVIEW. It can acquire 16 channels digital signal at one time as real-time, saving signal data to hard disk and trigger. It have number filter set to reduce the noise influence, and keep the digital signal complete. Second, the modal parameter identify function be developed by GUI of MATLAB. It is consisted of LSCE, RFPM and ERA algorithms. Discard the false modal parameter by drawing steady-state figure. And there is a 3D drawing function to display the result of modal identifies. The modal identify function with friendly graphical user interface and simply operation; show the completion of operating step by step. The modal identify function is validated by a truss model simulation data. Come to the conclusion, the result data from the modal identify is reliable and quick. In the finally, do some study work of structure damage identify by BP neural network. Choose conjugate gradient algorithms as training function, choose the rate of change of natural frequency and modal vector of front 10 orders at a freedom degree as the input of BP neural network, and use the number of nodes of truss as the number of output. It’s validated by a truss model simulation also, and identify the damage position and extent by modulate the value of input and output. As a result, use BP neural network to identify damage is feasible.
The spatial lattice structure damage monitoring and analysis system in the paper take the advantage of LabVIEW and MATLAB. The structure digital signal is complete, the modal identify result is reliable, and structure damage identify by BP neural network is feasible.
引文
1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.振动与冲击. 2004,23(4):1-7
2刘伟,高维成,.网壳结构损伤识别理论及仿真研究.哈尔滨工业大学学报, 2006,38(2):1-7
3高维成,刘伟.网壳结构损伤识别的数值模拟及试验研究.工程力学. 2006,23(10):111-117
4刘伟.空间网格结构损伤探测的理论与试验研究.哈尔滨工业大学硕士论文. 2004:9-22
5高维成,史纪鑫,邹经湘,沈世钊.高层建筑上的穹顶结构及其自振特性的理论和试验研究.地震工程和工程振动. 2003,23(5):1-7
6万支远,鲁植雄.基于LabVIEW同步控制技术的数据采集平台设计.工业控制计算机. 2007,20(4):48-49
7高育芳.基于虚拟仪器的振动测试系统.苏州大学学报(工科版). 2006.,26(6):52-54
8周占怀,茹秋生.振动测试方法及测试系统的研究.制造业自动化. 2006,28(12):89-91
9杨大柱.基于LabVIEW的数字滤波器设计.计算机应用. 2006,第6期19-20
10陆宁.基于LabVEIW的网络虚拟实验教学平台的构建.中国水运(理论版). 2007,5(3):116-117
11孙圣和,刘明亮,施正豪.现代时域测量.哈尔滨工业大学. 1989
12 N.S.Nahman, M.E.Guillume. Deconvolution of Time Domain Waveform in the Presence of Noise. NBS.Tech.Note. 1981:1047-1054
13 M.Raid,R.B.Stanford. Impulse Response Evaluation Using Frequency Domain Optimal Compensation Deconvolution. proc.23rd Midwest sgmp on Circuits and Sysyem .1987:521～525
14 A.L.Klosterman, J.R.Lemon. Building Block Approach to Structural Dynamic. ASME1969,69:35-43
15陆鑫森,J.K.范迪瓦.结构响应谱中参数估计的最小P乘优化.振动与冲击.1982,3:(2-8)
16 Nagamatsu et al. Analysis of Forced Vibration Bu Reduced Impedance Method. Bulletin of JSME. 1983,26:214-230
17张宇光,吴家驹.适用于微机的一种多参考点参数识别技术.振动工程学报.1990,3(3):76-80
18丘杰、陈福根、张永光.一种辩识多输入多输出振动系统参数的方法.应用力学学报. 1992,9(1):60-69
19黄文虎、邵成勋.振动系统参数识别的时域方法.振动与冲击1982,1(1):43-52
20 Rune Brincker, Lingmi Zhang, Palle Anderson. Modal Identification from Ambient Responses Using Frequency Domain Decomposition. Proc. Of the 18th IMAC,2001,625-629
21 Ibrahim S R. Double least Square Approach for Use in Structural Modal Identification. AIAA. Journal, 1986,24(3): 178～198
22 J.N Juang,R.S.pappa. An Eigensystem Realization Algorithm(ERA) for Modal Parameter Identification & Modal Reduction. .NASA/JPL workshop on Identification & control of Flexible Space Structures. 1984:99～108
23 K.J.A strom,T.Bonlin. Numerical Identification of Linear Dynamic Systems Form Normal Operating Records IFAC Symposium on self-Adaptive, Teddington.England;1965:20-27
24 G.E.P.Box, G.M.Jenkins. TimeSeries nalysis,Forecasting and Control. 2nd Editor 1976,25-31
25傅志方,施勤忠.多维时序模型分析及其在模态分析中的应用.全国第三届振动理论及应用会议论文集.1987, 133～138
26王彤张令弥运行模态分析的频域空间域分解法及其应用航空学报. 2006,1:63-66
27 Brincker R , Zhang L M , Anderson P , et al. Modal identification from ambient response using f requency domain decomposition. Proceedings of t he 18th IMAC. USA :Society for Experimental Mechanics , 2000:200-222
28常军,张启伟,孙利民,结构模态参数识别的随机子空间法.苏州科技学院学报(工程技术版)2006,9:10-12
29 Peeter B, De R G. Reference-based stochastic subspace identification for output-only modal analysis. Mechanical and Signal Processing 1999,13:855-878
30果树卿,梁建文,张郁山.用HHT方法识别强迫振动下线性双自由度体系的模态参数.自然科学进展. 2006,16(3):375-379
31黄天立,楼梦麟.基于HHT的非线性结构系统识别研究.地震工程与工程振动. 2006,26(3):80-83
32 Kirkegaard, P, A. Rytter . Use of Neural Networks for Damage Assessment in a Steel Mast in Proc. of the 12th International Modal Analysis Conference. 1994,1128–1134
33 Chung-Bang Yun, Eun Young Bahng. Substructural identification using neural networks Computers and Structures. 2000,77:41-52
34 C. Zang, M. Imregun. Combined neural network and reduced FRF techniques for slight damage detection using measured. Archive of Applied Mechanics. 2001,71:525-536
35 C. Zang, M. Imregun. Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. Journal of Sound and Vibration. 2001,242(5):813-827
36 A. Zubaydi, M.R. Haddara, A.S.J. Swamidas. Damage identification in a ship’s structure using neural networks. Ocean Engineering. 2002,29:1187–1200
37 Rajasekaran S.,and Kamasamy J.V. Discussion of Modeling initial design process using artificial neural network. Journal of Computing in Civil Engineering 1997,11(1):15-18
38丁泉顺陈艾荣项海帆.桥梁断面气动导数识别的修正最小二乘法.同济大学学报.2001,29(1):26-29
39 Fassois, S.D., Parametric ldenfificafion of Vibrating Systems, in the Encyclopedia of Vibration. Academic Press, 2001:24-29
40 Rune Brincker, Palle Anderson, Nis Moller. Output Only Modal Testing of a Car Body Subject to Engine Excitation. Proc. Of the 18th IMAC,2000::763-769
41 Hyoung M. Kim, Mohamed Kaouk. Modal Analysis and Model Correlation of the Mir Space Station. Proc. Of the 18th IMAC,2000:1724-1730
42吕志民,徐金梧大型构件动态固有频率和阻尼系数辨识方法.机械工程学报. 2001,37(6):72-75
43李中付,华宏星,宋汉文,郑栋梁,陈之炎基于环境激励的工作模态参数识别.上海交通大学学报.2001,35(8):1167-1171
44时国勤诸得超王俊奎.线性振动亏损系统广义模态参数识别方法.固体力学学报.1991,12(3):235-243
45 Y.M.Cho,G.Xu and T.Kailath. Fast Recursive Identification of State Space Models via Exploitation of Displacement Structure. Automatica. 1994,30(1):45-59
46 M.Verhaegen. Identification of the Deterministic Part of MIMO State Space Models Given in Innovations Form from Input-output Data. Automatica.1994,30(1):61-74
47 K.Liu. Identification of Linear Time-varying Systems. Journal of Sound and Vibration.1997,206(4):487-505
48 F.Tasker,A.Bosse and S.Fisher. Real-Time Modal Parameter Estimation Using Subspace Methods: Theory. Mechanical Systems and Signal Processing. 1998,12(6):797-808
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50 F.Tasker,A.Bosse and S.Fisher. Real-time Modal Parameter Estimation Using Subspace Methods: Applications. Mechanical Systems and Signal Processing. 1998,12(6):809-823
51郭嗣琮,陈刚.信息科学中的软计算方法中的遗传算法.东北大学出版社,2001:143~240
52王凌.智能优化算法及其应用.清华大学出版社. 2001:83-103
53张智星,孙春在,水谷英二(日),神经—模糊和软计算张春华,高平安(译).西安交通大学出版社2000: 139-198
54 Lippmann R P. An introduction to computing with neural nets. IEEE ASSP Magazine, 1987,3(4):4~22
2刘伟,高维成,.网壳结构损伤识别理论及仿真研究.哈尔滨工业大学学报, 2006,38(2):1-7
3高维成,刘伟.网壳结构损伤识别的数值模拟及试验研究.工程力学. 2006,23(10):111-117
4刘伟.空间网格结构损伤探测的理论与试验研究.哈尔滨工业大学硕士论文. 2004:9-22
5高维成,史纪鑫,邹经湘,沈世钊.高层建筑上的穹顶结构及其自振特性的理论和试验研究.地震工程和工程振动. 2003,23(5):1-7
6万支远,鲁植雄.基于LabVIEW同步控制技术的数据采集平台设计.工业控制计算机. 2007,20(4):48-49
7高育芳.基于虚拟仪器的振动测试系统.苏州大学学报(工科版). 2006.,26(6):52-54
8周占怀,茹秋生.振动测试方法及测试系统的研究.制造业自动化. 2006,28(12):89-91
9杨大柱.基于LabVIEW的数字滤波器设计.计算机应用. 2006,第6期19-20
10陆宁.基于LabVEIW的网络虚拟实验教学平台的构建.中国水运(理论版). 2007,5(3):116-117
11孙圣和,刘明亮,施正豪.现代时域测量.哈尔滨工业大学. 1989
12 N.S.Nahman, M.E.Guillume. Deconvolution of Time Domain Waveform in the Presence of Noise. NBS.Tech.Note. 1981:1047-1054
13 M.Raid,R.B.Stanford. Impulse Response Evaluation Using Frequency Domain Optimal Compensation Deconvolution. proc.23rd Midwest sgmp on Circuits and Sysyem .1987:521～525
14 A.L.Klosterman, J.R.Lemon. Building Block Approach to Structural Dynamic. ASME1969,69:35-43
15陆鑫森,J.K.范迪瓦.结构响应谱中参数估计的最小P乘优化.振动与冲击.1982,3:(2-8)
16 Nagamatsu et al. Analysis of Forced Vibration Bu Reduced Impedance Method. Bulletin of JSME. 1983,26:214-230
17张宇光,吴家驹.适用于微机的一种多参考点参数识别技术.振动工程学报.1990,3(3):76-80
18丘杰、陈福根、张永光.一种辩识多输入多输出振动系统参数的方法.应用力学学报. 1992,9(1):60-69
19黄文虎、邵成勋.振动系统参数识别的时域方法.振动与冲击1982,1(1):43-52
20 Rune Brincker, Lingmi Zhang, Palle Anderson. Modal Identification from Ambient Responses Using Frequency Domain Decomposition. Proc. Of the 18th IMAC,2001,625-629
21 Ibrahim S R. Double least Square Approach for Use in Structural Modal Identification. AIAA. Journal, 1986,24(3): 178～198
22 J.N Juang,R.S.pappa. An Eigensystem Realization Algorithm(ERA) for Modal Parameter Identification & Modal Reduction. .NASA/JPL workshop on Identification & control of Flexible Space Structures. 1984:99～108
23 K.J.A strom,T.Bonlin. Numerical Identification of Linear Dynamic Systems Form Normal Operating Records IFAC Symposium on self-Adaptive, Teddington.England;1965:20-27
24 G.E.P.Box, G.M.Jenkins. TimeSeries nalysis,Forecasting and Control. 2nd Editor 1976,25-31
25傅志方,施勤忠.多维时序模型分析及其在模态分析中的应用.全国第三届振动理论及应用会议论文集.1987, 133～138
26王彤张令弥运行模态分析的频域空间域分解法及其应用航空学报. 2006,1:63-66
27 Brincker R , Zhang L M , Anderson P , et al. Modal identification from ambient response using f requency domain decomposition. Proceedings of t he 18th IMAC. USA :Society for Experimental Mechanics , 2000:200-222
28常军,张启伟,孙利民,结构模态参数识别的随机子空间法.苏州科技学院学报(工程技术版)2006,9:10-12
29 Peeter B, De R G. Reference-based stochastic subspace identification for output-only modal analysis. Mechanical and Signal Processing 1999,13:855-878
30果树卿,梁建文,张郁山.用HHT方法识别强迫振动下线性双自由度体系的模态参数.自然科学进展. 2006,16(3):375-379
31黄天立,楼梦麟.基于HHT的非线性结构系统识别研究.地震工程与工程振动. 2006,26(3):80-83
32 Kirkegaard, P, A. Rytter . Use of Neural Networks for Damage Assessment in a Steel Mast in Proc. of the 12th International Modal Analysis Conference. 1994,1128–1134
33 Chung-Bang Yun, Eun Young Bahng. Substructural identification using neural networks Computers and Structures. 2000,77:41-52
34 C. Zang, M. Imregun. Combined neural network and reduced FRF techniques for slight damage detection using measured. Archive of Applied Mechanics. 2001,71:525-536
35 C. Zang, M. Imregun. Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. Journal of Sound and Vibration. 2001,242(5):813-827
36 A. Zubaydi, M.R. Haddara, A.S.J. Swamidas. Damage identification in a ship’s structure using neural networks. Ocean Engineering. 2002,29:1187–1200
37 Rajasekaran S.,and Kamasamy J.V. Discussion of Modeling initial design process using artificial neural network. Journal of Computing in Civil Engineering 1997,11(1):15-18
38丁泉顺陈艾荣项海帆.桥梁断面气动导数识别的修正最小二乘法.同济大学学报.2001,29(1):26-29
39 Fassois, S.D., Parametric ldenfificafion of Vibrating Systems, in the Encyclopedia of Vibration. Academic Press, 2001:24-29
40 Rune Brincker, Palle Anderson, Nis Moller. Output Only Modal Testing of a Car Body Subject to Engine Excitation. Proc. Of the 18th IMAC,2000::763-769
41 Hyoung M. Kim, Mohamed Kaouk. Modal Analysis and Model Correlation of the Mir Space Station. Proc. Of the 18th IMAC,2000:1724-1730
42吕志民,徐金梧大型构件动态固有频率和阻尼系数辨识方法.机械工程学报. 2001,37(6):72-75
43李中付,华宏星,宋汉文,郑栋梁,陈之炎基于环境激励的工作模态参数识别.上海交通大学学报.2001,35(8):1167-1171
44时国勤诸得超王俊奎.线性振动亏损系统广义模态参数识别方法.固体力学学报.1991,12(3):235-243
45 Y.M.Cho,G.Xu and T.Kailath. Fast Recursive Identification of State Space Models via Exploitation of Displacement Structure. Automatica. 1994,30(1):45-59
46 M.Verhaegen. Identification of the Deterministic Part of MIMO State Space Models Given in Innovations Form from Input-output Data. Automatica.1994,30(1):61-74
47 K.Liu. Identification of Linear Time-varying Systems. Journal of Sound and Vibration.1997,206(4):487-505
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